MODELING ROAD AND RAIL FREIGHT ENERGY CONSUMPTION: A COMPARATIVE STUDY ASHIS PARAJULI

Transcription

1 MODELING ROAD AND RAIL FREIGHT ENERGY CONSUMPTION: A COMPARATIVE STUDY ASHIS PARAJULI 2005

2 MODELLING ROAD AND RAIL FREIGHT ENERGY CONSUMPTION: A COMPARATIVE STUDY ASHIS PARAJULI BEng (CIVIL) SCHOOL OF URBAN DEVELOPMENT QUEENSLAND UNIVERSITY OF TECHNOLOGY 2005 SUBMISSION FOR THE DEGREE OF MASTER OF ENGINEERING

3 Keywords Freight task, Road freight energy consumption, Rail freight energy consumption, Pick-up leg, Line-haul, Delivery leg, Payload.

4 ABSTRACT After reviewing land based freight growth trends nationally and internationally, this thesis discusses the main parameters governing fuel consumption, as well as past approaches in modelling road and rail energy consumption. Past work on comparing these two main modes is also reviewed here. The review included ways of estimating energy consumption of a complete freight task i.e., from origin to destination. Mathematical models estimating modal energy consumption are presented in this thesis. Modal energy consumption is a complex function to be approximated in practice due to numerous variables affecting their outcome. Energy demands are particularly sensitive to changes in vehicle characteristics such as mass and size; route parameters such as grade and curvature; traffic conditions such as level of congestion; and less sensitive to ambient conditions, such as temperature and altitude. There is a large set of energy estimation models available to transportation planners. Unfortunately, unless simple relationships are established for energy estimation and modal comparison, their application in freight movement planning and corridor development becomes computationally prohibitive. This thesis describes the development of a modal freight energy comparison tool to quantify the energy advantage from mode choice, corridor development and vehicle types and loading improvements. The thesis also describes the used modelling processes and the trade-offs between model complexity and data quality. The tool developed in this thesis is based on well established relationships between energy consumption and traffic flow, route and vehicle operating characteristics for road freight movement. The rail freight component was developed from equations of motion together with parameters obtained from past studies. The relationships have been enhanced to fit the purpose of corridor level comparative analysis. The comparison tool has been implemented using a spreadsheet based approach developed specifically to calculate the total door to door energy consumption for given task options. A series of linked sheets enable the user to: specify all necessary inputs; estimate road and rail

5 energy by trip segment. The outputs consist of trip segment energy demand and total energy efficiency of each option. A case study approach, for aiding in model development and testing, is presented. Toowoomba second range crossing in Southern Queensland, Australia (section between below Postman s Ridge and Gowrie Junction) was selected. Four options considered include existing and proposed road and rail corridors. The existing rail and road corridors could be taken as a typical poor case, with very high grades and sharp curvatures. The proposed new road section has a relaxed curvature and gradient. The section of proposed rail corridor, under consideration here, still contains a high grade section. However, the proposed track length is considerably shorter than the base-case. The new proposed train alignment was found as the most efficient mode and the existing trains as the least efficient mode when measured based on absolute expected fuel gain (litres/tonnage of freight moved). This could be attributed to the improvement in curvature and load carrying capacity. However, when the options are compared in terms of litres/1000 NTK, the new train option did not show a significant advantage. Furthermore, the developed model was applied on some simulated cases to test the functionality of other aspects of the model. The total door-to-door energy consumption and the efficiency were compared for all the simulated cases. It showed that the energy efficiency of scenarios varies exponentially with the variation in the ratio of road pickup and delivery legs to the rail line-haul length. In general, energy efficiency of the intermodal options was found to be better unless the best case of the road and the worst case of intermodal option was compared. The modelling approaches presented in the thesis and the comparison model developed in this study could be used for several purposes namely: to assess the energy (and hence greenhouse gas) implications of specific modal freight movements; to aid in the economic and environmental evaluation of transport options; and to assess the potential for energy efficiency gains from vehicle and infrastructure improvements. A number of suggested improvements to the model are also discussed.

6 Table of Contents Chapter One Introduction 1.1 Background Scope Structure of the thesis 2 Chapter Two Freight Trends: Task and Energy 2.1 Introduction Freight Growth International Domestic Rail and road freight movements Bulk freight movements Non-bulk freight movements Competitive neutrality between road and rail Main mode characteristics Road Freight vehicles and units Rail Freight locomotives and units Energy in freight: Trends Energy consumption trends Energy efficiency trends 15 Chapter Three Estimating Modal Energy Consumption 3.1 Introduction Factors affecting energy consumption Fuel and energy content Road transport Rail transport Energy consumption models Road transport Rail transport Energy consumption: Comparative studies Introduction to Intermodal transport Previous comparative methodologies Factors influencing comparative studies Limitations of comparative studies Conclusions and implications Conclusions from the literature review Implications for the thesis 43

7 Chapter Four Model Development 4.1 Introduction General Model Requirements Selecting the fuel efficiency measuring unit Classifying the commodities Route characteristics Determining vehicle characteristics Data collection Road transport sub-model Background Amendments to NIMPAC algorithm Adjustment factors Summary for road Vehicle simulator Rail transport sub-model Additional transport process sub-model Intermodal transfer energy Shunting energy Spreadsheet model platform Summary 70 Chapter Five Sensitivity Analysis 5.1 Introduction Model Errors Errors and uncertainty in road energy estimation Background Roughness sensitivity Speed coefficients and speed sensitivity Grade sensitivity Curvature sensitivity Congestion sensitivity Payload sensitivity Sensitivity summary of road sub-model Errors and uncertainty in rail energy estimation Background Train length Train Mass Train Speed Grade and curvature Numbers of Wagons and Locomotives Sensitivity summary of rail sub-model Model complexity and input data 89

8 Chapter Six Case Study 6.1 Introduction Site description Background Option One (Existing Road) Option Two (Existing Rail) Option Three (Proposed Road Alignment) Option Four (Proposed Rail) Freight description Energy estimation Option One (Existing Road) Option Two (Existing Rail) Option Three (Proposed Road Alignment) Option Four (Proposed Rail) Options comparison 114 Chapter Seven Model Application: Simulated Cases 7.1 Background Route specification and comparison scenarios Energy estimation Scenario One to Six Scenario Seven to Twelve Scenario Thirteen to Eighteen Scenario Nineteen to Twenty Four Scenario Twenty Five to Twenty Eight Overall results 129 Chapter Eight Conclusions and Future Research 8.1 Literature Review Model Development and sensitivity of model parameters Case Study Model application: On Simulated Cases Future Research 136 References Appendices

9 List of Figures 1.1 Structure of the thesis Structure of Chapter The interstate non-bulk freight task: Trends Types of combination vehicles Transport Energy Consumption in EU (15 countries) Freight Energy Consumption in Australia Australian domestic freight energy efficiency Structure of Chapter II Energy Train for a typical urban car Model of energy flow in vehicle ARFCOM approach to modeling fuel consumption Intermodal transfer of various carriage units Factors influencing a comparative study Comparison routes Overview of model development methodology Fuel consumption versus vehicle speed Relationship between load and fuel consumption correction factor Fuel consumption versus grade Fuel consumption versus congestion Fuel consumption versus road roughness Effect of Gross Vehicle Mass in Energy consumption Relationships between payload and energy consumption Gauge width dimension Flow diagram of the comparison spreadsheet tool Input rail sheet Output road sheet Summary sheet Error versus complexity Roughness sensitivity a Speed sensitivity (constant coefficient variation, A) b Speed sensitivity (reciprocal coefficient variation, B) c Speed sensitivity (square coefficient variation, C) a Grade sensitivity at 2% gradient b Grade sensitivity at 4% gradient c Grade sensitivity at 8% gradient a Curvature sensitivity for very curvy section b Curvature sensitivity for less curvy section a Congestion sensitivity at light traffic section b Congestion sensitivity at heavy traffic section Effect of variation in train length 83

10 5.8 Effect of variation in train mass Effect of variation in train speed Effect of variation in route gradient Effect of variation in curvature radius Effect of variation of Number of Axles Sensitivity Comparison of various parameters Road options Rail options Grade profile (Postman Ridge to entrance of Toowoomba city) Grade profile (Exit from Toowoomba city to Nass Road junction) Speed and grade profile of existing road route Grade profile of existing rail track Grade profile Postman Ridge to Charlton (new proposed road alignment) Grade profile near Lockyer to Gowrie (new proposed rail alignment) a B Double Performance Chart (A) b B Double Performance Chart (B) a Six Axles Articulated Truck Performance Chart (A) b Six Axles Articulated Truck Performance Chart (B) Simulation performance comparison Fuel performance on new proposed road route Rail performance: Old rail route versus new rail route Four options comparison Intermodal freight movement concept Road alone freight movement concept Performance of road vehicles on pick-up and delivery links Total fuel consumed for scenario one to six Aggregate fuel performance (Scenario one to scenario six) Road vehicle performance with 80% payload on 200km road Train performance in 600km rail link Efficiency of three train types on 600m rail line haul link Total fuel consumed in scenario 7 to Energy efficiency between scenario 7 to Total fuel consumed in scenario 13 to Energy efficiency between scenario 13 to Total fuel consumed in scenario 19 to scenario Energy efficiency between scenario 7 and scenario Fuel performance of road vehicle on road line-haul link Efficiency of road alone haulage Fuel efficiency for various combinations with Type A Train Fuel efficiency for various combinations with Type B Train Fuel efficiency for various combinations with Type C Train Performance of some simulated cases 135

11 List of Tables 2.1 Modal Performances by Indicator (out of a maximum 10 points) External Cost of Rail vs Truck Locomotive classification and numbers Types of service and corresponding locomotive class Energy efficiency of rail locomotives Road based transport energy efficiency (aggregated) Energy content of fuel Factors affecting fuel consumption of heavy commercial vehicles Factors influencing vehicle energy consumption rate Factors influencing rail fuel consumption Cars and light commercial vehicles fuel consumption models Heavy commercial vehicles fuel consumption models Rail fuel consumption models Horizontal curvature adjustment factor Traffic congestion adjustments to fuel consumption Classification of road section based on roughness Adjustment factors Coefficient contributors Intermodal transfer energy Shunting energy demand Constant values taken for sensitivity analysis of various paramters Sensitivity summary of various parameters Constant values taken for sensitivity analysis of various parameters Sensitivity Comparison Summary of Road characteristics to the east of Toowoomba Summary of Warrego Highway characteristics passing through the city Summary of Warrego Highway characteristics passing through the city Summary of rail track characteristics Summary of new proposed second range crossing Freight type Comparison table (existing road) Train consist information Fuel performance on the existing rail track Comparison table (proposed road) New track s train properties and performance Four options comparison 115

12 7.1 Freight routes general characteristics Alignment properties of hypothetical corridors Train properties List of scenarios Freight moving capacity of scenario one to six Importance of model parameters on road and rail fuel consumption Fuel performance on proposed and existing corridors 134 Appendices Appendix A Appendix B Appendix C Appendix D Appendix E Appendix F Appendix G Appendix H Appendix I Appendix J Appendix K Appendix L Commodity classification Representative vehicles and their characteristics Gradient adjustment factors Roughness adjustment factors Spreadsheet tool description and users guide Spreadsheet Tool A CD A sample result from Vehicle Simulator Run Toowoomba Case Study: Proposed Road Alignment Details Toowoomba Case Study: Existing Road Alignment Details Toowoomba Case Study: Existing Rail Alignment Details Toowoomba Case Study: Proposed Rail Alignment Details Route alignment details of simulated cases

13 Acronyms ABS EU GDP GTK GVM HCV HDM IRI LCV NAASRA NAFTA NIMPAC NRM NTK QR QT UniSA UoW VCR VOC Australian Bureau of Statistics European Union Gross Domestic Product Gross Tonnage Kilometers Gross Vehicle Mass Heavy Commercial Vehicle Highway Development and Management International Roughness Index Light Commercial Vehicle National Association of Australian State Road Authority North American Free Trade Agreement NAASRA Improved Model for Project Assessment and Costing NAASRA Roughness Meter Net Tonnage Kilometers Queensland Rail Queensland Transport University of South Australia University of Wollongong Volume to Capacity Ratio Vehicle Operating Cost

14 Acknowledgements During this research project, I have received assistance and guidance from many sources. I would like to express my gratitude to the following: My supervisors Professor Luis Ferreira and Dr. Jonathan Bunker for their guidance, support, patience and many interesting discussions. My uncles, Dr. Partha Mani Parajuli, Yogeshwor Parajuli and Sharad Koirala for all the technical and personal supports. School of Civil Engineering for the financial support. Dr. Peter Pudney (UniSA), Prof. Philip Laird (UoW) and Mr. Les Bruza (QT) for their help during model development phase. All my friends for their moral support and good discussions. I am deeply indebted to my aunt Mrs. Reena Parajuli for her support and encouragement during my study and living in Brisbane. Finally, I thank my mother (Indira Parajuli), father (Ananta Vijaya Parajuli) and aunt (Urmila Koirala) for all the happiness and pride they bestow into my life and for the sacrifices that they had to make and for the belief that they have shown in me.

15 The errors and inadequacies of the work are the responsibility of the author alone.

16 Statement of Original Authorship The work contained in this thesis has not been previously submitted for a degree or diploma at any other higher education institution. To the best of my knowledge and belief, the thesis contains no materials previously published or written by another person except where due reference is made. Ashis Parajuli 17 November 2005

17 CHAPTER I INTRODUCTION 1.1 Background The Australian annual freight task is forecast to reach up to 391 billion tonne-km by 2011, an expected rise of about 50 percent (reference year being 2001). The road freight task alone is projected to exceed 220 billion tonne-km by the year 2011 (BTRE, 2002). Corresponding to this increase in freight task, a rise in energy consumption can be expected, in spite of energy efficiency gains from vehicle and engine design improvements. Energy consumption is directly related to vehicular emissions. Hence, reducing energy consumption would also benefit the emission reduction program. BTRE (2002) projected, for business as usual condition, emissions from Australian transport in year 2020 to be around 68 percent higher than 1990 levels (Kyoto base level). Another motivation for energy reduction is to reduce total freight costs. Although road transport generally is not regarded as energy efficient mode, it has gained a very large market share of non-bulk freight movement due to the higher reliability and flexibility. Moreover, for relatively short hauling distances, road dominates the market (Bunker and Ferreira, 2002). Several reported energy efficiency studies of freight transport portrays road as one of the least efficient modes. However, the comparison is generally based only on linehaul movement does not reflect the overall efficiency of the task. A complete task may involve more than one mode such as a road legs for pickup and delivery, in the cases of rail line-hauling. The need to model energy consumption is closely linked to determining the energy efficiency of freight movements. Research into energy estimation has been extensive, 1

18 with some established relationships between fuel consumption of a vehicle and various parameters influencing energy consumption. The thesis presents the development and application of an analytical energy accounting framework to evaluate the performance of available options on land based freight movement, which is movement involving road and rail. 1.2 Scope Firstly, the aim of this research was to review the availability of models with the capability to compare given freight moving options on energy demand (MJ/tonnekm) basis. From this review, factors to be considered for the proper comparison were to be determined. Secondly, the research focused on the development of a methodology and a resulting framework that could be used to estimate the energy consumption for various freight movement sections differed by route, traffic and vehicle parameters. The analytical energy accounting framework developed would be useful to transport planners in quantifying the energy advantage from mode choice, corridor development and vehicle types and loading improvements. It is envisaged that the model can be used as a part of a planning tool to determine the total cost involved in the freight movement. 1.3 Structure of the thesis The thesis is structured into eight chapters and twelve Appendices as shown in Figure 1.1, with a view to providing logical and consistent sequence of information. 2

19 Abstract Table of contents Declaration Acknowledgement Chapter I Introduction Chapter II Freight trends: Task and Energy Chapter III Estimating modal energy Chapter IV Model development Chapter V Sensitivity Analysis Chapter VI Case Study Chapter VIII Model Application: Simulated Cases Chapter VIII Conclusions and Future Research Appendices Figure 1. 1 Structure of the thesis Literature review Literature review divided into two chapters (chapter II and III). Chapter II discusses the trends of freight task and energy consumption, both domestically and internationally. It also discusses the modes involved in freight movement and vehicle characteristics. Chapter III discusses the factors affecting energy consumption and presents the various energy estimation models used for rail and road. Model development The fourth chapter presents the model development process and discusses the spreadsheet tool developed as a part of this research. The main issues addressed in this chapter are the definition of model requirements and specification, and estimation procedures. 3

20 Sensitivity analysis The fifth chapter presents a parametric study of the model. It discusses the importance of each parameter and the coefficients attached to them. This chapter discusses the likely error ranges associated with the output of the model developed; when certain plausible assumptions are made about the measurement errors of the various independent variables. Case study and Model Application The sixth chapter presents a case study carried out as a part of the research. It includes the application and testing of the developed comparison model. This chapter presents the energy consumption estimation for four available options and discusses the advantages and limitations of various options. The seventh chapter includes model application on some simulated cases. This section illustrates the extended application of the model on door-to-door fuel consumption estimation and presents its use in determining the energy efficient option. Conclusions The eighth chapter summarises the work described in the thesis, drawing general conclusions about this specific project and also suggests areas where additional research is considered beneficial. The chapter also discusses the assumptions and related limitations. It also recommends the area where further research would be beneficial. Appendices The section contains auxiliary information relevant to the chapters mentioned above. A CD is included in the appendix which contains a spreadsheet tool developed as a part of this study to aid in comparing various land based freight moving options. The appendix also contains the user guide which helps to use the spreadsheet tool. 4

21 CHAPTER TWO FREIGHT TRENDS: TASK AND ENERGY 2.1 Introduction The literature review carried out for this thesis is divided into two main areas, namely: freight movement trends and modal energy consumption estimation. This chapter deals with the first main area which focuses on the freight modes and growth trends in both the freight task and the level of energy used in freight transport. This chapter deals with the following issues: The main modes involved in freight movements; The growth trends in freight movements; and The trends in freight energy consumption and vehicle energy efficiency. Figure 2.1 shows the structure of this chapter. 2.1 Introduction 2.2 Freight growth International Domestic Bulk freight movements 2.3 Rail and road freight movements Non bulk freight movements Competitive neutrality between road and rail 2.4 Main mode characteristics Road freight vehicles and units Rail freight vehicles and units Energy consumption trends 2.5 Energy in freight: Trends Energy efficiency trends Figure 2. 1 Structure of Chapter II 5

22 2.2 Freight growth International During the recent past, the world has experienced significant growth in freight movements. In Europe, an increase of 55 % in tonne-km between 1980 and 1998 has been recorded, with the largest annual growth in road transport (3.9 % on average) and short sea shipping (2.6 %), (Trafico and ETCAE, 2001). The growth in land freight task has been less in Austria and the Netherlands compared to other European countries. According to Van Arkel et al (2002) the freight task has grown by about 20% in the Netherlands from 1990 to European freight transport grew by 70% since 1970, (Communication from the Commission to the European Parliament and the Council, 1999). This significant growth of freight task in Europe started to aggravate the road congestion problem. The annual cost of congestion in the European Union reached 2% of GDP, with road users accounting for some 90 % of this amount, (EC, 1995). In Europe, about 2% annual growth is expected in freight transport for the next two decades (reference year being 1999). If freight transport is not given the proper consideration, then it might be very costly for Europe to resolve the problems arising from increased congestion and emissions. (Communication from the Commission to the European Parliament and the Council, 1999) Murtishaw and Schippen (2001) noted that the freight task in the US rose from just over 4000 billion tonne-km in 1988 to over 5000 billion tonne-km in This is an increase of about 23% in 10 years or around 2.3% per annum. North America overall has also experienced an increase in land freight movements. This has created problems with the movement of goods by truck between the North American Free Trade Agreement (NAFTA) partner countries. Traffic at land border crossings has experienced significant growth, particularly along the border between Texas and Mexico (Steven, 2002). 6

23 Congestion has a direct impact in the energy efficiency of freight vehicles. Hence, with a significant increase in congestion, there is an urgent need for mode shift or corridor infrastructure investment Domestic In Australia, in tonne-km terms, the total domestic freight task has increased over the past two decades by 58%. Road has increased its share during this time, from 17% in 1974 to 34% in 1993, (ABS, 1997). The total (rail and road) freight task in Australia amounted to 277 billion tonne-km for year In Queensland, the freight movement for the year 2001 (year ending on 31 March, 2001) was reported to be 93,416 million tonne-km, (ABS, 2002). Hence Queensland s freight task comprised of more than 33% of the whole Australian task. However, Queensland has only 18.7% percentage of total Australia s population and covers 22.5% of total land (2001 Census). Apelbaum (2003) projected the road freight sector to reached 46,072 million tonne-km by 2000/01, an increment of around 32% (reference year being 1998/99). BTRE (2002) projected Australian land freight task to reach up to 391 billion tonnekm by This is an expected rise of about 50% (reference year being 2001) in the coming 10 years. Road freight task alone is projected to exceed 220 billion tonne-km by the year For the projection of Australian freight task and energy consumption, BTRE (2002) used a bottom-up modelling approach across each of the main transport activities. In this approach, the estimates were made using a summation across major transport sub-sectors (typically calculated using vehicle fleet models or activity specific econometric equations). BTRE (2002) argued that bottom-up projection provide more close estimation because of its ability to cope for increased traffic congestion. Previously adopted top-down projection approach estimated a slightly higher value for fuel used. BTRE (2002) highlighted the reason for this slightly higher estimation by top down model as lack of any constraint parameters to allow for the trend towards saturation in future. 7

24 2.3 Rail and road freight movements Bulk freight movements In Australia, in the bulk freight movement, rail has a good market share due to its price competitiveness. Balls et al (2002) reported the dominance of long bulk freight market by rail. Bulk freight commodities include sugar, coal, steel, minerals, grains which are usually (but not exclusively) transported in large volume, (Mahoney, 1985) Non-bulk freight movements In Australia, due to the higher reliability and flexibility of road freight transport, this mode has gained a very large market share of non-bulk freight movement. Moreover, for relatively short hauling distances, road dominates the market (Bunker and Ferreira, 2002). Earlier Houghton and McRobert (1998) also mentioned an increasing dominance of road haulage in freight transport and emphasised the need for intermodal choice and appropriate logistics for better productivity, customer satisfaction and environmental protection. Interstate freight movements in Australia have been increasing steadily. Amongst the total interstate non-bulk freight task, the trend in the shares of all the modes is shown in Figure 2.2. Figure 2. 2 The Australia interstate non-bulk freight task: Trends Source BTE (1999) 8

25 Although the total freight task for both rail and road has been increasing over time, the relative share of road freight compared to rail has been increasing at a very fast rate. Road has taken some market share from coastal shipping and it has also suppressed the growth in rail freight. Until 1983 the non bulk freight shares of road and rail were almost equal, around 11 billion net tonne-km. However, road transport has increased its share by more than two times since then Competitive neutrality between road and rail Bunker and Giles (2001) using a survey of decision makers carried out on Brisbane- Cairns corridor, highlighted the perception of each mode relative to several performance indicators, such as fuel use, vehicle productivity and freight cost to operators. Table 2. 1 Modal Performances by Indicator (out of a maximum 10 points) Source: Bunker and Giles (2001) The results, which are summarized in Table 2.1, show that road is perceived as relatively inefficient with respect to energy use when compared with rail and sea. However the present trends show that road transport is being utilized excessively. One of the main reasons that the road freight task is growing at the expense of other modes could be the priority that the road transport is getting from policy makers. Gargett et al (1999) indicated that current pricing tends to favour road transport over rail by failing to internalize many costs, shown in Table 2.2. Jones and Rowat (2003) highlighted the main issues in road and rail pricing, namely; infrastructure subsidy, uneven tax treatment, and the lack of good data in the relative pavement damage by different categories of vehicles. 9

26 Table 2. 2 External Costs of Rail vs Truck (Amounts are in Australian cent per net tonne km) Source: Gargett et al (1999) In Australia, poor rail track condition has also helped road transport to grow rapidly. Many previous studies have suggested the improvement of the different tracks for better rail freight movement. Laird et al (2002) cited twenty-two such research studies that recommended track improvement as a means to attract more freight to rail. 2.4 Main mode characteristics Road Freight vehicles and units Road based heavy commercial freight vehicles have been classified according to load the vehicle carries and the vehicle size by various studies, (PMFTS, 2000) and (QT, 2001). In several studies, vehicles have been categorized into passenger cars, buses, light commercial vehicles (LCVs), rigid trucks and articulated trucks, (BTCE, 1993), (Apelbaum, 1998), (Murtishaw and Schippen, 2001), (Ahn et al, 2002), (IFEU and SGKV, 2002). LCVs are being used to cater for freight demand in urban areas as heavy commercial vehicles alone can not fulfil the entire freight task and also due to just in time performance. Laird (2003b) noted the rising trend of LCVs freight task from 0.7 billion tonne-km during to 4.6 billion-tonne-km on for Australian urban road freight task. Although these vehicles are very competitive in urban logistics, Dijkstra and Dings (1997) reported that delivery vans have very high energy use and emissions compared to trucks. 10

27 Figure 2. 3 Types of combination vehicles Source: QT (2001) 11

28 Figure 2.3 shows the classification and length restriction imposed by Queensland Transport (QT) on combination vehicles. In addition, there are other restrictions imposed by QT for those vehicles to be able to operate. The restriction for load, load per axle and axle spacing are some of them. Those restrictions depict the concerns regarding safety and infrastructure damage rather than energy consumption efficiency and are mentioned in QT (2001). Sigut (1995) reported on RoadRailer and its use in Australia. RoadRailer is a land transportation technology combining the main features of road and rail modes. The modified RoadRailer is hauled on road by a prime mover and on rail by a locomotive or a modified prime mover Rail Freight locomotives and units In long distance bulk freight movement, rail still dominates the freight task data due to competitive price advantage. ABS (2003) divided locomotives into diesel powered and electric powered. These groups were further subdivided as per their operating system such as on broad gauge, standard gauge and narrow gauge. Table 2. 3a Locomotive classification and numbers Source: ABS (2003) Table 2.3b Wagon classification and numbers Source: ABS (2003) 12

29 Table 2.3a and 2.3b show the number of locomotives and wagons in the Australian rail fleet in 2000 and A large number of the narrow gauge diesel locomotives are owned by Queensland operators (Queensland Rail and Sugar Cane Railways). These locomotives service the Brisbane to Cairns route or the extensive rail network transporting sugar cane. Queensland Rail has the largest fleet of locomotives with 350 narrow gauge diesels and 184 narrow gauge electrics. Other operators with large locomotive fleets are Freight Corp (NSW) and Tranz Rail (NZ) which operate in Tasmania, (ABS, 2003). Hoyt and Levary (1990) classified the locomotives used according to the types of service for which they are utilized. Table 2.4 shows the classes and their respective requirements. The locomotives fulfilling the requirements of respective classes could be grouped into one. Table 2. 4 Types of service and corresponding locomotive class Source: Hoyt and Levary (1990) Lukaszewicz (2001) distinguished the wagons into two categories with respect to their exposure to the outer environment namely Hbis and Oms. Hbis is a covered type wagon whereas Oms is an opened type wagon. Sigut (1995) reported on piggyback technology and its decreasing use in Australia. Piggyback is a transportation technology using road trailers loaded in flat rail wagons. The trailers can be modified (with strengthened underframe) or not. Modified Piggybacks can be lifted by a lifting machine using bottom lift arms, and a special hitch-wagon provides flexible support during the journey. Unmodified trailers have to be loaded by pushing with a prime mover over a ramp, and secured to the wagon by a number of ropes/chains. 13

30 2.5 Energy in freight: Trends Energy consumption trends International trends In Canada, over the period 1990 to 1999, transportation energy use increased by 20.3% or Petajoules. Energy used in freight transport increased by 30.6% (201.5 Petajoules). The freight transport share of total transport energy use increased from 35.1% to 38.1 %, (RAC, 2001). The US also experienced a large growth in transport energy use from 1970 to In the same period, freight sector outpaced all other energy consuming sector in terms of growth in energy use. Vanek and Morlok (2000) noted a 66% increase in freight energy consumption over the same period. In Europe there is a lack of data which describe the trend of energy consumption in the freight sector. EuroStat, one of the largest collectors of those sorts of data, has no such detailed (split) data available as yet. The total transport energy consumption is being considered here, Figure 2.4. Figure 2. 4 Transport Energy Consumption in EU (15 countries) Source: EC (2002) As shown in Figure 2.4, the transport sector energy consumption rose steadily (around 2.3% per annum) over last decade for the fifteen EU countries. 14

31 Domestic trends With the rise in freight task in Australia, there is a related rise in energy consumption. Such an increase in task (58% increment in 20 years) has lead to significant increase in energy consumption in spite of energy efficiency gains. Figure 2.5 shows the trend of energy consumption in the Australian freight transport sector, made up of road, rail and sea modes only. Figure 2. 5 Freight Energy Consumption in Australia Source: Laird (2003b) Energy efficiency trends For rail transport, several previous studies recommended various energy efficiency assumptions for different locomotives on different corridors. Table 2.5 shows the efficiency noted on some of past work in this area. 15

32 Date Source Description Corridor Energy Efficiency (net-tonne-km/mj) 1990 Laird and Adorni Braccesi (1993) Sydney Melbourne to 2 Rail freight For super freighters using 81 class 3000 HP locomotives Benjamin and NR locomotive Melbourne to 2.84 Laird (2001) Brisbane 4000 hp NR Class Australian 2002 Affleck (2002) 2300 Class (supplementary corridor (Corridor 5.18 to 8.64 (gross tonnekm/mj) power for steed specific * grades by other) values were not revealed.) 2002 Rail Freight In Canadian 4.2 Laird (2003b) Corridors 1994/95 Queensland Rail 3 and West Rail 2000 BHP iron ore train Pilbara s Coal train operation Central 5 Queensland 1999/2000 Adelaide Perth Standard super Melbourne 2.7 freighters Sydney Brisbane 1980 Rail freight Sydney to Melbourne 1980 Rail freight Sydney to Adelaide Table 2. 5 Energy efficiency of rail locomotives 1.5 to 2 *conversion factor 38.6 MJ per litre 3 Laird (2003b) noted the improving trend of fuel efficiency on articulated trucks during the 1990s. For the year 1990/91, articulated vehicles were reported to have fuel efficiency of 0.82 net tonne-km per MJ. Within the next eight years (to 1998/99), the efficiency rose to 0.95 net tonne-km per MJ. This is a 16% increase in the average energy efficiency of all articulated trucks. Figure 2.6 shows the comparison of energy efficiency drawn for different modes of freight transport in Australia. It depicts that efficiency has been increasing, except for road transport during The increase in efficiency is also accompanied by the increase in freight energy consumption as shown in Figure

33 Figure 2. 6 Australian domestic freight energy efficiency Source: Laird (2003b) However in Queensland, Apelbaum (1998) recorded the rising trend of energy efficiency on road until 1994/95. Apelbaum (1998) suggested the increase of the energy efficiency due mainly to the effect of introduction of turbo compounding, turbo charging, after cooling, computerized engine control system, reduction in aerodynamic drag and improved drive lines. ATC (1991), on their study of the energy efficiency of both truck and rail in the US, suggested the main contributors of improved fuel economy as: Locomotive design changes; Rail equipment design changes; Truck equipment design changes; Rail operations changes; and Truck operations changes. Road based transport energy efficiency has also been noted in several past studies. Slight variations in energy efficiency values of similar modes and categories have been reported in the literature. Tables 2.6 a and 2.6 b summarize some of the results reviewed. 17

34 Vehicle description Specific energy consumption Source (litres per NTK) 9 axles B Double to axles Articulated truck Affleck (2002) Double road train B Double ACIL(2001) Table 2. 6a Road based transport energy efficiency (aggregated) Conversion factor: 38.6 MJ per litre of diesel Specific energy consumption Vehicle description Urban Non Urban Source Light Commercial lt per NTK lt per NTK Vehicle Rigid Truck lt per NTK lt per NTK Apelbaum (1998) Articulated Truck lt per NTK lt per NTK Articulated truck (40 Highway Rural Road IFEU and tonne Gross weight) 47.7 lt per 100 km 34 lt/100km 36 lt/100km SGKV Average load 47% (0.038 lt / NTK) (0.028 lt / NTK) (0.029 lt / NTK) (2002) Table 2.6 b Road based transport energy efficiency (divided into urban and non-urban) Conversion factor: 38.6 MJ/ litre of diesel for Apelbaum (1998) and 42.7 MJ/Kg for IFEU and SGKV (2002) The energy efficiency data reported by ACIL (2001) broadly agrees with the results shown in Table 2.6 a and Table 2.6 b. For interstate and intrastate rail freight movements, the same value of efficiency (2.5 tonne-km per MJ) was reported. Whereas private bulk rail efficiency was reported to be better by more than two times (6.67 tonne-km per MJ). ACIL (2001) separated the performance of B-Double road vehicles. The reported efficiency for a B-Double road vehicle is 0.92 tonne-km per MJ. AGO (2005) also uses Apelbaum Consulting Group energy data for determining proportion of total consumption of each fuel type by each vehicle type in road transport. 18

35 CHAPTER THREE ESTIMATING MODAL ENERGY CONSUMPTION 3.1 Introduction This chapter deals with the second main area of the literature review which provides an understanding of energy consumed by road and rail transport. The following issues are explored at this stage so as to aid in the development of a modal energy consumption model: The parameters governing the energy consumption and efficiency for each main land transport mode; Existing modal energy consumption models; The parameters that need consideration while comparing the energy consumption between road and rail transport; and Past works on energy comparative methodologies. Figure 3.1 shows the structure of this chapter. 3.1 Introduction Fuel and energy content 3.2 Factors affecting fuel consumption Road transport Rail transport 3.3 Energy consumption models Road transport Rail transport Introduction to intermodal transport 3.4 Energy consumption: Comparative studies Previous comparative methodologies Factors influencing comparative studies Limitations of comparative studies Conclusions from the literature review 3.5 Conclusions and implications Implications for the thesis Figure 3. 1 Structure of Chapter III 19

36 3.2 Factors affecting energy consumption Vehicle type is not the only parameter that affects modal energy consumption and energy efficiency. There are several other parameters that need to be addressed. For a better understanding of fuel consumption, firstly the energy content of the fuel is discussed. Energy consumption influencing parameters for road and rail modes are addressed later in this section Fuel and energy content Fuel consumption is expressed in volume per travelled distance, and is therefore influenced by the energy content of the fuel. For a given thermal efficiency of the engine, the fuel consumption is lower when the energy contained in a litre of fuel is higher. Among the different sources of energy used in Australian freight transport, the energy produced by fossil fuels is expected to dominate. BTRE (2002) assumed diesel to be practically the only source of motive power for articulated trucks to Similarly, for rigid trucks diesel is assumed to be the primary fuel with its share increasing to 95% in For rail (including passenger train), diesel oil has dominated the energy supplied PJ energy was derived from diesel in year 2000 compared to 6.42 PJ using electricity, (BTRE, 2002). ABS (2003) reported the existence of 2035 diesel locomotives in operation compare to 265 electric locomotives (Table 2.3a), which strengthen the fact that still diesel power is driving the large portion of rail transport. BTRE (2002) projected the rise of diesel utilization up to PJ by the year 2020 compared to 8.12 PJ in electricity share. ABARE (1993) reported on the energy content of different kind of fuels. The values reported, which are indicative only, are the gross energy content of the fuel that is; the total amount of heat that will be released by combustion at 15 C and 1 atmospheric pressure. The gross energy content of the Automotive Diesel Oil (ADO) has been listed as 38.6 MJ per litre, (ABARE, 1993). Affleck (2002) also adopted the same 38.6 MJ per litre of diesel as the conversion factor of diesel fuel into energy, as did Laird and Adorni-Braccessi (1993). 20

37 IFEU and SGKV (2002) in their energy comparison of different locomotives in Europe took 42.7 MJ / kg as the energy content of diesel. Wood et al (1981) opted for MJ / kg as the gross energy content of diesel to study the energy consumption by various types of vehicles for the UK conditions. Similarly Wang (2000) reported the energy content being 41.7 GJ per tonne of diesel (35.7 GJ per cubic meter) which is equivalent to 41.7 MJ / kg. Whereas Shayler et al (1999) used the energy content of the diesel fuel as 44 MJ / kg. ABARE (1993) has expressed specific volume of ADO as 1182 litre per tonne. That means ABARE (1993) recommended MJ / kg of diesel as compare to 42.7 MJ / kg that IFEU and SGKV (2002) used in their study for comparing energy consumption. AGO (2005) also reported emission factors relating to energy consumption in the Gross Calorific Value (GCV) to keep it in accordance with ABARE reports. Wood et al (1981) used primary energy equivalent of diesel as kwh per litre. This exceeds the primary energy equivalent that could be derived from Laird and Adorni-Braccessi (1993) by 0.39 kwh per litre. Considering Laird and Adorni- Braccessi (1993) s conversion factor of 38.6 MJ per litre of diesel and 1 kwh per 3.6 MJ, the primary energy of diesel is kwh per litre. Slight variations in the energy content of diesel could be observed across the literature. IFEU and SGKV (2002) explained this variation as the cause of differences in extracting and refining procedures at various locations. This difference in procedure could also result in differences in carbon content and sulphur content of the fuel. This variation of carbon content is expected to have an impact on the gross energy content of the fuel. In Australia, coal is the main source of fuel for the generation of electricity. ABARE (1993) noted that coal accounted for 41.8% of total energy consumption in , but only 4.6% of final energy consumption. This marked difference is due to coal mainly being used in conversion processes, particularly electricity generation. 21

38 Energy Content (MJ / kg) Source Diesel Wood et al (1981) Diesel ABARE (1993) Diesel Wang (2000) Diesel IFEU and SGKV (2002) Diesel Shayler et al (1999) Black Coal ABARE (1993) Table 3. 1 Energy Content of fuel Concawe (1999) reported that density and heating value are the two relevant fuel properties of diesel, but these values alone have no effect on the thermal efficiency and do not induce energy savings. A term called cetane number (CN) was introduced while describing petroleum product s quality and burning tendency on different types of engine, Kagami et al (1984) and Patel (1999). Patel (1999) reported that CN expresses the ignition quality of fuel. The higher the CN, the shorter the ignition delay period leading to lower rates of pressure rise and allowing improved control of combustion. Viscosity of liquid fuel has been considered as another parameter governing fuel quality for energy content. Kagami et al (1984) noted viscosity and its impact on specific fuel consumption and emissions. The specific fuel consumption (km per litre) was noted to have risen until the viscosity reached 5 mm 2 per sec limits. Specific fuel consumption started to decrease slightly as the velocity rose beyond 6 mm 2 per sec. BT (1995) mentioned the effect of low Sulphur diesel on fuel efficiency of heavy commercial vehicles. It raised doubts that the clean diesel and emission control technologies might bring a negative impact on the fuel efficiency of heavy commercial vehicles. 22

39 3.2.2 Road transport William (1977) reported the factors affecting the fuel consumption of heavy commercial vehicles. The factors were categorized according to their relative impact on fuel consumption. Table 3. 2 Factors affecting fuel consumption of heavy commercial vehicles Source: William (1977) Essenhigh et al (1979) studied the variation of automobile fuel consumption with respect to vehicle size and engine displacement. The study concluded that the effect of weight on fuel consumption is much more complex than a simple linear correlation between specific fuel consumption and weight would imply. However, Ghojel and Watson (1995) gave two separate relationships (one for an urban cycle and other for a highway cycle), describing the linear variation of specific fuel consumption of heavy vehicles with respect to vehicle mass. Those relations were reported to have correlation coefficient (R 2 ) of and respectively. The relationship developed by Thoresen (1993) did not provide such a good fit and was developed from the freight vehicle operating cost survey which contained a small of number of heavy commercial vehicles. Similarly, Houghton and McRobert (1998), in comparative study of resource consumption, assumed the linear variation in fuel consumption with respect to gross vehicle mass. 23

40 Other studies, such as Biggs (1988), Bowyer et al (1985), Biggs (1987) and Post et al (1984) categorized the fuel consumption influencing parameters as: Rolling resistance Aerodynamic resistance Inertial forces Grade force Cornering resistance Drive-train efficiency Power required for vehicle accessory Greenwood and Bennett (2001) presented a flow diagram showing above factors and their relative energy utilization, as shown in Figure 3.2. Those authors argued that only 18 percent of the total energy in the fuel is available to propel the vehicle along the road under typical urban driving conditions. Figure 3. 2 Energy Train for a typical urban car Source: Adopted from Greenwood and Bennett (2001) BT (1995) noted major fuel consumption influencing parameters as the age and type of vehicles in operation, condition of the equipment and standards for maintenance and repair, technologies used, terrain travelled and driver's skill. Ahn et al (2002), in a study on energy consumption patterns of cars and light commercial vehicles, categorised the variables influencing vehicle energy rates into six broad groups. 24

41 Category Travel related Weather Related Vehicle related Roadway related Traffic related Driver related Table 3. 3 Factors Distance between two terminals, number of trips etc Temperature, humidity, wind effects etc Engine size, the condition of engine, equipments in the vehicles such as AC, catalytic converter etc Road grade, surface roughness, etc Vehicle to vehicle interaction and vehicle to control interaction Driver behaviour and aggressiveness Factors influencing vehicle energy consumption rate Source: Ahn et al (2002) There are various small additional fuel consumption needs to be fulfilled, such as those due to evaporation loss (EC, 1999); cold start (Chang et al, 1976); tyre pressure variation increasing the rolling resistance; and small fluctuations of speed (Biggs, 1988) and (BT, 1995) Rail transport Meibom (2001) illustrated different operating phases of any vehicle and described the fuel consumption requirement of each phase. Figure 3. 3 Model of energy flow in vehicle Source : Meibom (2001) Figure 3.3 could be used to study the energy consumption influencing factors. The losses of fuel through the energy storage unit, such in the form of evaporative losses, influence the final energy consumption of a vehicle. Factors such as type of engine 25

42 and motors would have a higher impact on energy consumption, as those factors govern the type of fuel required and energy conversion efficiency. The losses during transmission also have an influence on the final energy consumption. Since the major portion of energy is utilized on tractive force, the latter is an important parameters governing energy consumption. In addition, the energy requirement for running other accessory functions, such as air conditioning, lighting, etc., also influence the final energy consumption. IFEU and SGKV (2002) addressed the following factors as important parameters to be considered for rail fuel estimation: Traction type (diesel or electric); Train length and total weight; Ratio of gross to tare mass of train and unit load devices; Route characteristics (gradient, curvature); and Driving behaviour (speed, acceleration) and air resistance. Lukaszewicz (2001) derived an energy consumption estimating relation with assumptions that the energy consumption of a train varies with: Track parameters such as radius of curvature, rail pads (e.g., hard rubber, steel, soft rubber), track type (e.g., continuous welded, jointed, etc), ballast and grade. Mechanical and physical parameters such as wheel radius, gear ratio, engine conversion efficiency, length and face of train and type of wagon. Operating conditions such as velocity, acceleration, load and rotational inertia. External factors such as wind and climate, which might affect slippage ratio along with other factors relating to track and vehicle; and Driver s behaviour. EC (1999) considered steady load, velocity and number of stops as significant parameters that could describe the energy consumption of train. Earlier Jorgenson and Sorenson (1998) had used a similar approach to estimate rail fuel consumption. 26

43 Hoyt and Levary (1990) grouped the factors influencing rail transport fuel consumption as train characteristics, terrain characteristics and other unpredictable variables, as shown in Table 3.4. Table 3. 4 Factors influencing rail fuel consumption Source: Hoyt and Levary (1990) Type of track, wagon, velocity, number of stops and driver s behaviour were not mentioned by Hoyt and Levary (1990). These factors are listed on other studies, such as EC (1999) and Lukaszewicz (2001), as parameters affecting rail fuel consumption. 3.3 Energy consumption models Road transport Passenger cars and light commercial vehicles (LCV) During the 1970 s, the energy consumption of cars and LCVs were estimated using regression models using speed as the single most important independent variable. Chang et al (1976) used distance between links and travel time for fuel consumption estimation. This type of average speed model continues to be used due to its simplicity and acceptable accuracy. Bowyer et al (1985) and Biggs (1988) also used the average speed formulation along with other models. To better describe the fuel estimation, the terms such as rise, fall and roughness were introduced in those regression (empirical) models. Greenwood and Bennett (2001) reported the form of those equations as: Fuel consumption = a0 + a1/v + a2 v 2 + a3*rise + a4*fall + a5*roughness Post et al (1984) compared the results of a more complex power demand model with the simple average speed model and concluded that both give similar results and accuracy for longer distance trips. Bowyer et al (1985) stated satisfactory 27

44 performance of the average speed model on a long distance provided average travel speeds are not high. In the 1980 s, advances in fuel consumption modelling leaded to the incorporation of various other parameters. Post et al (1984) developed a relationship between fuel consumption and power developed at the vehicle s tail shaft. The tail shaft power (Z tot ) represented a summation of drag power, inertial power and gradient power. Two constant terms were introduced representing the idle fuel consumption and efficiency. FC (ml/min) = α + β Z tot for Z tot 0 kw = α (ml/min) for Z tot < 0 kw Ferreira (1985) developed an empirical relation for estimating urban fuel consumption using data from Leeds, the UK. The fuel consumption influencing factors such as stop/start and slowing down was incorporated in that model. Bowyer et al (1985) classified different types of models into four groups, namely, Average speed model; Running speed model; Four mode elemental model; and Instantaneous model. The shortcomings of average speed models, such as its inability to differentiate fuel consumption during the running and idle phase, led to the development of running speed model. Running speed fuel consumption model incorporates the average effect of grade, effect of difference in fuel consumption while running and idle. Bowyer et al (1985) reported that this model could underestimate the fuel consumption over a trip and the error was related to the grade term. Further moves towards accurately estimating the energy consumption led to the development of the four mode elemental model. This type of model was also reported by Akcelik (1983). Bowyer (1985) presented a refined form of the same model, which estimates fuel consumption by classifying a vehicle operation into four phases, namely: idle, cruise, acceleration and deceleration. As reported by Post et al (1984), average and running speed models can not estimate energy consumption well for short section (less than 5 km) whereas four mode elemental model could be used. 28

45 Instantaneous model resembles the model developed by Post et al (1984). These models explain fuel consumption in small time increments. The use of instantaneous model on long road section is less likely to improve the energy estimation result and it only increases the complexity of calculation. Bowyer et al (1985) and Post et al (1984) suggested the good performance of previously mentioned simpler (average speed and regression) model over instantaneous and four mode elemental models when it comes to longer trip distances. For specific and more accurate calculation of fuel consumption, relations based on mechanical performance of the engine have been developed. Shayler et al (1999) developed such a model and predicted fuel consumption using characteristic relationships of engine. Specific fuel consumption estimation was based on gross indicated power which is related to compression ratio and spark timing. In recent times, there is the emergence of data-based models as developed by West et al (1997), who tested the vehicle on road and on dynamometer to establish relationship between fuel consumption, vehicle speed and acceleration. Ahn et al (2002) developed a regression model that uses instantaneous speed and acceleration to estimate energy and emissions. Unlike West et al (1997), the model was divided into two relations so as to correlate the vehicle fuel performance with change in the nature of acceleration (positive and negative). This change in the acceleration induced a different set of regression coefficients. Tong et al (2000) studied the fuel consumption of the light duty petrol and diesel van, petrol passenger car and double-decker public bus. The relationship between instantaneous speed and fuel consumption was established for each vehicle class. A summary of fuel consumption models reviewed is presented in Table

46 Table 3. 5 Cars and light commercial vehicles fuel consumption models Heavy commercial vehicles Biggs (1988) extended the work of Bowyer et al (1985) to include heavy commercial vehicles (up to 40 tonne articulated trucks). The set of models, known as ARFCOM, used three categories, namely; instantaneous fuel consumption model, elemental model and running speed model. Figure 3.4 shows the approach that ARFCOM used for modelling fuel consumption. 30

47 Figure 3. 4 ARFCOM approach to modelling fuel consumption Source: Biggs (1988) The developed models incorporate various power components such as power needed to overcome rolling resistance, aerodynamic resistance, inertial force, grade force, cornering resistance and power needed for vehicle s other accessories. HDM-4 (Highway Development and Management) took the ARFCOM model as a basis to quantify the fuel consumption as a part of estimating vehicle operating cost, (Greenwood and Bennett, 2001). Instantaneous fuel consumption models are well suited to congested traffic conditions. However, such models require a high computational effort and the vehicle must be coupled with microscopic traffic simulators. For estimating fuel consumption of commercial vehicles at the corridor level, the running speed submodel is likely to be appropriate. Some precautions are necessary for using the models to allow for fuel estimation during negative power demand phase, frequent change in vehicle parameters and underestimation of the grade effect. The effect of these shortcomings could be reduced by dividing the road length into homogeneous sections and recalibrating the model for new vehicle parameters. Since these mechanistic models do not have a variable describing speed-smoothness explicitly, they are insensitive to small changes in traffic conditions. Thoresen and Roper (1996) suggested the necessity of further research to validate ARFCOM roughness estimate since the effect of speed variability associated with higher roughness values are not catered for. 31

48 Kent and Mudford (1979) suggested a different approach which uses the carbon balance method to estimate fuel consumption. EC (1999) also suggested a similar method for commercial vehicle fuel consumption estimation. Kent and Mudford (1979) suggested the use of two different phases namely cruising and non cruising phase, having different emission relations, whereas EC (1999) did not make the differentiation. In the case of Kent and Mudford (1979), the suggested emission relation was a function of speed and acceleration, not speed alone as for EC (1999). Energy consumption models having speed as the only influencing parameter have been used for sometime. Tomita (1997) used a speed regression model developed by Adachi, Mori and Fujushiro in The developed relations were a polynomial function of speed. Meibom (2001) suggested a more complex model that estimates the energy consumption per driving cycle as a function of tractive force used to overcome air resistance, rolling resistance, difference in potential energy, energy needed for auxiliary purposes, load, average transmission efficiency and thermal efficiency. The difficulty in the application of the model is likely where there are large vehicle categories with different route parameters. Wang et al (1992) also reported an analytical model for energy consumption which dealt with mechanics of the vehicle system and evaluated the motion phenomenon of the system. The developed analytical model estimates the energy requirement over a cycle for a given vehicle and driving cycle. Other approach includes estimating fuel consumption as a function of number of cylinders (z), engine speed (n) and fuel injected to the engine ( ) at every instance (Sandberg 2001). t end m fuel = ( n * * z / 2 * 1 / ) dt t o A summary of fuel consumption models reviewed is presented in Table

49 Model type Comments References Regression Various studies developed a correlation between energy consumption and fuel consumption influencing factors. Tomita (1997) Running speed Divide the operation of a vehicle into two Biggs (1988) phases; run and idle. Have little room to incorporate the effect of grade and inertial power. High chance of underestimating the effect of grade on a long trip. Four mode elemental Divide the vehicle s operation into four phases namely idle, cruise, acceleration and deceleration. Could be used for short trips. Biggs (1988) Instantaneous Based on emission and carbon balance method Analytical Table Rail transport Estimate the fuel consumption for a small increment in time and length. These types of models include a large set of input parameters. Carbon emission from the vehicle was correlated with speed and then later the carbon balance method was used for estimating fuel consumption. Have speed as the only energy consumption influencing parameter, other factors should be covered by the coefficients. Estimates the energy required over a cycle for a given vehicle and driving cycle. Heavy commercial vehicles fuel consumption models Biggs (1988) EC (1999) Wang et al (1992) Meibom (2001) Kraay et al (1991) developed an energy consumption model based on energy needed to overcome resistance along with an energy parameter related to change in kinetic energy. The resistance term in the relation accounted for grade, radius of curvature, mass of train, air friction, rail friction and speed. Different coefficients were adopted for correlating those terms with energy consumption which would depend on train and track type. The energy consumption of a train has been estimated using speed as a prime influencing factor. EC (1999) suggested a function of average speed and distance between stops for train energy estimation. EC (1999) and Sorenson (1998) present values of the constants determined after calibrating such models on particular corridor and locomotives. 33

50 Energy consumption (KJ / tonne km) = k * V 2 avg / ln(x) + C where: k and C are train dependent constants; x is the distance between stops in km; V avg is the average speed over the section of the route under consideration in km/hr. A similar model was suggested by Jorgenson et al (1998) for typical German ICE trains (with the values of k and c being and 74 respectively). In those models, the effect of grade is supposed to be incorporated by average speed as the effect of grade would be to reduce the average speed. EC (1999) also suggested another method for train energy estimation, which is based on steady state loading of the train. Steady state train loads in kn were converted to kj/tonne-km for several types of trains and were found to have a second order dependence on train speed due to aerodynamic loading. The integrated energy consumption for a train over a given route was ultimately expressed as a function of number of stops (N STOP ), change in elevation ( h), average and maximum speeds to which the train accelerates. E = (N STOP + 1) / L * V 2 max / 2 + B 0 + B 1 * V avg + B 2 * V 2 avg + g * h/l where: B 0, B 1 and B 2 are empirical coefficients for the steady state load. The model was found to produce good results where: there are less number of stops; and the acceleration and deceleration process are not that frequent. The model was found difficult to apply where: there are significant changes in the variables. There is a need to separate the route into homogeneous sections in presence of large variable set. there are difficulties in determining the true numbers of accelerations and V max that might occur because of traffic conditions. This affects the first term of the equation by underestimating acceleration energy consumption. 34

51 Lukaszewicz (2001) expressed train energy consumption as a function of tractive force at wheel, velocity, acceleration, slippage ratio, efficiency of the conversion and the locomotive s mechanical and physical parameters such as wheel radius and gear ratio. Separate relationships were obtained explaining the energy demand during coasting and non coasting phase. This approach could be suitable to compare the efficiency of different types of trains and track. IFEU and SGKV (2002) used a very simplistic approach for an energy consumption comparative study. Specific energy consumption per train-km (EC train, in wh / km) was calculated as a function of gross weight of train (M train, in tonne). 0.6 EC train = 315 * M train This model lacks a proper description of other fuel consumption influencing parameters such as slope, slippage, track curvature and speed variations. At the corridor level, it would be prudent to consider the steady state loading type model suggested by EC (1999), or the model suggested Kaary et al (1991), since these models were reported to have satisfactory performance when there were fewer stops and tracks were easily divided into homogenous sections. Table 3.7 summarises models reviewed here. Model type Comments References Power demand Estimates the energy required based on the Kraay et al (1991) power needed to overcome resistances and change in kinetic energy. Regression Table 3. 7 Various relations have been established between energy consumption by train and their operation parameters such as velocity, number of stops, mass etc. Rail fuel consumption models 3.4 Energy consumption: Comparative studies Lukaszewicz (2001) EC(1999) Jorgenson et al (1998) IFEU and SGKV (2002) Several Australian Railway Association (ARA) rail fact sheets argue that rail freight transport consumes much less energy than road transport. However rail alone cannot fulfil the entire responsibility of freight task. Hence for energy consumption comparison the concept of comparing the intermodal land transport with road 35

52 transport seems to provide more comprehensive and acceptable results than a single mode comparison. To better understand the door to door energy consumption, this section has been subdivided into introduction to intermodal transport, previous comparative methodology, factors affecting comparative results and limitations of previous studies Introduction to Intermodal transport Mahoney (1985) described intermodal transfer as a transfer of commodities or goods between two modes. Mahoney (1985) also emphasised containerization and intermodality as not being synonymous but the use of containers compatible with two or more modes greatly improved intermodal transfer of general cargo. To provide a seamless intermodal transfer between road and rail, the units such as piggy back, roadrailer, swap bodies have been in use (Mahoney, 1985 and Robl, 2002). There has been a significant improvement in the intermodal technology over the years. This could be confirmed by the technologies described in Mahoney (1985), Sigut (1995) and Robl (2002). Intermodal movement can be defined as the movement of goods in one and the same loading unit or road vehicle, which uses successively two or more modes of transport without handling the goods themselves in changing modes. However, for the purpose of this study, intermodal transport is defined as a system of moving goods from origin to destination which involves road and rail. IFEU and SGKV (2002) and Affleck (2002) compared the door to door energy consumption between road and intermodal transport. Both the studies found an energy advantage of intermodal transport in most cases. The development of seamless intermodal facility could be expected to further enhance the energy advantage and smooth freight movement. Different equipment is used in intermodal transfers phases such as move, stack and load-unload. Robl (2002) classified these lifting equipment according to the position from which they lift the trailers, namely bottom pick and top pick. All these 36

53 operation phases of intermodal transfer consume energy and the magnitude differs from adopted system and technology. Figure 3. 5 Intermodal transfer of various carriage units Source: accessed on August 4, Previous comparative methodologies IFEU and SGKV (2002) carried out an energy consumption comparative study to confirm the validity of the argument that shifting the freight load from road to rail would significantly reduce the energy consumption and greenhouse gas emissions. The study examined the energy consumption and greenhouse emissions in nineteen corridors in western and central Europe. They used average specific energy consumption data of trucks for different road types to estimate the fuel consumption for a total trip. The TREMOD model was used for quantifying the influence of load factor which estimates the fuel consumption for empty run to be as low as 2/3 of the fuel consumption of the fully 37

54 loaded value. For quantifying the grade influence multiplying factors such as 3.7, 0.3 and 1.5 were used for upgrade, downgrade and average grade respectively. Similarly, for estimating energy of rail transport, a specific fuel consumption relation was used as discussed in section To incorporate the difference in the specific energy consumption due to the difference in mass of certain wagon, the train was first compared with the reference set. Then the difference in those two set was adjusted to the considered wagon (unit) for finding the specific energy consumption rate of that unit. IFEU and SGKV (2002) considered the energy consumption in intermodal transfer phase and concluded that in the whole comparison process the energy consumed in these cycles are insignificant even for the shorter distance of 100 km (less than 3%). When energy consumed for shunting was combined with intermodal transfer phase, the significance did not rise by much. For the whole comparison process, IFEU and SGKV (2002) opted to omit the effect of shunting and intermodal transfer on energy consumption. Following IFEU and SGKV (2002) conclusion, Affleck (2002) opted to exclude the energy consumed in shunting and intermodal transfer in their comparative study carried out in seven freight corridors in Australia. Affleck (2002) based their study on the methodology suggested by IFEU and SGKV (2002). Hence, there is very little difference in the comparison methodology between those two studies, except for train fuel consumption estimation. Unlike IFEU and SGKV (2002), Affleck (2002) used corridor specific in service fuel consumption rates to calculate the locomotive fuel consumption. For truck fuel consumption, both studies used the actual fuel consumption rates collected from various sources. Affleck (2002) adopted typical pick up and delivery distance as suggested by road freight operators. Haferkorn (2002) compared the performance of truck and various types of freight trains. For a fair comparison, all the vehicles were loaded with the same containers (40 ft sea containers) and a payload of 30 tonne. Values for air and rolling resistance 38

55 were adopted from the literature. The vehicles performance was measured for transporting 1 million tonne-km per hour. This method is not related to energy consumption alone. The method induces a cost indicator which explains and prepares a base for comparison involving cost factors such as transport operating and depreciation costs. Dijkstra and Dings (1997) compared the specific energy consumption and emissions of freight transport between road, water, rail and air. The comparison was focused on specific energy consumption of those modes per effective kilometres (straight line distance between two points). However the detour factors would vary with location and plays a major role in the final energy consumption comparison. The average load factor and maximum load were also acknowledged as major characteristics of the freight transport modes. For the comparison, energy consumption data were collected and analysed to obtain the average specific energy consumption data by classified vehicle class. The comparison was limited to the mode level rather than energy consumed for door to door service. Another similar comparative study was carried out by ATC (1991). However, unlike other studies this study used computer simulation for truck and rail energy calculation (Vehicle Mission Simulator (VMS) for truck and Train Performance Simulator (TPS) for train). The study also considered specific routes, loads, and equipment. The results were determined along real transportation corridors like IFEU and SGKV (2002) and Affleck (2002). The results include calculations for fuel used in local rail switching and terminal operations. Rail demonstrated better fuel efficiency for all combinations of vehicle, from 1.4 to 9 times better than trucking. However, ATC (1991) noted the decrease in the relative advantage of rail compared to truck due to circuitous route. In addition, the report describes changes in the design and operations of both rail and truck transport and attempts to quantify the impact on fuel efficiency of each. Houghton and McRobert (1998) developed a worksheet model to compare resource consumption. The comparison lacks the proper explanation of pickup and delivery energy consumption and the variations in representative vehicle classes are limited. 39

56 3.4.3 Factors influencing comparative studies IFEU and SGKV (2002) noted some influencing factors that need consideration for a comprehensive comparison of combined transport rail/road versus road transport. Figure 3.6 summarizes these factors. Figure 3. 6 Factors influencing a comparative study Source: IFEU and SGKV (2002) IFEU and SGKV (2002) noted the factors such as energy supply mix, as a major influencing factor in comparative energy consumption. If electricity is used, about 2/3 (depending upon the input mix) of the supplied energy could be used for conversion and the upstream process steps (such as extraction and processing of fuels for electricity generation). Whereas for diesel fuel, the final energy use contributes 40

57 about 90% of the total primary energy demand. Similarly, load factor was also given a high importance along with distance. For determining the load factor variation, the TREMOD model was used. The factors such as return trips, characteristics of freight and logistics were also expected to have an influence in the whole comparison process. Shunting, intermodal transfer and grade were given very less priority Limitations of comparative studies IFEU and SGKV (2002) used specific fuel consumption rates collected from various sources, to describe the fuel consumption of heavy commercial vehicles. This approach would limit the outcome to the corridor levels and would be difficult to transfer the results. IFEU and SGKV (2002) assumed that the grade effect for rail locomotives would be described by the extra weight that the added locomotive would induce for dragging the train at grade. No data was provided to support this assumption. IFEU and SGKV (2002) categorized the road vehicle s fuel consumption based on urban, non urban and rural cycle. The classification ignores other traffic and road related parameters. There are various vehicle categories that need to be considered such as LCV, rigid truck and articulated truck. The differentiation of the types of vehicles on the pickup and delivery legs could bring more understanding of the ultimate fuel consumption. As Affleck (2002) used the similar methodology, the study inherits the same limitation that of IFEU and SGKV (2002). Moreover Affleck (2002) used corridor specific fuel consumption data for both road and rail unlike IFEU and SGKV (2002) that used those data for road transport only. However, ATC (1991) used computer simulations which are not reviewed here. ATC (1991) mentioned that for truck simulation only one truck engine was selected, that is the Cummins 350. The energy consumption of empty back hauls was not considered in either of the comparative studies reviewed. Commodities were not classified in Affleck (2002) and IFEU and SGKV (2002). ATC (1991) and Affleck (2002) used tonne-km to represent freight task. Hence 41

58 these studies carry the deficiency of tonne-km method (as mentioned on BT (1995)), such as not being able to represent cubic volume and speed. ATC (1991) assumed that truck freight moves directly from origin to destination. However, there is a possibility of involvement of different freight vehicles for pickup and delivery legs when freight is being carried by long vehicle on road (Houghton and McRobert, 1998). 3.5 Conclusions and implications Conclusions from the literature review Among the various modes of freight movement, land freight movement modes were studied in detail for understanding their energy utilization characteristics and the main influencing factors. The growth of road freight movement has been found to be significant compared to rail. Several previous studies suggested rail as an energy efficient mode when measured on tonne-km per fuel consumption basis. The review looked at ways of estimating energy consumption of a complete freight task i.e., from origin to destination. Models developed for estimating the energy consumption for rail, heavy commercial vehicles and light commercial vehicles were reviewed. Approaches and methods used previously to establish the relationship between energy consumption influencing parameter and fuel consumption were also studied, so as to aid in developing the relationship during model development. For road fuel consumption estimation, models have been divided into instantaneous, four mode elemental, running speed and average speed, and carbon balance. For rail fuel estimation no such hierarchy of fuel consumption model has been established. The models reviewed are grouped as mechanistic (or power demand) and regression models. The latest methodologies adopted in freight modal energy comparison were studied. It has been acknowledged that for the comparison purposes, some additional parameters such as trips length and regulatory constrains also need consideration. The review supports the homogeneous division of road section and grouping of like vehicles for increasing the accuracy of estimation. The review suggests the grouping 42

59 of road sections as per terrain and traffic characteristics and vehicles as per load they are carrying, number of tyres and axles, power and engine capacity Implications for the thesis There are numerous studies carried out for quantifying the fuel consumption of rail and road modes. However, there is a lack of a proper methodology for comparing the fuel consumption performance of rail and road on Australian corridors. Studies such as Affleck (2002) started such comparisons, however, the results are corridor specific and cannot be implemented for other corridors. Hence, this research is focused on comparing the fuel consumption of rail and road taking into account the presence of various traffic and terrain characteristics. In this way, a general model may be implemented for application to various corridors within and outside Australia. Various fuel consumption influencing factors that are of importance to this study have been identified (Section 3.2 and section 3.4.3). Some of the important factors to be considered in this study are: Pay load Gradient Speed Roughness Vehicle category Road type and congestion The energy contained in a litre of diesel is taken as constant (38.6 MJ per litre) in spite of slight variations. Figure 3.7 highlights the route and transport characteristics that were compared for the proper understanding of the energy consumption of a freight vehicle from origin to destination. This study focuses on quantifying the total energy consumption on each of those routes. 43

60 Origin Collection Rail line haul Road leg (pick up) Could be LCV or rigid truck or articulated truck Total haulage by Road (by rigid or articulated truck) Road leg (pick up) Could be LCV or rigid truck Collection Road line haul (by rigid or articulated truck) Collection Road leg (delivery) Road leg (delivery) Collection Figure 3. 7 Could be LCV or rigid truck or articulated truck Comparison routes Destination Could be LCV or rigid truck 44

61 CHAPTER IV MODEL DEVELOPMENT 4.1 Introduction This chapter describes the development of a tool to undertake energy consumption modal comparisons between land based freight modes. The main issues addressed here are: the definition of model requirements and specification; estimation procedures and model calibration and validation. A graphical depiction of the comparison methodology adopted here is presented in Figure 4.1. The main elements of Figure 4.1 and the methodology are described in section 4.2 to 4.5. A spreadsheet model is presented in section 4.6. Task 1 Selecting fuel efficiency measuring unit Classifying commodities Task 2 Determination of Rail Fuel Efficiency Defining route characteristics Determining train characteristics Fixing of the sub model parameters Establishing a relationship between those parameters and fuel consumption Determination of Road Fuel Efficiency Defining route characteristics Determining road vehicle characteristics Fuel consumption model selection and Harmonization of the model using vehicle simulator Application of the two sub-models to a comparison model Task 3 Calibration, validation and application of the model. Figure 4. 1 Overview of model development methodology 45

62 4.2 General model requirements Selecting the fuel efficiency measuring unit MJ per tonne-km is adopted as the fuel efficiency measuring unit. The advantage of using MJ per tonne-km is the ability of the unit to measure the freight task as well as distance moved. A similar unit of fuel efficiency measurement has also been used by past comparative studies ranging from ATC (1991) to Affleck (2002). The summary table of the comparison model compares the different freight task options based on MJ per tonne-km Classifying the commodities In some cases quantifying the energy used in terms of MJ per tonne-km would not totally describe other various aspects of freight task (BT, 1995). For example, moving one tonne of cotton or steel would be different because of the density variation between cotton and steel, in which the former would require larger space to move. Hence commodities are classified for better understanding of freight movement and the energy consumption related to them. The adopted classification is shown in Appendix A Route characteristics Routes sections (for both rail and road movements) are to be divided based on homogeneity of alignment characteristics including grade and curvature, as far as possible. In addition, for road transport, homogeneous route division based on congestion and pavement roughness assist in better estimating the fuel consumption. For rail corridor descriptions, Houghton and McRobert (1998) used parameters such as track length; number; location and length of crossing loops; level crossings; number and type of sleepers (timber, concrete and steel); rail gauge (standard, narrow and board); height clearances; ballast (type and depth); track alignment; average speed and speed limits; signalling systems; and axle load limits. EC (1999) and Jorgensen and Sorenson (1998) suggested the use of number of stops and speed to 46

63 describe the train operating characteristics. Based on these recommendations and much used approach of Davis Formula (AREMA 1990), the route and operating condition description included parameters such as grade, curvature, track gauge, length of train, speed and mass Determining vehicle characteristics Road Since it is not possible to model each individual vehicle in the traffic stream, we resort to the use of representative vehicle for calculating energy consumption. In this thesis, the vehicles are classified in the following broad categories, namely: Light commercial vehicle (includes utility and two axle single tyred truck) Heavy commercial vehicle o Rigid o Articulated These categories are further divided into total of 19 different vehicle classes used in the model. The characteristics of the vehicles and respective grouping are shown in Appendix B. Rail Similarly for rail, fuel consumption depends on power and size of train. The trains are not classified in this study. The approach used to determine the power of the train depends on forces that the train overcome during the propagation. The assumption is the locomotive (or the combination of locomotives) to the nearest match of the power demand is available to drag the train on the track Data collection The train consist information of various types of train in operation between Brisbane to Toowoomba was collected from Queensland Rail (courtesy: Mr. Mark Nash). For the rail track information between Helidon to Gowrie, reports published by QR have been referred (QR 2001 and QR 2003). These are discussed in Chapter VI. 47

64 The route profile data of the Warrego Highway between Postman Ridge and Gowrie Junction was collected from Toowoomba District, Department of Main Roads (courtesy: Mr. Doug Head). The data was extracted from the provided drawings. A separate speed profile drawings were provided so as to aid in estimating speed of the assumed vehicle run. Brunswick Street Office of Queensland Transport (courtesy: Mr. Les Brusza) assisted while calibrating the road sub-model with the help of vehicle run simulator, namely Design Pro. The Design Pro is a vehicle run simulator proprietary to Caterpillar Inc., Peoria, Illinois, USA. They advocate that Design Pro would best specify the Cat engine and the best drive-train for any application with manufacturer specific product information. 4.3 Road transport sub-model Background The criteria adopted here in selecting a road transport sub-model were as follows: Ease of use Availability of appropriate input data Applicability of the model to Australian conditions Ability to deal adequately with the different energy influencing parameters NIMPAC style models satisfy three of those four selection criteria. NIMPAC is easy to use and understand due the simplicity of its algorithm. Input data set contain parameters that are easily available on the public domain and that are simple to understand and input. NIMPAC style model has been widely used in Australia. The Queensland Department of Main Roads is also using NIMPAC models for estimating Vehicle Operating Cost (VOC) parameters for non urban road project evaluation. NIMPAC style model being discussed here uses Look-Up Table approach. Thoresen (2003) reported that the look-up table approach does not allow the analyst to compute the combined, direct and indirect, effects that a particular traffic parameter may have on fuel use. For example, an increase in average gradient will directly increase fuel consumption by a specified amount at every speed of travel, but may 48

65 also indirectly affect fuel use through a reduction in the estimated speed of travel, could only estimate the fuel consumption by an appropriate choice by the user of travel speeds. NIMPAC was analysed in terms of dealing adequately with fuel estimation. The general algorithm of the NIMPAC model is (Thoresen and Roper, 1996): Fuel Consumption (litres/1000km) = Basic Fuel/Speed 1 Relationship Engine + Efficiency Adjustment + Gradient Curvature + Adjustment Adjustment + Road Roughness + Adjustment Traffic Congestion Adjustment Basic Fuel Speed Relationship Eq. 4.1 The literature review revealed that speed is one of the main parameters governing fuel consumption. The basic fuel/speed consumption relationship is adopted from NIMPAC model, which is: (Thoresen and Roper, 1996) Basic fuel consumption (l / 1000 km) = A + B / speed + C * speed 2 Eq. 4.2 This basic fuel speed relationship predicts the amount of fuel consumed over a flat straight road assuming vehicles at approximately constant speed with a complete absence of traffic congestion. Hence, the basic relationship can only shows how fuel consumption varies with various constant speed of vehicle. The coefficients of Eq. 4.2 vary with vehicle class and types. Appendix B contains the data presented by Thoresen (2003) for those constants. The value of coefficients (A and B) increase as the vehicle gets heavier. The value of coefficient C remains almost constant, between and 0.02, for vehicles considered in this study. Figure 4.2 shows the variation in fuel consumption for different speed and vehicle when they are drawn for vehicle travelling on flat and straight road section, NRM roughness count of 100/km and a volume to capacity ratio of 0.5. Hence, Figure 4.2 does not represent basic fuel demand based on basic speed/fuel relationship; however it shows the cumulative impact of factors mentioned above. The figure shows that the effect of speed on fuel consumption would be more prominent as the vehicle gets heavier. It portrays that the fuel efficient range comes in between 50km/h to 70km/h depending upon the type of vehicle. 49

66 Fuel cosumption VS Speed Specific Fuel Consumption (litres per 1000 km) Utility Vehicle Large Rigid Truck (3 axle 10 tyres) Articulated Truck (4 axle) B Double Speed Figure 4. 2 Fuel consumption versus vehicle speed Source: NIMPAC Model It was found that one of the important missing parameters on fuel consumption subroutine of NIMPAC style model is payload. This thesis is concentrated on freight movement comparison and vehicle payload is of prime importance. Vehicle operating modes The operation of the vehicles is to be divided into homogenous sections, according to vehicle and route characteristics. Each section should be differentiated with every change of speed and payload. And for even finer estimation, it is recommended to differentiate the section with change in road roughness, curvature, gradient and congestion level. Vehicle types Vehicle type is to be chosen from the set of representative vehicles. The representative vehicle set is adopted from Thoresen (2003). If any new set of vehicle is to be entered then the base data should be increased with the specific fuel consumption data (or speed and specific fuel consumption relation), along with the required set of data for correction factors such as payload, road roughness, congestion level and gradient. 50

67 4.3.2 Amendment to NIMPAC algorithm As mentioned in section 3.2.2, payload has a significant impact on energy consumption. Ghojel and Watson (1995) reported a good linear fit between basic fuel consumption and payload. IFEU and SGKV (2002) also successfully used a multiplying factor to incorporate the effect of variation in payload. It is already a tested approach to quantify a variation in payload as a multiplying factor. Therefore a payload correction factor was applied in Eq. 4.1, which becomes: Fuel Consumptio n (litres/10 00km) = Basic Payload Fuel/Speed Correction Relationsh ip Factor 1 Engine + Efficiency Adjustment + Gradient Adjustment Curvature + Adjustment + Road Roughness Adjustment + Traffic Congestion Adjustment Eq. 4.3 Payload correction factor ARFCOM (and HDM-4) model also conferred a high importance to the vehicle mass in fuel estimation. The concept of load factor is an appropriate method for the adjustment of fuel consumption rates, which is a function of vehicle mass as well. CSIRO, PPK and UniSA (2002) suggested a linear relationship between load factor and correction factor for fuel consumption as shown in Figure 4.3. Here the load factor implies the ratio of the load a vehicle is carrying to the total load that vehicle can carry and load correction factor implies the corresponding correction factor (multiplying) to be entailed in fuel estimation equation. Relationship between load factor and correction factor for fuel consumption Load Correction Factor Load factor (%) Figure 4. 3 Relationship between load and fuel consumption correction factor Source: CSIRO, PPK and UniSA (2002) 51

68 Figure 4.3 shows that load correction factor increases linearly with the increase in the load factor. Load correction factor (LCF) is 1 for 50% indicating the establishment of the basic fuel consumption relationship for 50% load factor. CSIRO, PPK and UniSA (2002) suggested fuel estimation equation is: Link Fuel Consumption ( L) Vehicletypei = [ Volume* Fleet Pr oportionij * Fuelconsumption( L / km) ij Fueltype j * SpeedCorrectionFactor( SCF) ij * LCFij * Length] In the model developed here, the mass that the vehicle is carrying is input by the user. This would induce a correction factor to incorporate the effect of mass on fuel performance of the vehicle. Payload correction factor was derived by running the computer based vehicle simulation model namely Design Pro Adjustment factors Engine efficiency Adjustment State of tune factor (FCAVF) models the engine efficiency adjustment factor of Eq The state of tune factor is dependent on type of vehicle. Thoresen (2003) expanded the limited vehicle categories of the NIMPAC model by including more combination of rigid truck and articulated vehicles. The extended vehicle set was accompanied by the corresponding FCAVF value. Thoresen (1988) reported that on ninety vehicles tested, the tuning of vehicles to manufacturers specifications had minimal effect on fuel use. On average, data indicated that untuned vehicles consumed only about one per cent more fuel compared with their fuel use when tuned. Gradient Adjustment NIMPAC style models use two separate paths to quantify the effect of grade - one is direct and the other is indirect via its effect on speed (Thoresen and Roper 1996). Since speed is not estimated internally in this model, the second effect has not been considered. 52

69 The input grade along with speed data and vehicle type will determine the gradient adjustment term of Eq The model uses the revised NIMPAC gradient adjustment lookup table presented in Thoresen (2003). The gradient adjustment was revised to incorporate the effect of grade ranging from 4% to 10% for the extended set of representative vehicles. Appendix C gives an overview of the gradient adjustment lookup table. Specific Fuel Consumption (litres per 1000 km) Fuel cosumption VS Grade Utility Vehicle Large Rigid Truck (3 axle 10 tyres) Articulated Truck (4 axle) B Double Grade Figure 4. 4 Fuel consumption versus grade Figure 4.4 shows the effect of grade on fuel consumption for vehicles travelling on straight road section at 35km/h, NRM of 100counts/km and a volume to capacity ratio of 0.5, based on the output of NIMPAC style model. Figure 4.4 shows that the effect of grade in fuel consumption increases with increasing grade and the effect is higher for heavy vehicles. This is discreet as one of the forces to overcome along the motion line would be the product of grade (sine of the angle) and mass of the vehicle. Hence rise in either of them would result in more fuel consumption. The slope of the line representing light vehicles is expected to be less as the product of those factors is less for such vehicles. 53

70 Curvature Adjustment Horizontal curvatures are classified as per the design speed (km/hr) which would resemble curves such as very curvy, curvy, less curvy and almost straight. Vehicle Category Horizontal curvature category Very Curvy Curvy Less curvy Almost straight Corresponding design speed (km/h) Corresponding radius range (metres) (Approx.) Corresponding correction factors L.C.V. and Rigid trucks Combination Vehicles Table 4. 1 Horizontal curvature adjustment factor The user needs to select curvature from the listed group. In order to apply these factors, details of the proportions of total road length applying to each of the four curve categories are required, with the final curvature correction factor being calculated in terms of the weighted average. As an alternative, road sections are to be categorized homogeneously according to the curvature type so as to induce a predetermined curvature correction as mentioned in Table 4.1. The effect of curvature via speed on fuel performance of vehicle has not been dealt in this model. Congestion adjustment In addition to reducing average vehicle speeds, congestion also results also in increased speed variation and associated acceleration and deceleration patterns. These variations from the steady speed driving pattern, if pronounced, may result in significant additional fuel use. Thoresen (2003) reported congestion impacts on fuel use can be direct, in terms of adjusting the basic fuel use relationship for congestion effects, and indirect, through congestion effects on speed. The congestion effect on speed is not discussed here and is open for user input. In the NIMPAC style model, the congestion impacts on fuel use is estimated using an adjustment factor obtained by multiplying the Volume to Capacity Ratio (VCR) by a 54

71 variable, FCONG, which adjusts fuel use to a level associated with a VCR of unity. This is the maximum value applicable, and there is no further adjustment when values of VCR higher than unity are applicable. Values of FCONG applicable to individual vehicle types are shown in Table 4.2 which has been adopted from the NIMPAC model. Table 4. 2 Traffic Congestion Adjustments to Fuel Consumption (FCONG) Source: Thoresen (2003) Congestion adjustment = MIN (1, VCR) * FCONG The maximum possible congestion adjustment based on VCR is the FCONG value for the vehicle type. Figure 4.5 shows the graphical representation of variation of fuel consumption with respect to congestion based on the output of NIMPAC style model. The graph is drawn for straight and flat road section with NRM of 100counts/km and travel speed of 60km/h. Specific Fuel Consumption (litres per 1000 km) Fuel cosumption VS Congestion Light Vehicles Heavy Vehicle Figure VCR Fuel consumption versus congestion The effect of difference in traffic congestion adjustment factor for heavy and light vehicle is depicted in Figure 4.5 by the difference in the slope of two lines. Figure 4.5 shows the linear variation of fuel consumption with volume to congestion ratio (VCR) till VCR reaches 1. As expected, VCR of 1 or above results in long queue spillbacks 55

72 at intersections and delays along the route. The impact of VCR above 1 on fuel consumption could well be reflected by VCR of 1, as both represent a very congested condition. Greater impact of congestion on heavy vehicle could be the representation of more idling and stop/start fuel demand for heavy vehicle compared to light and also the high fuel demand at low speed for heavy vehicle compared to light. Roughness Adjustment Road roughness has been divided into five levels ranging from very good to very poor. The division is based on Thoresen and Roper (1996). Table 4. 3 Classification of road section based on roughness Source: Thoresen and Roper (1996) NIMPAC models calculate a pavement condition cost factor, GCGFAC, which is combined with another factor, FCGRVF, in order to derive appropriate roughness fuel consumption adjustment factors. GCGFAC values are common to all vehicle categories, whereas FCGRVF is sensitive to speed and vehicle category. GCFGAC = where: GCGFAC CFSMAX CSENSP NRMA CFSMAX Minimum Eq. 4.4 CSENSP * ( CNRM PAVC) /( NRMA PAVC) = Pavement condition cost factor = Maximum cost factor (fuel and tyres) for surfaced roads = Cost sensitivity for surfaced roads = Coefficient of the PSR to NRM conversion ratio PAVC = Minimum roughness of road section after construction/reconstruction CNRM = Current road roughness in NRM counts per kilometre As variations in the values of model variables in the above equations can cause differences in fuel roughness adjustments between models, Thoresen and Roper (1996) recommended that these be set as follows in order to harmonise resulting estimates: Values for CFSMAX and CSENSP should be set at 1.75, PAVC be 56

73 assigned a value of 50 (NRM counts per km), and NRMA should be assigned a value of 250 (NRM counts per km). Thoresen (2003) presented a revised lookup table for FCGRVF. Appendix D gives an overview of FCGRVF lookup table which is used here to estimate the roughness impact on fuel consumption. The final roughness correction factor is then the product of FCGRVF and GCGFAC, (Thoresen and Roper, 1996) which makes roughness adjustment a function of vehicle, speed and road surface parameters. Specific Fuel Consumption (litres per 1000 km) Figure Fuel cosumption VS Road Roughness NRM Counts/km Fuel consumption versus road roughness Utility Vehicle Large Rigid Truck (3 axle 10 tyres) Articulated Truck (4 axle) B Double Figure 4.6 portrays the variation in specific fuel consumption as per NRM counts/km for different vehicles travelling at 65km/h and 0.5 volumes to capacity congestion on a straight and flat road section based on output of NIMPAC style model. Sensitivity of road roughness on fuel consumption varies with vehicle type. The effect of variation in NRM counts per km on specific fuel consumption is greater for heavier vehicles, as shown in Figure 4.6. This difference is prudent since the balancing vertical component of the forces would be the function of roughness coefficient (which is denoted by NRM counts here) and mass of the vehicle. Hence the greater the mass of the vehicle/roughness value, the higher would be the energy required to overcome the friction due to roughness. 57

74 4.3.4 Summary for road Table 4.4 shows the minimum and maximum effect of each adjustment factor. Other circumstance, such as use of the parameter for the portion of total run also play an important role in total energy estimation, along with the absolute maximum and minimum mentioned in Table 4.4. Adjustment Factor Affected by Min. effect Max. effect Engine efficiency Type of vehicle 7% 10% Gradient Grade, speed and vehicle type 0% 123% Curvature Curve and vehicle type 0% 20% Congestion VCR and vehicle type 0% 40% Roughness Road surface, speed and vehicle type 0% 48%* *48% for poor road surface (NRM/km = 250), affect of the factor would rise as NRM counts increase. Table 4. 4 Adjustment factors Vehicle simulator The Design Pro software (refer section 4.2.5) was used to determine the effect of payload on vehicle energy consumption. Several simulation runs were performed. Figure 4.7 run. portrays the results in graphical form for a typical B-Double simulated 450 Fuel consumption vs Gross Vehicle Mass (GVM) lt/1000km kmph 80kmph 89kmph 97kmph 105kmph 0 Figure GVM Effect of Gross Vehicle Mass in Energy consumption 58

75 A linear increase in the energy consumption with the increase in Gross Vehicle Mass (GVM) was obtained. This could be explained with linear increment in inertial energy demand to propel the vehicle with the increase in mass of an object. The slope of the lines in Figure 4.7 is almost constant (approx or ). Payload term could be used more effectively than GVM, especially in case where amount of freight being moved is of prime importance. Assuming a constant tare weight of a typical B-Double as 19.5 ton and total GVM capacity as 53 ton, the above relationship could be changed in terms of payload and energy consumption. Fuel consumption (lt/1000 km) Figure 4. 8 Payload vs Fuel consumption (B Double) Payload 72 kmph 80 kmph 89 kmph 97 kmph 105 kmph Relationships between payload and energy consumption The linear curves fitting in above points would give the correlation coefficients of more than 0.9 and the relationships of the form: Fuel Consumption ( lt /1000 km) 210 Payload + C The high slope of the lines (around 210) indicates that fuel consumption is very sensitive to payload factor. Hence inclusion of payload term, in NIMPAC style model, to fit the purpose of energy quantification is essential. * Design Pro is a vehicle run simulator proprietary to Caterpillar Inc., Peoria, Illinois, USA. Design Pro is expected to best specify the Cat engine and the best drivetrain for any application with manufacturer specific product information. 59

76 The variation in the speed did not show a high fluctuation in slope of the lines. However, as expected the lines are shifted up for every increase in the speed corresponding to the higher energy demand for higher speed. 4.4 Rail transport sub-model Due to the unavailability of rail fuel consumption data and high degree of uncertainty involved in the use of average MJ/NTK and MJ/GTK values, the rail transport submodel is developed based on the existing practices reviewed in the literature and personal communication with Dr. Peter Pudney and Prof. Phil Laird. Rail energy consumption can be estimated based on the equation of motion taking the train a point mass moving along a smooth track under the influence of an applied force: m dv / dt = F( v, u) R( v) + T( x) Where: m is the mass of the train; v is the speed of the train; F is the tractive force produced at the wheels; u is the control setting; R(v) is the resistive force acting on the train; and T is track force, due to gradient and curvature, acting on the train and x is the location of the train. Because of the inertia of rotating parts, the effective mass of the train is slightly greater than the actual mass. The difference between actual mass and effective mass is small, particularly for long-haul trains, and can be ignored. Tractive force The tractive force required to maintain a constant speed v is: F = R(v) - T(x). The associated tractive power at the wheels is: P = v [R(v) - T(x)] Eq. 4.5 Resistive force Resistance acceleration is usually modelled as a quadratic function of speed. 2 R ( v) = r + ( r v) + ( r ) Eq v 60

77 The coefficients r 0, r 1 and r 2 are particularly difficult to estimate, but will generally increase with the length and mass of the train. AREMA Manual for Railway Engineering has tabulated predominate but not exclusive contributors to the coefficients (r 0, r 1 and r 2 ). Table 4. 5 Coefficient contributors Source: AREMA (1990) The following formulae are based on work done by Lukaszewicz (2001): r r = (65 Number of axles) + ( Mass of the train) r = 22 + (0.58 Length of the train) = 5 + (0.08 Length of the train) Eq. 4.7 The coefficients were derived for ordinary freight trains of mixed consist on a straight rail track in Sweden. Track force The force due to the track can be modelled as T ( x) = G( x) C( x) Eq. 4.8 where G is the gradient force acting on the train, and C is the force acting against the train due to the curvature of the track. Gradient force is positive on declines and is given by: G( x) = m g Sin( θ ( x)) Eq. 4.9 where θ is the angle of slope of the track and g is the acceleration due to gravity (9.8m/sec 2 ). The curvature force is usually assumed to be independent of speed. Resistance due to curvature has been widely used as 0.8 lb/ton per degree of curvature (AREMA 61

78 1990), where degree of curvature is the change in bearing on a curve with a 100 foot chord. For other than standard gauge track, the following relationship was proposed: Rc = 0.17 Gauge in feet where Rc is the curve resistance in lb/ton per degree of curvature. The width of the narrow, standard and board gauges are shown in Figure 4.9 which would enhance the understanding of the proposed curvature penalty. Figure 4. 9 Gauge width dimension Taking the curvature penalty as 0.8 lb/ton for a degree of curvature for a standard gauge track and using SI units, the force acting against the train on a curve with radius r(x) is 6.33 mass C( x) = r( x) The ratio proposed in AREMA (1990) was used for determining the curvature penalty for various gauge width track. Hence curvature penalty would be; 6.33 mass C ( x) = ( Ratio based on the width of the track) r( x) Eq where the ratio would be 1 if the track is standard gauge (4 feet 8.5 inch) and 1.11 if the track is board gauge (5 feet 3 inch). In addition, AREMA (1990) recommended a proportional reduction in curve compensation in presence of wayside rail lubrication and/or improved wagons and track. 62

79 Combination of equations from Eq. 4.5 to Eq gives the power required to maintain a constant speed v on a track with constant gradient and curvature. Fuel flow rate Taking into account the efficiency of the traction system and the fuel consumption of the diesel generator, the rate of fuel consumption can also be estimated based on Power calculated from the combination of Eq. 4.5 to Eq The Specific Fuel Consumption (SFC) of diesel engine plays an important role in predicting the amount of fuel being used to generate the required energy. SFC is dependent on the engine design and particularly sensitive to compression ratio. Thus, any change in specific fuel consumption of diesel generator would impact the fuel consumption estimation. The developed spreadsheet tool allows the users to overwrite the default value. However, the difference in the SFC between different engines tends to be quite small. Specific fuel consumption of diesel generator at full power when installed in locomotive was taken as 0.23kg/kWh; converting to SI units gives kg/j. The specific gravity of diesel fuel is 0.83, and so the volumetric fuel consumption is litres/j, and the fuel flow rate will be (litres/s)/w. When the power required in maintaining a constant speed is P (in watts), the corresponding fuel flow rate will be: Fuel Demand = P Duration / η Eq where η is the efficiency of the electric traction system which vary depending on the engine used and the track speed of the locomotive. According to AREMA (1990), the efficiency of diesel-electric locomotives would be in between 80% to 85%. 63

80 Idling power of the locomotive Lukaszewicz (2001) provided an empirical mean value (66 kw) originating from idling and coasting of freight trains. Converting the values to SI units, it would be Joules/Sec which is the value adopted in this study. Braking and Accelerating energy Braking is relatively difficult to model due to the uncertainty in type of brake used. In particular, mechanical braking is used to supplement dynamic (electrical) braking at low speed (Howlett and Pudney 1995). In the model, the rate of fuel supplied was assumed to be zero during braking. However, in practice a low notch setting is often used to operate the electrical brakes (Howlett and Pudney 1995). However, the precise nature of braking was not considered in overall fuel estimation. The braking at any stage of the journey might necessitate excessive application of power at some other stage to accelerate the vehicle. The combination of equations (Eq. 4.5 to Eq 4.10) would give the energy needed to run the train in a constant speed. However, additional energy is required for a train to accelerate. The tractive effort needed in each step to overcome resistance and acceleration can be estimated as described in Eq The latter is based on Rochard and Schmid (2000) and the assumption that coefficient for rotating masses (including wheels, shafts and axles) is almost equal to unity and can be ignored, particularly for longhaul trains. Tractive Effort = acceleration * mass + Resistance Eq The power required at each step would be the product of tractive effort and speed at that step. Based on this power, the fuel demand for accelerating could be estimated using Eq and Eq

81 However, this process requires the power (resulting to fuel flow rate) estimation for every instant. The iterative work involved was excluded in this study by considering the accelerating section as the speed holding section with average speed. The change in energy demand due to differing type of train movement is explained below. For instance, if the train of 2864 tonnes is to come to 60km/hr speed from rest in 10 minutes (acceleration m/sec 2 ; distance 5.04 km), the fuel demand would be 31.1 litres (based on Eq and assuming the efficiency to be 1). However, if the same movement is assumed to be under constant average speed of 30km/h then the model would give the result to be 15.5 litres. Hence the effect of change in speed is prominent and highlights the importance of driver behaviour. In the case study (Chapter VI), the fluctuation in the speed has not been taken into consideration because of the high degree of uncertainty in the speed profile of the considered options. Since in all the options (including road), the energy demand for the change in the velocity is not considered, the result of the comparative study is not expected to alter by a significant amount. In addition, when the section is significantly long, the energy required to accelerate the train would only made up a small section of the total energy demand. Hence in such cases, which are what the tool is directed for, a prudent result could be expected. 4.5 Additional transport process sub-model Intermodal transfer energy The amount of energy required to transfer freight from one mode to another is grouped in the energy demand of intermodal transfer. This energy demand depends upon various factors such as: Intermodal transfer platform area; Handling equipments in use; Mass of the freight; Size and number of containers; and Management. 65

82 Andersen et al (2001) reported the energy efficiency of goods handling in a transfer station. The energy efficiency reflects the data gathered from six port operators grouped by different loading ways. Table 4.6 shows the values which are used to estimate the intermodal transfer energy. Type Energy Efficiency (kwh/tonne) Energy Efficiency (MJ/tonne) Bulk Average Source: Andersen et al (2001) Table 4. 6 Intermodal transfer energy Shunting energy Shunting process also requires additional energy. Shunting is mainly carried out using diesel locomotives. Typical energy values for shunting that IFEU and SGKV (2002) used is 0.03 kg diesel fuel per gross tonne. In literature, a considerable difference in the typical shunting values could be found. For instance, Andersen et al (2001) found that two diesel locomotives (operated in two shifts, 16 hrs/day/engine) would use 0.35 litres fuel per net tonne as a shunting energy demand. IFEU and SGKV (2002) recommended that the significance of shunting energy demand is less while analysing the corridor level energy gain. Hence, even with the considerable variation in the reported shunting energy, an arbitrary value proposed by IFEU and SGKV (2002) is considered in this study with the conversion factor of 38.6 MJ/litre and specific gravity of In case of access to more reliable value by the user, the tool allows the user to replace the default value. Energy Efficiency kg/ gross tonnes lt/ gross tonnes MJ/ gross tonnes Shunting processes Table 4. 7 Shunting energy demand 4.6 Spreadsheet model platform Section 4.2 to 4.5 discussed the development three sub-models needed for a comprehensive analysis of energy advantage of various modes and options involved. For ease in use of such models especially with the combination, a spreadsheet tool 66

83 was developed. This section briefly describes the three distinct sections of the spreadsheet tool namely input, computation and output. Appendix E contains more elaborative description and discusses how to operate the tool. Appendix F contains a CD which has the spreadsheet tool developed as a part of this study. The spreadsheet has nine sheets namely: input freight characteristics, input road, input rail, vehicle characteristics, lookup tables, calculation, output road, output rail and summary table. The interrelationships between the sheets is summarised in Figure Helps in identifying the freight task Informs users about the size of containers and number of trips required Lookup tables Input Sheet Freight characteristics Input road Estimating energy required for road movement section including pick up and delivery Estimating energy required for rail movement section including road pick up and delivery Input rail Output sheet Road Rail Summary sheet (Comparison) Figure Flow diagram of the comparison spreadsheet tool The Input Freight Characteristics sheet allows the user to define, and later identify, the freight characteristics such as type of freight, size of freight and type of commodity. In some cases quantifying the energy used in terms of MJ per tonne-km would not totally describe other various aspects of freight task (BT 1995). The major deficiency of the measurement is the inability to deal with the volume of the task, which would govern the number of containers and trips ultimately affecting the final energy consumption. These parameters may be tallied at first so the user is better 67

84 informed about the number of containers required to carry the commodity and trips generated for the task. The main aim of this sheet is to make an allowance for such judgement by informing users about the available volume and freight volume. The Input Road sheet allows user to input the freight movement characteristics of the pickup, road line haul and delivery section. The sheet contains space to input 15 pickup and delivery legs at once. Each pickup/delivery leg description has 5 rows. Each row allows segregation based on traffic and terrain characteristics of freight task. Road line haul section has three segments with fifteen rows in each segment. Each of those rows allows segregation based on traffic and terrain characteristics of freight. Three segments separated here allow three different vehicles of the same freight fleet to be considered at once for energy consumption comparison. Repeated run of the spreadsheet tool is necessary to encompass the energy performance of more number of vehicles on the fleet (more than three, if any) at once. Figure Input rail sheet 68

85 Similarly the input rail sheet provides the user to input the freight movement characteristics involving road for pickup and delivery, and rail for line haul movement. The screenshot of Input Road Sheet is shown in Figure Lookup table and calculation sheets quantify the adjustment factors based on tabulated values and formulae based on section 4.2 to The output sheets present the result after the computation. The road and rail output sheets present the energy demand for travelling each segment of road or/and rail and for each activity. Figure 4.12 shows a sample Output (Road) sheet. The summary table sheet compares the energy required for pickup, line haul and delivery legs for options mentioned on input road sheet and input rail sheet to depict the overall modal freight energy. The screenshot of summary table is shown in Figure Output Sheet (ROAD) #VALUE! Identification code Origin rahs Option code Destination jaejr 0 Type of packing Other freight - Unitised Type of commodity Chemical related products not elsewhere specified Pick Up Section Operating Characteristics Road Efficiency Length Speed Payload Congestion Congestion Grade Curvature Roughness Roughness Fuel Section Vehicle Adjustment (km) (kmph) Payload Factor (VCR) factor Grade (%) Factor Curvature Factor (NRM/km) Factor consumption Start point End point PU01 B Double PU02 B Double PU03 B Double PU04 B Double PU05 B Double PU06 B Double PU07 B Double PU08 B Double PU09 B Double PU10 B Double PU11 B Double PU12 B Double PU13 B Double PU14 B Double PU15 B Double PU16 B Double PU17 B Double PU18 B Double PU19 B Double PU20 B Double PU21 B Double PU22 B Double PU23 B Double PU24 B Double PU25 B Double PU26 B Double PU27 B Double PU28 B Double PU29 B Double PU30 B Double PU31 B Double PU32 B Double PU33 B Double PU34 B Double PU35 B Double PU36 B Double PU37 B Double PU38 B Double PU39 B Double PU40 B Double PU41 B Double PU42 B Double PU43 B Double PU44 B Double PU45 B Double Operating 0 0 0Characteristics PU46 B Double PU47 B Double PU48 B Double PU49 B Double PU50 B Double PU51 B Double PU52 B Double PU53 B Double PU54 B Double PU55 B Double PU56 B Double PU57 B Double PU58 B Double PU59 B Double PU60 B Double PU61 B Double PU62 B Double PU63 B Double PU64 B Double PU65 B Double PU66 B Double PU67 B Double PU68 B Double PU69 B Double PU70 B Double PU71 B Double PU72 B Double PU73 B Double PU74 B Double PU75 B Double Road line haul section 0 Operating Characteristics Road Efficiency Length Speed Payload Congestion Congestion Grade Curvature Roughness Roughness Fuel Section Vehicle Adjustment (km) (kmph) Payload Factor (VCR) factor Grade (%) Factor Curvature Factor (NRM/km) Factor consumption Start point End point RoLH01 B Double RoLH02 B Double RoLH03 B Double This section is for one set RoLH04 B Double of vehicle in the fleet. RoLH05 B Double However input does allow RoLH06 B Double the user to change the RoLH07 B Double RoLH08 B Double type of vehicle as per the RoLH09 B Double section in the case where RoLH10 B Double the vehicle in the fleet are RoLH11 B Double stopped at some point and RoLH12 B Double freight is loaded into RoLH13 B Double another vehicle. RoLH14 B Double RoLH15 B Double Figure Output Road Sheet 69

86 Figure Summary sheet 4.7 Summary This chapter discussed the use of some existing models and some previous recommended values to estimate the corridor level energy consumption. This chapter also highlighted the development of a spreadsheet comparison tool. The chapter proposed some amendments in the existing models to compare the vehicles and corridor options based on energy performance. The proposed models, on which there are some amendments to fit the requirements are: NIMPAC Style model Davis Formula updated by Lukaszewicz (2001) For further enhancement in the confidence level of the model, it is recommended to verify the models with on track testing techniques such as coasting down and dynamometer testing. 70

87 CHAPTER V SENSITIVITY ANALYSIS 5.1 Introduction Sensitivity testing of parameters can add greatly to the validity of an energy estimation model. Here the parameter sensitivity tests are also used as validating tool by confirming whether a small perturbation to a parameter s numerical value results in a significant change in the model s behaviour. The results of these tests can indicate the level of accuracy that is required when assigning numerical values to a model s parameters. It can be impractical to run a sensitivity analysis for every possible value because of the limitless possibilities to be simulated. A simple and straightforward process for analysing the sensitivity of an energy consumption model is carried out in this chapter. Sensitivity tests are performed on each model parameter discretely. This chapter discusses the likely error ranges associated with the output of the developed model when certain plausible assumptions are made about the measurement errors of the various independent variables. The chapter also helps to better understand the relationships between the parameters influencing energy consumption and the relative importance of those parameters in energy estimation. 5.2 Model Errors The search for models which more accurately represent complex situations and interactions is worthwhile. However, it is not possible to model every complex situation in a simple model. This deficiency of a model is evident through the output error. Richardson (2001) mentioned three types of errors associated with models. The first type of error is the inability of the model to completely represent a given situation, which is known as specification error. The second type is the error that arises through poor input data which is known as measurement error. Hence measurement error is the property of data and cannot be significantly reduced in the modelling process, 71

88 with the exception of model propagation (i.e. the means by which a model magnifies or diminishes errors in different variables). The third type is sampling error which indicates the extent to which results vary across different samples of same population. The sampling error can be reduced by taking a larger sample. The total output error of any model results from the combination of specification and measurement errors. Intuitively the curve of specification error would slope downward asymptotically with the increased complexity of model, whereas the measurement error would increase with an increase in complexity of the model as shown in Figure 5.1. Richardson (2001) mentioned that a more complex model will reduce the specification error. However, it will also increase the chances of measurement error. At some point, the inclusion of more variables into the model will increase the measurement error more than it will reduce the specification error. This trade-off between specification error and measurement error can be further demonstrated by considering the use of a dataset which has a higher degree of measurement error, as represented by e meas curve in Figure 5.1. Under these conditions, the measurement error will be higher at all levels of model complexity, as will be the total error, as shown in Figure 5.1. The complexity is defined as being measured by the number and structure of relevant explanatory variables included in the model. Figure 5. 1 Error versus Complexity Source: Richardson (2001) 72

89 5.3 Errors and uncertainty in road energy estimation Background The road sub-model proposed in Chapter IV is tested for its sensitivity of adjustment factors such as grade, roughness, payload, speed and curvature. The sensitivity of the model estimation coefficients was scrutinized. Effect of changes in input values, such as speed of 70 km/h instead of 75 km/h, was discussed in Chapter IV. This chapter deals with the effect of change in value of the correction factor on energy estimation, rather than the direct effect of alteration in input parameters such as change in speed, roughness or grade. This chapter deals with the change in energy estimation for the same speed (say 70 km/h) due to change in the estimation coefficients. A simplified relationship between the energy influencing parameters is reinstated below: (see Eq. 4.2) FuelConsumption = Grade Payload 2 Correction Curvature ( A + B / v + C v ) correction 1+ such as Eq.5. 1 Factors Congestion factor Roughness As discussed previously, the remaining energy influencing parameters are fixed for a sensitivity testing of single parameter. Table 5.1 shows the details of those values and the parameters. Parameters Roughness Speed Grade Curvature Congestion Sensitivity (NRM/km) (km/h) (%) (Volume/capacity) Roughness coefficients Nil Nil 0.3 Speed coefficients Nil Nil 0.3 Grade coefficients and 35 2,4,8 Nil 0.3 Curvature coefficients and 65 Nil 0.3 Congestion coefficients Nil Nil 0.3 and 1 Payload Good Asphalt 72 to 113 Nil Nil Unknown Table 5. 1 Constant values taken for sensitivity analysis of various parameters The length of the road section is not expected to alter the sensitivity result significantly. However, the length considered for the sensitivity analysis was 1000 km. For the consistency in testing, the same vehicle types were selected for each sensitivity testing. The four different types of representative vehicles selected are: B-Double Articulated 4 Axles Truck 73

90 Rigid 3 Axles Truck Utility Truck The details of the vehicles are given in Appendix B Roughness sensitivity The overall sensitivity of roughness with fuel consumption was discussed in section This section deals with the sensitivity of the coefficients of the roughness correction factor (see Eq. 5.1). The energy influencing parameters were fixed for the sensitivity testing of the roughness parameter. Table 5.1 shows the constant values being used in the sensitivity testing. Figure 5.2 shows the result of the roughness sensitivity analysis at a NRM roughness count of 100 per km. Change in Fuel Consumption (%) Roughness sensitivity B Double Articulated 4 axle Rigid Truck 3 axles Utility Vehicle Alteration in Adjustment Factor (%) Figure 5. 2 Roughness sensitivity A change of 20% in the roughness adjustment factor would bring a corresponding change of about 0.7% in fuel consumption for B-Doubles and about 0.37% for Utility vehicles. As expected, the effect increases for heavier vehicle and similarly with high NRM value. The effect of alteration in roughness adjustment factor did not result in a very significant change in fuel consumption Speed coefficients and speed sensitivity This section deals with the sensitivity of the coefficients of basic speed fuel relationships (Eq. 4.2). The alteration of all three speed coefficients simultaneously by an equal amount would be reflected on energy consumption with the change in same magnitude, hence showing a one to one relationship. 74

91 The energy influencing parameters were fixed for the sensitivity testing of the roughness parameter. Table 5.1 shows the constant values being used in the sensitivity testing. Figure 5.3 a, b and c show the result of the speed sensitivity analysis of three different speed coefficients (A, B and C) mentioned in Eq Change in fuel consumption (%) Speed sensitivity (constant term variation) B-Double Articulated 4 axles Rigid 3 axles Utility Vehicle Alteration in Speed coefficient (%) Figure 5. 3a Speed sensitivity (constant coefficient variation, A) Figure 5.3a and Figure 5.3b show that the effect of the alteration in constant coefficient and reciprocal coefficient of the basic speed fuel relationship (first term, Eq. 5.1) would have higher impact on energy consumption of heavier vehicles compared to light. The exception to this is the Utility Vehicle while sensitivity testing of two coefficients namely, A and C. The Utility vehicle was not showing a consistent trend, which might be the effect of extreme lightness of the vehicle compared to the remaining three. Furthermore, the effect of constant and reciprocal coefficient alteration is quite prominent on energy consumption which is represented by the line slope greater than

92 Change in fuel consumption (%) Speed sensitivity (reciprocal term variation) B-Double Articulated 4 axles Rigid 3 axles Utility Vehicle Alteration in Speed coefficient (%) Figure 5.3b Speed sensitivity (reciprocal coefficient variation, B) Change in fuel consumption (%) Speed sensitivity (square term variation) B-Double Articulated 4 axles Rigid 3 axles Utility Vehicle Alteration in Speed coefficient (%) Figure 5.3c Speed sensitivity (square coefficient variation, C) Figure 5.3c shows that the effect of the alteration in square coefficient of the basic speed fuel relationship (Eq. 5.1) would have higher impact on energy consumption of lighter vehicles compared to heavy. C v 2 is expected to cover the resistance of aerodynamic drag. Hence, the square coefficient (C) depends on aerodynamics of the vehicle. Hence, it is prudent to assume that for a small vehicle change in aerodynamics would have a higher impact on percentage of fuel used. Same as other speed coefficients, the effect of square coefficient alteration is also quite prominent on energy consumption which is represented by the line slope between 0.17 and Grade sensitivity 76

93 The overall sensitivity of grade with fuel consumption was discussed in section This section deals with the sensitivity of the coefficients of the grade correction factor (see Eq. 5.1). The energy influencing parameters were fixed for the sensitivity testing of the grade parameter. Table 5.1 shows the constant values being used in the sensitivity testing. Figure 5.4 a, b and c show the result of the grade sensitivity analysis at 2%, 4% and 8% gradient. The figures portray that the grade sensitivity is higher for heavier vehicles and higher grades. Change in energy estimation (%) Grade sensitivity at 2% B-Double Articulated 4 axle Rigid 3 axle Utility Alteration in adjustment factor (%) Figure 5. 4a Grade sensitivity at 2% gradient Change in energy estimation (%) B-Double Articulated 4 axle Rigid 3 axle Utility Grade sensitivity at 4% Alteration in adjustment factor (%) Figure 5.4b Grade sensitivity at 4% gradient 77

94 Change in energy estimation (%) Grade sensitivity at 8% B-Double Articulated 4 axle Rigid 3 axle Utility Alteration in adjustm ent factor (%) Figure 5.4c Grade sensitivity at 8% gradient Curvature sensitivity The overall sensitivity of curvature regarding fuel consumption was discussed in section This section deals with the sensitivity of the coefficients of the curvature correction factor (see Eq. 5.1). The energy influencing parameters were fixed for the sensitivity testing of the grade parameter. Table 5.1 shows the constant values being used in the sensitivity testing. Since the speed is a curvature dependent factor, the speed is varied for different curvature sensitivity testing. Figure 5.5a shows the curvature sensitivity for very curvy road where the limiting speed is 30 km/h and Figure 5.5b shows the curvature sensitivity for less curvy road where the limiting speed is 65 km/h. Change in energy cionsumption (%) Horizontal curvature sensitivity (Very curvy section) Utility Vehicle Rigid Truck Articulated Truck B Double 0 Figure 5. 5a Alteration in adjustment factor (%) Curvature sensitivity for very curvy section 78

95 Horizontal curvature sensitivity (Less curvy section) Change in energy cionsumption (%) Utility Vehicle Rigid Truck Articulated Truck B Double Alteration in adjustment factor (%) Figure 5.5b Curvature sensitivity for less curvy section The horizontal curve sensitivity rose as the degree of curvature decreases. This is prudent as there is already a high degree of penalty for very curvy road so the small change in fuel consumption would not make a huge difference in the energy estimation. Moreover, as the curve is easier, the sensitivity of heavy and light vehicle starts to separate whereas for relatively sharp curvature the degree of sensitivity for heavy and light vehicles are almost same. The curvature would make up only a small segment of total road being travelled in most of the freight corridors. Hence during the comparison process of the energy consumption, the effect of alteration in curvature correction factor is not expected to make a huge difference. 79

96 5.3.6 Congestion sensitivity The overall sensitivity of congestion with fuel consumption was discussed in section This section deals with the sensitivity of the coefficients of the curvature correction factor (see Eq. 5.1). The energy influencing parameters were fixed for the sensitivity testing of the congestion parameter. Table 5.1 shows the constant values being used in the sensitivity testing. Congestion sensitivity is carried out in the low congestion level and high congestion level represented by Volume to Capacity Ratio (VCR) of 0.3 and 1 respectively. Change in energy estimation (%) Congestion sensitivity at 0.3 VCR B-Double Articulated 4 axle Rigid 3 axle Utility Alteration in adjustment factor (%) Figure 5. 6a Congestion sensitivity at light traffic section Change in energy estimation (%) Congestion sensitivity at 1 VCR B-Double Articulated 4 axle Rigid 3 axle Utility Alteration in adjustment factor (%) Figure 5.6 b Congestion sensitivity at heavy traffic section 80

97 Figure 5.6 a and b portray that the sensitivity degree of congestion coefficient is high for heavy commercial vehicles and low for light duty vehicles. In addition, the degree of congestion sensitivity would be more for highly congested road Payload sensitivity The payload sensitivity is very important in terms of freight modal energy estimation and comparison. Design Pro* vehicle run simulator suggested a linear relationship between payload and fuel consumption with an average slope of the line between 165 and 175. This relationship between gross vehicle mass and fuel consumption, derived from Design Pro* simulation, is used for the energy estimation. Appendix G contains a sample data set used for deriving the relationship. The payload sensitivity for B-Doubles was carried out by altering the slope of the line. The alteration has an effect in the ratio of 1:2 max (20% change in slope of the line would effect the fuel consumption by 10%). Hence, this shows that payload is also an important parameter influencing the energy estimation Sensitivity summary of road sub-model The sensitivity study of road sub-model parameters suggested that the error in estimation coefficients would affect the fuel consumption estimation in the ratio of 1:2 maximum (the error in speed coefficient by 20% would affect the energy estimation by 10%). This maximum ratio is for error in speed coefficients and payload slope. The next highest impact is from error in grade coefficient which is in the range of 1:3.5 (the error in grade coefficient by 35% would affect the energy estimation by 10%). The sensitivity analysis carried out above suggested the following order for the sensitivity of the road sub-model parameters; i. Speed coefficients and Payload ii. Grade coefficients iii. Congestion coefficients; and iv. Curvature and Roughness coefficients. * Design Pro is a vehicle run simulator proprietary to Caterpillar Inc., Peoria, Illinois, USA. They advocate that Design Pro would best specify the Cat engine and the best drivetrain for any application with manufacturer specific product information. 81

98 The curvature and roughness had shown almost the same magnitude of sensitivity. Hence, the above discussion depicts that speed coefficients and payload slope are the most important factor in energy estimation model. Moreover, these parameters would be in use for the entire movement of the freight. Hence, any errors in these terms are expected to bring high degree of uncertainty in energy estimation. The remaining factors do not have high impact on energy estimation process. Furthermore, these parameters (except roughness) would only be affecting a short portion of freight movement corridor. Hence, the final comparison result would experience very small effect of errors in the coefficients of these parameters. Parameters Change in Parameter (%) Change in Energy consumption (%) Speed Payload Grade Congestion Curvature Roughness Table 5. 2 Sensitivity summary of various parameters 5.4 Errors and uncertainty in rail energy estimation Background The rail sub-model proposed in Chapter IV is tested for its sensitivity of Train length Train mass Train Speed Grade; and Curvature Number of Locomotives and wagons The sensitivity of the model estimation coefficients was also scrutinized. A simplified relationship between the energy influencing parameters is reinstated below: FuelConsumption = Specific fuel consumption of diesel generator Efficiency 2 [ Speed [( r + r speed + r speed ) [ Grade force Curvature force ] Eq

99 The remaining energy influencing parameters are fixed for a sensitivity testing of single parameter. Table 5.3 shows the details of those values and the parameters. Parameters Sensitivity Length (m) Mass (tonnes) Grade (%) Curvature (metres) Speed (km/h) No. of Loco. No. of Wag. Train Length (metres) 3200 Nil Nil 80 Train Mass (Tonnes) 900 Nil Nil Grade (%) Nil Curvature (metres) Nil Speed (km/h) Nil Nil 1 49 Number of Loco Nil Nil Number of Wagon Nil Nil 80 1 Table 5. 3 Constant values taken for sensitivity analysis of various parameters The following sections (5.4.2 to 5.4.7) discuss the rail sub-model s parameters. It shows what would be the corresponding fluctuation in the model energy estimation for a change input parameters values. For in depth understanding of the model estimation, the sections also discusses the effects of constant coefficients alteration in energy estimation Train Length Train length is used to quantify the resistive coefficients such as r 1 and r 2 (see Eq. 5.2). The parameters are believed to determine the resistive forces caused due to aerodynamics and rolling resistance. The alteration of length from 650 m to 900 metres is portrayed in the Figure 5.7. For the study of train length variation, the rest of the energy influencing parameters are fixed. The fixed values of the parameters are tabulated in Table 5.3. Efficiency (Lt/1000GTK) Train Length Sensitivity Length of Train (m) Figure 5. 7 Effect of variation in Train Length 83

100 Train length is also a function of number of wagons and locomotives. Usually the number of wagons and locomotives determine the number of axles that are presents in the train consists. Since there is axle load limitation, it is not possible for a very short train (with less number of axles) to carry a heavy load. Figure 5.7 shows that with every increase in length of train, there is a decrease in efficiency. However, this figure might not always describe the practical rail world s efficiency. This is because with every increase in train length, a corresponding increase in mass of the train is expected Train Mass Train mass influence energy consumption of train from various angles. Its main influence would be in the rolling resistance estimation and coefficient r 0. If the track forces (grade and curve) are also under consideration then mass has a direct affect on them as well. Figure 5.8 shows the sensitivity of train mass in energy consumption. The values of constant chosen for this sensitivity analysis are shown in Table Train Mass Sensitivity Fuel Efficiency (Lt/ 1000 GTK) Train Mass (Tonnes) Figure 5. 8 Effect of variation in Train Mass Figure 5.8 shows that the efficiency of the movement increases as the mass of the train increases. It depicts that the train mass is a sensitive parameters in describing 84

101 the fuel efficiency of the movement. However, the figure only shows the effect of train mass in energy consumption when train length, number of wagons and locomotives are kept constant. For the range that Figure 5.8 portrays, the assumption might hold true. However, when the mass is further increased then there might be the need of more locomotives and wagons. This is because of the power needed to move the vehicle and axle load limit to be maintained on the track Train Speed Speed affects the fuel consumption by influencing the fuel flow in the engine and aerodynamic resistance and others. In fact, speed has been a prominent parameter in modelling energy consumption since long. Figure 5.9 shows the sensitivity of train speed in energy consumption. It shows that as the speed increases the efficiency decreases. It depicts the change in speed is a sensitive parameter in determining the fuel efficiency of the movement. The values of constant chosen for this sensitivity analysis are shown in Table Train Speed Sensitivity Fuel Efficiency (Lt/1000 GTK) Figure Speed (km/h) Effect of variation in Train Speed Grade and curvature This section discusses the sensitivity of the penalties assigned to route parameters such as grade and curvature. The values of other parameters chosen for the sensitivity study of grade and curvature are given in Table 5.3. As the train would 85

102 usually not run at 80 km/h in high grade and curvature, the speed value was reduced (to 50km/h) for sensitivity study of route parameters. It is believed that the reduced values would more resemble the practical ground. Fuel Efficiency (Lt/ 1000 GTK) Grade Sensitivity Grade (%) Figure Effect of variation in Route Gradient Fuel Efficiency (Lt/1000 GTK) Curvature Sensitivity Curvature Radius (m) Figure Effect of variation in Curvature Radius The sensitivity figures (Figure 5.10 and 5.11) showed distinct characteristics. The variation due to the grade increment is linear whereas variation due to curvature is polynomial (of the form - Constant X -y ). This suggests that curvature parameter is more sensitive as the radius of curvature is less. However, the change in radius from 2800 m to 3000 m is not expected to have significant difference in fuel efficiency. 86

103 5.4.6 Numbers of Wagons and Locomotives Numbers of Wagons and Locomotives have a direct impact on length of the train and number of axles. These parameters play an important role in determining the coefficients such as r 0, r 1 and r 2. Due to this complex relationship between number of locomotives and wagons and train length, the sensitivity of this can not be assessed without further assumption. However, it is possible to study the sensitivity of the number of axles in the energy consumption. Figure 5.12 shows the effect of axle number variation in fuel consumption. As expected, the efficiency of the movement decreases as the frictional forces increases. Number of Axles Sensitivity Fuel Efficiency (Lt/ 1000 GTK) Number of Axles Figure Effect of variation of Number of Axles Sensitivity summary of rail sub-model This section discusses the relative importance of the parameters mentioned in section to The relative importance of parameters are determined by the corresponding change in energy estimation (percentage) induced due to a pre-defined change in percentage of in input parameters. 87

104 Percentage change in Fuel Sensitivity comparison Curvature (at 2000 m Radius) Grade (at 1%) Length (at 650 m) Speed (at 30 km/h) Mass (at 3200 tonnes) Number of Axles (at 101 axles) Percentage change in Parameter Figure Sensitivity Comparison of various parameters The degree of sensitivity was found to be varying with percentage change in parameters. For instance, curvature was most sensitive when percentage change in parameter is more than 80%. Whereas, curvature was less sensitive when change is parameter is less than 20%. When 20% change in parameter was considered as the datum for comparison, the sensitivity analysis carried out above suggested the following order for the sensitivity of the rail sub-model parameters. S.N. Parameters Change in Parameters (%) Change in Fuel Consumption (%) 1 Grade (at 1%) Length ( at 900 m) Speed (at 30 km/h) Mass (at 3200 t) Curvature (at 2000m) Number of axles (at 101 axles) Table 5. 4 Sensitivity Comparison The speed and mass had shown the same magnitude of sensitivity. Moreover, these parameters would be in use for the entire movement of the freight. Hence any errors in these terms are expected to bring high degree of uncertainty in energy estimation. 88

105 Grade showed a high impact on fuel consumption when tested at 1% gradient. Whereas another route parameter (curvature) did not show high sensitivity at 2000m radius. But the curvature sensitivity is expected to increase at low radius values, which is depicted in Figure Train length was also found to alter the fuel consumption estimation significantly. However, as discussed in Section 5.4.2, train length might have a compound effect due to increase in mass and number of axles. Hence, though the number of axles alone did not show significant alteration, but when combined with train length and mass it would be a significant factor. 5.5 Model Complexity and input data This study deals with large set of vehicles, both on road and rail. The data requirement would be high if the complexity of the energy estimation model is increased. Moreover, any increment in the complexity of the energy estimation model would demand a higher quality data to match the output value. The shaded region in Figure 5.1 roughly indicates the working range of the developed model. For a fixed data quality (which is relatively poor), the model complexity can be limited to the simpler level, as shown in Figure 5.1, to obtain a superior output. While it may be difficult to quantify the curves (in Figure 5.1) for the model developed here, the overall implication is clear: using more complex models with bad data simply increases the total error in the model. Sighting the scarcity of adequate set of good quality data and better data error tolerance in simpler model, we resort to the use of simple model for energy consumption estimation. Hence the energy consumption model developed in this study is based on the lower specification measurement parameters. For the model developed, the parameters such as payload, grade, and alignment curve and vehicle type were believed to have lesser measurement errors. Hence these parameters were given the higher specification measurement (compared to other parameters) to improve the model output. This importance was found to be closely matched with the degree of sensitivity of parameters affecting the energy estimation. 89

106 CHAPTER VI CASE STUDY AND MODEL APPLICATION 6.1 Introduction To demonstrate the application and guide the further development of the proposed model, a case study corridor has been selected. The area is selected based on the following criteria: inclusion of both rail and road corridor; presence of different route alignment characteristics such as grade and horizontal curvature; and representation of a realistic freight carrying route. This chapter discusses: the applicability of the developed comparison model in assessing freight movement options based on energy consumed; and the applicability of the model in evaluating a new corridor development project based on the energy savings. 6.2 Site description Background The Warrego Highway, National Highway A2, links Brisbane with Toowoomba, and the Darling Downs. The Warrego Highway is a part of the Brisbane-Darwin corridor. Commencing on Brisbane's western outskirts, the Warrego Highway bypasses the city of Ipswich to the north before heading in a generally western direction to Toowoomba. Just east of Toowoomba is the Great Dividing Range commonly referred as the Toowoomba Range. The highway then crosses through relatively busy city of Toowoomba (population about 105, Census) before turning to a more north-westerly direction crossing the Darling Downs and linking the towns of Oakey, Dalby, Chinchilla, Miles, Roma and Mitchell before terminating at Charleville. 90

107 Most of the Warrego Highway between Brisbane and Toowoomba is 4 lane dual carriageway. Long term planning and route selection has commenced for a bypass of Toowoomba. Toowoomba has a pivotal role in acting as a transport hub for the Darling Downs and beyond and is an important focal point for interstate and intrastate freight movement, being at the confluence of the Warrego, New England and Gore Highways (Maunsell, 1998). This study focuses on analysing the energy consumed in different freight moving options (involving road and rail) through Toowoomba. The arbitrary boundaries to the study area are the junction of Warrego Highway and Paynter Road (east of Toowoomba) and the junction of Warrego Highway and Nass road (west of Toowoomba). Four different options are considered in this study. The options considered are: 1. Existing road route between the junction of Warrego Highway and Paynter road (east of Toowoomba) and junction of Warrego Highway and Nass road (west of Toowoomba). 2. Existing railway line between Warrego Highway, Postman Ridge (east of Toowoomba) and Gowrie junction (west of Toowoomba). 3. Proposed bypass road corridor between the junction of Warrego Highway and Paynter Road and junction of Warrego Highway and Nass road. 4. Proposed new rail line between the junction of Warrego Highway and Paynter Road and junction of Warrego Highway and Nass road. The above options are shown in Figures 6.1 and 6.2. In Figure 6.2, the solid thick line, passing through Postmans Ridge, Harlaxton and Wetalla, represents the new proposed rail route, whereas a thin line almost following the Murphys Creek represents the existing rail line. 91

108 Figure 6. 1 Road options Source: Maunsell (1998) Figure 6. 2 Rail options Source: QR and QT (2003) Option One (Existing Road) A portion between Brisbane to Toowoomba (option involving existing Warrego Highway section between Postman Ridge Road and Nass Road) is considered in this section. The following description is based on the road plans provided by 92

109 Department of Main Roads, Toowoomba district, Toowoomba street index and site visit. Appendix I contains the detail alignment data extracted from the maps provided by DMR, Toowoomba. Alignment description Towards Toowoomba city (East of the city) Paynter Road to Flat Gully (Ironbank Gum Wattle) [approx km] The large portion of the road section has a gradient of around 1.5%. The section also has a large horizontal curve radius (about 6000m) representing a rather straight road section. Flat Gully (Ironbank Gum Wattle) to Connoles Road junction [approx.1.1 km] The portion of the road contains both ups and downs with a gradient of maximum 3.2% and a minimum 0%. The large section of the road does not have significant horizontal curvature. However, as the section approaches towards Connoles Road junction, the horizontal curve radius reaches 3000m, which is the minimum for this section. Connoles Road junction to Murphys Creek Road junction [approx.0.7 km] The road section eases from the horizontal curve having radius of 3000m to a straight road while moving from Connoles Road junction to Murphy Creek Road junction. The road remains straight with a gradient of 0.5% max for large part of the section. Murphys Creek Road junction to Blanchview Road junction [approx.1.2 km] The road gradient gradually increases in this section till it reaches the maximum of 4.31% and then starts to ease a little with about 2% near the Blanchview Road. The road section is almost straight throughout. Blanchview Road junction to Park Ridge Road junction [approx. 0.5 km] The road gradient eases to nil (or almost zero) towards the west of Warrego Highway and Blanchview Road junction. Again the grade rises to about 1% just west of Park Ridge Road junction. However the horizontal curve of the section is negligible. 93

110 Park Ridge Road junction to west of Roches Road junction [approx. 2.1km] The road is relatively steep having around 2.5% gradient almost all the way with maximum of 3.5% gradient near Park Ridge Road junction and at the end of this considered section (that is about 350m west of Roches Road junction). Road section is relatively straight with only a single prominent curve present at around the junction where Jones Road meet Warrego Highway. West of Roches Road junction to Crossing of East Street [approx. 5.3 km] This stretch of Warrego Highway is comparatively very windy with steep gradient. The curvature of the road is in some places as low as 120 m and the maximum gradient in this section is above 10%. Segment Location 1 Paynter Road junction to Flat Gully 2 Flat Gully to Connoles Road junction 3 Connoles Road junction to Murphys Creek Road junction 4 Murphys Creek Road junction to Blanchview Road junction 5 Blanchview Road junction to Park Ridge Road junction. 6 Park Ridge Road junction to west of Roches Road junction 7 West of Roches Road junction to crossing of East Street. Table 6. 1 Approx. Distance Speed (km/h) Grade 1.25 km 100 Around 1.5% 1.1 km 100 Max. 3.2% Min. 0% 0.7 km 100 Max. 0.5% Min. 0% 1.2 km 100 Max. 4.31% Min. 2% 0.5 km 80 Max. 1% Min. 0% 2.1 km 60 and 80 at the end 5.3 km 100, 80 & 60 (decreases as the road reaches East Street junction) Max. 3.5% Min. 2.5% Max. 10% Summary of Road characteristics to the east of Toowoomba Horizontal curvature 6000 m Radius. Straight section to 3000 m radius. Straight section to 3000 m radius. Almost a straight section throughout. Almost a straight section throughout. Almost straight section throughout. Min. 120 m Radius Curvy section throughout. 94

111 The simplified grade profile used for energy estimation of this section is shown in Figure 6.3. The latter shows that the gradient of the section is very prominent and in some cases the high grade angle may demand 100% more energy than on the plane road as discussed in Section 4.2.3, Chapter IV and Table Grade profile (Postman Ridge to Toowoomba) 10 8 Grade (%) Distance (m) Figure 6. 3 Grade profile (Postman Ridge to entrance of Toowoomba city) City Segment (After crossing East Street junction till Nugents Pinch Road) From the west of the East Street junction, the Warrego highway enters the urban environment possessing relatively high amount of traffic. The segment of the Warrego highway within the Toowoomba city is about 11 km long. Within this segment, there are about 15 signalized intersections and about 42 unsignalized intersections. Hence the effect of such intersections on fuel consumption for this segment might be prominent; both due to the decrease in travel speed and increase in stop/start manoeuvres. 95

112 Segment Location 1 East St. Crossing to James St. Crossing 2 Cohoe St. Crossing to West St. Crossing 3 West St. Crossing to Hursley St. Crossing 4 Hursley St. Crossing to Bridge St. Crossing 5 Bridge St. Crossing to McDougall St. Crossing 6 McDougall St. Crossing to Nugent Pinch Road Junction. Table 6. 2 Approx. Speed Distance (km/h) 0.9 km 60 (assume) Horizontal curvature Two sharp curves and a small section of large radius curve. More than 0.5km of straight section Straight section 3.3 km 60 (assume) 1.9 km 60 One curve. Rest of the section is straight. 1.6 km 60 Straight Section. Curve at the end junction. 2.2 km 60 Curve at the start junction. Rest of the section is straight. 1.5 km 80 Most of the section is straight. Very large radius curves around Nugent Pinch road junction. Summary of Warrego Highway characteristics passing through the city (towards Nugents Pinch Road) Outward from Toowoomba city (West of the city) Nugents Pinch Road junction to Banyula Road junction The road section has a relatively steep gradient in the beginning and it eases as it approaches Banyula Road junction. The section has a comfortable horizontal curve radius of 930m which gets even better as the road reaches Banyula Road junction. Banyula Road junction to Charlton Connection Road junction The road section has a continuous grade range from 1.35% to 3.5%. The road is straight for most of the length, however for a small section there is a horizontal curve of radius approximately 915m. Overall the change in vertical alignment of the section is more distinct than horizontal. 96

113 Segment Location Approx. Distance Speed (km/h) Grade Horizontal curvature 1 Nugents Pinch 0.5 km 80 Min. 0% Min. 930 m Road junction to Max. 2% Radius. Banyula Road Straight for most junction of the section. 2 Banyula Road 1.1 km 80 Min. Straight for most junction to 1.35% of the section. Charlton Max. Min. 915 m Connection Road 3.50% Radius. junction 3 Charlton 2.8 km 60 Min. 0.5% Strain for most Connection Road Max. 6.7% of the section. Junction to Nass Min 913 m Road and Wirth Radius Road Junction Table 6. 3 Summary of Warrego Highway characteristics passing through the city (towards Nash Junction) The simplified grade profile used for energy estimation of this section is shown in figure 6.4. The figure shows that there is less steep grade compared to the section of Warrego Highway coming into Toowoomba from Ipswich. Grade (%) Grade Profile (Toowoomba to Nass Road Junction) Distance (m) Figure 6. 4 Grade profile (Exit from Toowoomba city to Nass Road junction) 97

114 Figure 6.5 shows the simplified speed profile with the grade alignment of the same section. The speed profile shown in Figure 6.5 was established for a heavy commercial vehicle. The speed profile was determined based on: speed profile of the small car speed profile drawing provided by Department of Main Roads, Toowoomba Districts; and analytical judgement. This speed profile was kept constant through out the fuel estimation analysis. The main objective of fixation of the speed profile was to standardize the results of various vehicle runs in the corridor Existing road route Speed (km/h) Figure 6. 5 Speed Grade Distance (m) Speed and grade profile of existing road route Option Two (Existing Rail) Grade (%) The option involves existing rail track between near Postman s Ridge locality (before crossing Lockyer creek) and Gowrie Junction. QR (2001) was used to extract the route alignment data, which provides rail track information of section stretching from 98

115 Quilpie in the west to Rosewood in the east (the extent of the Brisbane Metropolitan Area). The total length of the track between Postman s Ridge locality and Gowrie Junction is about 50 km. The track was segregated into 350 segments based on the homogeneity of horizontal and vertical alignment. Table 6.4 presents the eight broad sectional divisions carried out to give an overview of the route. The detail data of horizontal and vertical alignment for the track are in Appendix J. The track between Postman s Ridge locality and Toowoomba is a single track railway. This track climbs up the Great Dividing Range, passing though number of tunnels before cresting at Harlaxton. From Harlaxton, the track descends to the Toowoomba CBD. There are five passing loops on this section namely Lockyer, Murphy s Creek, Holmes, Spring Bluff and Rangeview. The maximum allowable speed is 80 km/h, with block trains restricted to a maximum speed of 60 km/h and triple header block trains between Harlaxton and Murphy s Creek, in the Down direction, restricted to a maximum speed of 20 km/h (QR 2001). The maximum grade for this section is 2%, when grades on both the direction are taken into account. The minimum nominal horizontal curve radius for that section is 100 meters. The track length between Toowoomba CBD and Gowrie junction is about 12 km. The maximum grade for this section is 1.27%. For most of the length the track has gradient higher than or about 0.67%. This track segment is relatively windy with lowest of the curvature measuring around 100m. The simplified grade profile of the rail track between Helidon Toowoomba-Gowrie is shown in Figure 6.6. Table 6.4 shows the sectional running times for two types of trains currently operating on the track, which are for this study purpose divided into eight board sections. The given running time do not reflect acceleration and deceleration characteristics of trains. 99

116 Grade (%) Grade Profile Distance (Km) Figure 6. 6 Grade profile of existing rail track Location Approx. Distance Grade Horizontal Curvature Running time (min) (km) (m) Freight Mineral Up Down Up Down 1 Helidon - Lockyer Min Lockyer-Murphys Creek Min Murphys Creek-Holmes Min Holmes-Spring Bluff Min Spring Bluff-Rangeview Min Rangeview-Toowoomba Min Toowoomba-Willowburn Min Willowburn-Gowrie Min Table 6. 4 Summary of Rail track characteristics Segment Option Three (Proposed Road alignment) The new road route starts from Warrego Highway. It joins the existing four lane Warrego Highway at Paynters Road, Postmans Ridge, at a grade separated interchange with Brisbane oriented connection. The route (under consideration here) ends under the Warrego Highway, Charlton (where Warrego Highway meets Nass Road and Wirth Road). The total length between the proposed sections is 28.5km.The location is planned to provide a simple interchange for all interconnecting movements with the highways. 100

117 Alignment description The horizontal curve is comfortable in this option, the tightest of the radius being 600m. The vertical alignment for the section has a maximum grade of 5.5%. A desirable maximum grade of 4% has been proposed for the route west of range. The route section is segregated to form 58 homogeneous segments for case study purpose based on drawing given in Maunsell (1998). The route description followed hereafter for this new proposed corridor is from Maunsell (1998) and is only divided into 8 segments. Appendix H contains the alignment data extracted from those segments. Segment 1 - Warrego Highway (east) to Murphys Creek Road The new route joins the existing four lane Warrego Highway at Paynters Road, Postmans Ridge, at a grade separated interchange with Brisbane oriented connections. The new west bound carriageway passes below the Warrego Highway then crosses Rocky Creek and then continue across Postmans Ridge Road and then linked to Murphys Creek Road. Segment 2 Murphys Creek Road to Wards Hill After crossing Murphys Creek Road, the route would then cross over the ridge north of Six Mile Creek and proceed to cross a series of gullies and ridges before crossing the main spur in a deep cutting under the transmission lines at Wards Hill. Segment 3 Wards Hill to McNamaras Road The route would pass over the ridge at Wards Hill and then continue along the southern base of Wards Hill, across Six Mile Creek and Gittens Road. The route then ascends westwards on a maximum 5.5% grade to cross Gittens Road and then passes through Withcott Quarry. Segment 4 McNamaras Road to Morleys Road The route would then follow the northern slopes of the Withcott Valley to commence its ascent of the Dividing Range. Segment 5 Morleys Road to New England Highway The route cuts under Morleys Road (requiring a new overbridge) then continues the ascent of the Range on 5.5% grade. From Wallens Road, the route continues to 101

118 ascend the north slope of the escarpment, on an alignment which follows below the Southern and Western (Main) Railway, and Blue Mountain Heights residential estate. Segment 6 New England Highway to Bedford Street The road then completes the ascent of the escarpment by crossing under the New England Highway in a tunnel. The new road would continue across Old Goombungee Road, Gowrie Creek and Western Railway. A section of Gowrie Creek is to be realigned where the road encroaches into the creek. The route then passes to the south of the Toowoomba City Council s solid waste landfill area, and on to Bedford Road. Segment 7 Bedford Street to Ganzer Road The route crosses over Bedford Street on an overbridge, then continues through open farmland and crosses over Boundary Road on an overbridges. The route then continues over open fields on a fill embankment, before crossing to the south of Hermitage Road/Ganzer Road just west of Nugent Pinch Road. Segment 8 Ganzer Road to Warrego Highway The route continues along the gully on the south side of Ganzer Road, continues through farmland (including several hobby farms) on a gradual grade. The simplified grade profile of the new proposed road alignment is presented in Figure 6.7. Table 6.5 gives the summary of new proposed second range crossing. 6 Grade Profile Grade (%) Chainage (m) Figure 6. 7 alignment) Grade profile Postman Ridge to Charlton (new proposed road 102

119 Segment Location Approx. Distance Grade 1 Warrego 4.2 km Max. 2.5% Highway(east) to Min. 0% Murphy Creek Road 2 Murphy Creek Road to Wards Hill 3 Wards Hill to McNamaras Road 4 McNamaras Road to Morleys Road 5 Morleys Road to New England Highway 6 New England Highway to Bedford Street 7 Bedford Street to Ganzer Road 8 Ganzer Road to Warrego Highway Table km Max. 4.96% Mostly with average gradient between 2-3%. 2.7 km Max. 5.5% Mostly steep with 4-5.5% grade 4.8 km Max. 5.5% Mostly with grade between % grade 3.3 km Mostly 5.5% grade. 3.4 km Max. 4.6% Mostly with grade between 1.5 to 2% 2.8 km Max % Min. 0 % 4.5 km Max. 2.64% Min. 0% Summary of new proposed second range crossing Horizontal curvature Min. 650 m. Mostly large radius curve (>1200km). and straight section Min m Mostly curvy with large radius curve (>2000 m). Min. 650 m Mostly curvy with large radius curve (>1000m) Min. 660 m Mostly curvy with large radius curve (>1000 m) Min. 600 m Mostly curvy road with 600 (or more) m radius curve. Min. 610 m. Mostly straight large curve radius section (>3000 m). Min m Mostly straight section Min m Mostly straight section Option Four (Proposed Rail) Maunsell (1998) suggested of building Queensland Rail s routes in common corridor where relevant, and the location and size of a freight/industrial terminal with possible sharing with Queensland Rail. However, in some section of the proposed road section, the grades are higher than suitable for rail alignment. Queensland Rail has been undertaking several studies of the Grandchester to Gowrie Junction corridor with a view to upgrading the route in question. The work has been done in sections and resolved as far as possible, section by section. The several route segments have been subjected to preliminary work. The alternative routes between Helidon and Gowrie Junction are under consideration. The rail route proposed by one of the QR and QT study (QR and QT 2003) was chosen as the new alternative in this study. The simplified grade profile of the proposed rail track is shown in Figure

120 1.67 Grade Profile Grade (%) Figure Distance (m) Grade profile near Lockyer to Gowrie (new proposed rail alignment) Figure 6.8 suggest that grade profile of the new proposed rail alignment is not very relaxed. Particularly the short section considered in this study possesses the high gradient. However, the sharp curves that are present in the existing rail track are considerably reduced to improve the performance of the train. Appendix K contains the curvature data of the section which supports the above statement. 6.3 Freight description Freight in this region is mainly carried by road and rail. Rail has traditionally carried bulk products such as grain and livestock over relatively long distances but recent developments have increased the extent of road based transport of these commodities. Productivity improvements have been achieved through the use of freight efficient vehicles (road trains and B - Doubles). The freight task in the region is directed to a wide range of commodities including bulk grains, livestock, meat products, dairy products, horticultural products (including flowers), manufactured products (export and import), food items (export and import) and construction materials (export and import). In addition, to the freight task generated by the region itself, there is a considerable quantity of freight passing through the region both interstate and intrastate. Significant quantities of freight to 104

121 and from the Port of Brisbane pass through the region bound for interstate destinations such as Melbourne. There is a diversity of types and quantities of products being carried, not necessarily in the most efficient manner or mode. There is an expectation that with the appropriate infrastructure, more efficient mode shares would evolve with consequent savings to industry and greater safety and convenience on the road network. This case study focused on the movement of freight described in Table 6.6. The values used for the comparison were based on the train consists information provided by Coal and Freight Services Department of Queensland Rail (courtesy: Mr. Mark Nash) and study carried out at University of Wollongong and Samrom Pty Ltd as a part of Rail CRC project (courtesy: Prof. Philip Laird). Freight Type Coal Containerized Freight Table 6. 6 Freight type Amount 2000 tonnes 300 tonnes 6.4 Energy estimation This section discusses the estimated energy consumed for each option. The section considers the fuel consumed in the line haul section of the freight movement for the purpose of corridor option evaluation of both old and new alignments. However, the tool is furnished with the subroutine to calculate the fuel consumed for pick up and delivery legs as well. As discussed in Chapter III, this is essential for determining the actual energy advantage that one freight moving option has over other. This inclusion of energy consumed in pick up and delivery section demands more details of pick and delivery route legs and also the vehicle types. The tool users are left to decide on those factors to compare door-to-door modal efficiencies Option one (Existing road) B-Doubles and semi-trailers are the widely used freight moving vehicles in the existing road route. Both of these vehicles were considered for in the case study. The 105

122 vehicle characteristics of a representative B-Double and Semi-trailer are presented in Appendix B. The existing road section was divided into 73 sections while approaching the Toowoomba city from Postman Ridge. The city section runs for about 11 km and was discussed earlier in Section To represent the congestion of the city run, the volume to capacity ratio for this section was taken as 0.6. The section of the road exiting from the city is about 1.5 km and was divided into 14 sections according to grade and curvature of the section. B Double Figure 6.9a and Figure 6.9b show the estimated fuel performance of a B Double. Depending upon the payload, the total number of runs varies which directly affect the total fuel consumption. Although the absolute fuel consumption increased with increase in payload, the fuel consumption performance also showed the increment. For a freight task of 300 tonnes (see: Table 6.6), the total fuel consumed by a B- Double is demonstrated in Figure 6.9b. The latter shows the improvement in loading (payload) from 58% to 98% induced an improvement in fuel consumption by 35% (the base-case being fuel consumed for 58% loading). As discussed in section 6.2.2, the segment of road entering Toowoomba (Postman Ridge to Toowoomba Run; km) is very windy and steep. This is reflected on the fuel performance shown in Figure 6.9a, in which (for every loading condition) fuel performance of Postman Ridge to Toowoomba run is less compared to other two runs. Although the grade was assumed to be absent in the city run, the fuel performance is low compared to the performance of the section coming out of the city. This difference in performance is the result of high congestion on fuel consumption while driving within the city area. If fuel performance (Lt./1000 NTK) while moving out of the city (Toowoomba to Gowrie Junction Run) is taken as 1, then the ratio of fuel consumed in between Out of City Run, City Run and Entering City Run would be approximately 1:1.2:1.4, respectively (i.e. 20% and 40% increment respectively). 106

123 Figure 6. 9a B Double Performance Chart (A) Figure 6. 9b B Double Performance Chart (B) Six Axles Articulated Truck Figure 6.10a and Figure 6.10b show the estimated fuel performance of a Six-Axle Articulated Truck. For a freight task of 300 tonnes (see: Table 6.6), the total fuel consumed by the Six-Axle Articulated Truck is demonstrated in Figure 6.10b. The latter shows the improvement in loading (payload) from 58% to 98% induced an 107

124 improvement in fuel consumption by 32.2% (the base-case being fuel consumed in 58% loading). The fuel performance of Postman Ridge to Toowoomba run is less compared to other two runs for Six-Axle Articulated Truck. Due to the congestion penalty, the fuel consumption while driving within the city area is high compared to Toowoomba to Gowrie Junction Run regardless of no grade assumption. If fuel performance (Lt./1000 NTK) while moving out of the city (Toowoomba to Gowrie Junction Run) is taken as 1, then the ratio of fuel consumed in between Out of City Run, City Run and Entering City run would be approximately 1:1.2:1.5, respectively (i.e. 20% and 50% increment respectively). Figure 6. 10a Six Axles Articulated Truck Performance Chart (A) Figure 6.10b Six Axles Articulated Truck Performance Chart (B) 108

125 Table 6.7 compares the performance of the two road freight vehicles considered. The tabulated values are the performance of respective vehicles on the existing road route. Payload Description 58% 76% 98% B - Articulated B - Articulated B - Articulated Double Truck Double Truck Double Truck Freight moved (tonnes) Fuel Consumed in a single run (Lt.) Total Efficiency (Lt./ 1000 NTK) Table 6. 7 Comparison table (existing road) Table 6.7 shows that to move a small amount of load, choosing a smaller vehicle would be advantageous. For example, when there is a 25 tonnes to be transported then use of 6 Axle Articulated Truck would give better than 30 Lt./1000 NTK where as use of B Double would only give approximately 33 Lt./1000 NTK. In such case, use of 6 Axle Articulated Truck would prove beneficial for an energy prospective Option two (Existing rail) Typical train consists running on the Helidon to Gowrie Junction track are presented in Table The latter is based on the information gathered from Queensland Rail and the simulation by Mr. Max Michell of Samrom Pty Ltd Adelaide (Personal communication with Prof. Phil Laird). Train Type Locomotives Wagons Approx Weight (Tonnes) Approx Length (Meters) Approx Coal Train (loaded) , Coal Train (Empty) Container Train Primary Industries(Loaded) , Primary Industries(Empty) Table 6. 8 Train consist information 109

126 The existing rail track under consideration was divided into 273 sections, which includes the track segments between Murphy Creek and Gowrie Junction via Toowoomba. The length of track under consideration for entering the city of Toowoomba is approximately 30 km and the length of track exiting the city to Gowrie Junction is approximately 12 km. The energy intensity of rail freight was found high compared to values suggested by previous studies for other corridors. The difference could be the result of hilly terrain of the study area, which affects the track forces (grade and curvature) and mass carrying ability. Moreover, there is also the restriction imposed on the length of the train (due to crossing loop length) which would adversely affect the load carrying capacity. The fuel performance of the existing rail is shown in Table 6.9. Train Properties Train Length = 640m Train Mass =1800 ton Gross to Net Ratio = 1.36 Section (Approx.) Fuel Used (Litres) Distance Travelled (km) Efficiency (Lt./1000 NTK) Helidon to Murphy Creek Murphy Creek to Spring Bluff Spring Bluff to Gowrie Junction Total Table 6. 9 Fuel performance on the existing rail track As discussed in chapter three, the efficiency varies considerably with train properties. The train properties used for performance computation in this study is tabulated in the first column of Table 6.9. The first section (distance km) is the distance between Helidon to Murphy Creek. Since this section has less curve and relatively relaxed grade, the fuel performance of this section is better compared to the second and third sections, as shown in third and fourth rows of table 6.9. The second section (distance km) is the distance between Murphy Creek to 5.11km away from Spring Bluff. This section has considerable grade and curvature 110

127 as could be seen on Section These constrain in curvature and high grade is also reflected in the calculated efficiency in Table 6.9. The third section (distance km) is the distance between 5.11km away from Spring Bluff to Gowrie Junction. This section is less curvy and has less grade (refer Section 4.2.3). Hence the performance is better in this section. The simulation by Mr Max Michell of Samrom Pty Ltd. Adelaide gave a similar result. The result for a 670m long two locomotive train, carrying 2000 tonnes, was around 909 litres. The difference in the results could be due to the variation in the length of track (around 2km); the slight variation in the interpretation of the alignment profile; differences in train properties assumed. The graphical representation of this comparison is presented in Figure Figure Simulation Performance Comparison Option Three (Proposed Road alignment) The proposed road alignment possesses less horizontal curvature and the gradient is very little compared to the existing road. However, the length of road under 111

128 consideration in the proposed road alignment is almost equal to the length of the existing road alignment. Unlike in the existing road section, the road length was not divided into city and outskirts section here. This is because the new proposed road does not pass through the city section. The speed profile of the road section was assumed based on proposed grade and curvature of the section. The speed profile was kept constant for both types of vehicles considered. The efficiency of for the three different payload condition under consideration is presented in Table The overall efficiency for 98% payload and 58% payload was found to improve by 6.6% and 7.7% corresponding to the improvement in road alignment (based on existing road efficiency). Figure Fuel performance on new proposed road route 112

129 Vehicle 6 Axles Articulated Truck B Double Table Description Payload 58% 76% 98% Weight moved (tonnes) Fuel Consumed in a single run (Lt.) Total Efficiency (Lt./ 1000 NTK) Weight moved (tonnes) Fuel Consumed in a single run (Lt.) Total Efficiency (Lt./ 1000 NTK) Comparison table (proposed road) Option Four (Proposed new Rail) The proposed new rail has more relaxed curvature desirable for the smooth running of long train. However, the section under consideration here mainly consists of high gradient. The travel distance between the places has been considerably reduced. Because of this, the absolute amount of fuel saved would be important measurement regardless of the apparent decrease in energy efficiency (measured in terms of Lt./1000 NTK). The energy efficiency (measured in terms of Lt/tonne moved) is also expected to improve because of the relaxation of limiting train length and increased load carrying capacity due to favourable alignment. During the study of Rail CRC Project 24, Samrom Pty Ltd and Phil Laird suggested that the following train (refer Table 6.11) would be able to run on the new proposed rail alignment. However, operation of the train (refer Table 6.11) would not be possible on the existing rail track due to length and speed restriction. The performance showed in Table 6.11 portrays that there is not essentially a huge gain in efficiency when measured in terms of Lt./1000 NTK. Train Properties Run Performance Number of Locomotives Trailing Load Train Length Speed Tonne 1250 m 100 km/hr Fuel Used Distance Travelled Efficiency (Lt./1000 (Litres) (km) NTK) Table New track s train properties and performance This lack in expected large gain in the efficiency term could be attributed to: Insignificant improvement in the proposed grade of the track due to the nature of the terrain. (refer: Figure 6.6 and 6.8); and Increased running speed. 113

130 However, the absolute improvement in the fuel efficiency (measured in lt/1000 tonnes moved) is noted to have improved, when freight movement between near Lockyer and Gowrie is considered for both the rail options. This could be attributed to the factors mentioned in the first paragraph of this section (6.4.4). This improvement is portrayed in Figure Figure Rail performance: Old Rail Route versus New Rail Route Note: Fuel used presented above is for a single run. In new route a single run was assumed to have capacity to carry approximately 1075 tonnes more Options Comparison The options and performances discussed in section to have been summarized in this section. The starting and ending points of all the options involved do not exactly overlap with each other. Hence the comparison shown below should only be treated as a preliminary analysis and suggestion. Movement of 2400 tonnes of containerized freight has been considered in the Table 6.12 for the comparison purpose. 114

131 Vehicle Route Distance Number Efficiency Fuel (km) of Runs Lt./1000 NTK Lt./1000 Ton (Lt.) Articulated Truck Existing B Double Route Articulated Truck Proposed B Double Route Old Route Train Existing New Route Train Proposed Table Four options comparison Table 6.12 shows that the existing trains as the efficient mode (amongst the comparison) of moving the freight when compared in terms of litres per 1000 tonnes. However, when the absolute expected fuel gain is considered, the new trains is found as the most efficient mode and the existing trains as the least efficient one. This is depicted in Figure 6.14 below. Figure Four options comparison In the existing condition, B Double operation has been found to be more energetically beneficial than train and articulated trucks. However, while comparing between the two trucks, the load factor plays as important role as discussed in section

132 CHAPTER VII MODEL APPLICATION: Simulated Cases 7.1 Background Some simulated routes have been considered in this chapter to portray the extended application of the model developed in this thesis. The virtual routes were planned so as to take into account the effect of gradient, travel speed, curvature and the handling of the freight at the intermodal station in a realistic way. The options consist of road and rail line hauling accompanied with road pick-up and delivery. The routes described below are arbitrary and may not exactly resemble any actual freight corridors. However, the virtual routes were developed to closely reflect real-world scenarios. The routes were developed based on the concepts shown in Figure 7.1 and 7.2. Rail Link Intermodal Terminals Road link For Pick Up and Delivery Freight Depot Freight collection and distribution route Figure 7. 1 Intermodal freight movement concept Road Link Freight Depot Freight collection and distribution route Figure 7. 2 Road alone freight movement concept 116

133 Freight collection and distribution route has been assumed to be same in both the cases (road alone and intermodal). These routes might comprise of city run and use of smaller road vehicle, but would be same for both. Hence the energy consumed in these legs is not discussed in the comparison process. 7.2 Route specification and comparison scenarios This section discusses the characteristics of the virtual corridors used in the model application. This study resorted to five hypothetical freight corridors to illustrate the use of energy comparison model. The general characteristics of the freight routes are given in Table 7.1. Corridor No. Length (km) Pick-up Road Link Rail Link Delivery Road Link No. of Intermodal terminals Road Alone movement (Length 800 km) 0 Table 7. 1 Freight routes general characteristics The route alignments were fixed so as to develop a fair comparison between scenarios. Table 7.2 presents the route alignment; the detail breakdown of this alignment is presented in Appendix L. Percentage of total link (%) Geometric Properties Rail Line Haul Road Line Haul Road Pick-up and Delivery Grade Curvature Grade + Curvature Straight Section Table 7. 2 Alignment properties of hypothetical corridors Each link was segregated into several homogeneous sections. As shown in appendix L, the length of each homogenous section had been determined throughout the analysis as a percentage of total route distance to simplify the comparison process. Similarly, the roughness of the road surface had been fixed to 100 NRM counts per km and the volume capacity ratio had been fixed at 0.3 for all on road movements. The simulated case-studies are further categorized depending upon the operational characteristics of freight movements. They are categorized based on: 117

134 i. Type of vehicle used: a)6 axle articulated truck; b)b-double; c)train Type ii. Payload ratio The payload ratio of road vehicles was varied to illustrate the expected effect of payload on trip energy demand. Three standard types of trains were used to determine the effect of variation in train properties. The train types used for the comparison are shown in Table 7.3. Properties Train Type Type A Type B Type C Length of Train (m) Mass of Train (tonnes) Gross to Net Ratio Number of Locomotives Number of Wagons Total Number of Axles Net Weight Carried (tonnes) Table 7. 3 Train Properties Based on those operational characteristics and routes mentioned above, the model was run for 28 scenarios (Refer Table 7.4) and the outputs are discussed briefly in section

135 Operational Characteristics Scenario Road Leg length Vehicle Train leg length Payload Train Type Number (km) (km) (%) Axle Artic A Axle Artic B Axle Artic C B Double A B Double B B Double C B Double A Axle Artic A B Double B Axle Artic B B Double C Axle Artic C B Double A Axle Artic A B Double B Axle Artic B B Double C Axle Artic C B Double A Axle Artic A B Double B Axle Artic B B Double C Axle Artic C B Double NA 80 NA Axle Artic. NA 80 NA B Double NA 100 NA Axle Artic. NA 100 NA Table 7. 4 List of Scenarios These scenarios had been developed allowing the road link to meet the rail line-haul at different points, in order to quantify the energy impacts of each option. It is acknowledged that the operation of B-Double on pick-up and delivery links could be restricted by factors such as operational permission of long and heavy vehicles on certain road type and time of day. For the operation of any type of vehicle, the final freight depot centre should have been designed for the full operation of that vehicle type, especially for easy access and turning of long vehicles. Hence the operation of B-Doubles could be only for comparison purpose in some scenarios presented in Section 7.4, particularly when the pick-up and delivery legs are short in length and comprises of some urban movement. 119

136 7.3 Energy Estimation This section presents the energy demand of each scenario listed in Table 7.4 (in Section 7.2). The road and rail link length varies across the scenarios. Furthermore, there is also a change in alignment properties between various types of links such as road line-haul, rail line-haul and road pick up and delivery. This section also discusses the energy demand for each of those sections Scenario one to six (route remain constant with varying vehicle properties) Scenarios one to six operate on the same route. The variations across these scenarios are the type of road vehicles and the type of train in operation. Scenario one and four has the same type of train and similarly scenario two and five and scenario three and six also have the same type of train. The difference between these paired scenarios is the type of road vehicle (Articulated Truck or B-Double) serving road pick-up and delivery. However, both types of road vehicles are assumed to be operating on full loading capacity in these six scenarios. The performance of B-Double and Articulated Truck on the road pick-up and delivery link are presented in Figure 7.3. Figure 7. 3 Performance of road vehicles on pick-up and delivery links 120

137 The first eight bars in Figure 7.3 shows the fuel consumed on different section of road pick-up and delivery links. However, the last two bars on the right hand corner shows the fuel efficiency of those run which incorporates the freight being moved along with distance travelled. The fuel consumption portrayed in Figure 7.3 is for a single run of the vehicle on pick-up and delivery links. Due to variation in load carrying capacity of a train, the number of trips made in the road pick-up and delivery leg would vary to match the realistic payload limit of the train. The total fuel consumed in these six scenarios is presented in Figure 7.4. Fuel Consumption (Lt.) Road Pick up and Delivery Intermodal Transfer Rail Line Haul Figure 7. 4 Scenario One Scenario Four Scenario Two Scenario Five Total fuel consumed for scenario one to six Scenario Three Scenario Six The total fuel consumed portrayed in Figure 7.4 does not depict the efficiency of the scenario. The efficiency is depended on amount of freight being transferred as well. In these scenarios, one and four has the least freight moving capacity and three and six has the highest freight moving capacity. Table 7.5 presents the freight being moved in scenario one to six. Scenarios Train Type in Use Freight moved (Tonnes) Scenario One and Scenario Four Type A 1389 Scenario Two and Scenario Five Type B 1882 Scenario Three and Scenario Six Type C 2188 Table 7. 5 Freight moving capacity of scenario one to six Table 7.5 and Figure 7.4 could be used to derive the efficiency of the total movement across the six scenarios. The aggregate fuel performance across those six scenarios is presented in Figure

138 Figure 7. 5 Aggregate fuel performance (Scenario one to Scenario six) Figure 7.5 illustrates that scenario six is efficient compared to scenario one to five. Scenario six has B-Doubles operating on pick-up and delivery leg which is 100km and Type C train hauling the freight over 700km corridor. It portrays that even with Type C train in operation, if the pick-up and delivery links are served by Articulated Trucks then the overall performance would be poorer compared to Type B train with B-Doubles operating on pick-up and delivery links Scenario seven to twelve (route remain constant with varying vehicle properties) Scenario seven to twelve operates in 600 km long rail line-haul and 200 km long road pick-up and delivery corridor; with 80% payload in two road vehicle categories namely, B Double and Articulated Truck. Furthermore, three different train types were considered to illustrate the affect of variation in train properties. As shown in Table 7.4, scenario seven and eight operates in the same line-haul environment and hence the variation in total energy efficiency would illustrate the difference in performance of B-Double and Articulated Truck in pick-up and delivery link. Similarly, scenarios nine and ten operates in the same-line haul operating conditions and likewise scenarios eleven and twelve have the same linehaul condition. The performance of B-Double and Articulated truck in 200km long pick-up and delivery section considered here are presented in Figure

139 Figure 7. 6 Road vehicle performance with 80% payload on 200 km road Figure 7.6 shows that Articulated Truck consumes less fuel in each section of road. However, the load carrying capacity of Articulated Truck is less compared to B Double. Hence B-Double has higher efficiency than Articulated truck as shown in the right hand corner of Figure 7.6. Similarly, the fuel consumed by three different types of train (Type A, B and C) on 600 km long rail corridor for a single run is shown in Figure Fuel Consumption (Lt.) Type A Train Type B Train Type C Train 0 Figure 7. 7 Straight Section Grade + Curve Section Grade Section Train performance in 600 km rail link Curve Section Although Figure 7.7 shows that Type A train consume less energy, Type C train are more efficient when freight moved is also taken into consideration (Refer Figure 123

140 7.8). The total load carrying capacity of different train types are presented in Table 7.5 (Section 7.3.1) Type A Train Type B Train Type C Train Efficiency (Lt./1000 NTK) Figure 7. 8 Train Type (A, B and C) Efficiency of three train types on 600m rail line haul link The total fuel consumed by scenarios seven to twelve are presented in Figure 7.9. It shows scenario twelve consume the highest amount of energy. Fuel Consumption (Lt.) Road Pickup and Delivery Intermodal Transfer Rail Line Haul Scenario 7 Scenario 8 Scenario 9 Scenario 10 Scenario 11 Scenario 12 Figure 7. 9 Total fuel consumed in scenario 7 to scenario 12 The increment in total fuel consumed between scenario seven and scenario twelve is about 6978 lt (58% increment compared to scenario 7); and the increment in net freight mass being moved is 799 tonnes (about 57.5% compared to scenario 7). This 124

141 indicates almost one to one increment between fuel consumption and tonnes moved when compared in percentage terms. Efficiency (Lt./1000 NTK) Scenario 7 Scenario 8 Scenario 9 Scenario 10 Scenario 11 Scenario 12 Figure Energy efficiency between scenario 7 and 12 Scenario seven, nine and eleven is served by B-Doubles on road pick-up and delivery links. When total energy efficiency between scenario seven and eleven is compared, scenario eleven is efficient. The improvement in energy efficiency between scenario seven and eleven is 0.44 lt/1000 NTK (which is about 4.1% improvement compared to scenario seven efficiency). Scenario seven efficiency showed better performance compared to scenario twelve. This is because of the difference in operating efficiency of road freight moving vehicles. The performance of B Double with Type A train (scenario seven) was found to be more efficient than the performance of Articulated Truck with Type C train (scenario twelve). When compared individually, Type C train is efficient compared to Type A train (Refer Figure 7.8) Scenario thirteen to eighteen (route remain constant with varying vehicle properties) This section presents the performance of freight moving vehicles when the combined length of road pick-up and delivery leg is 300km and rail line hauling length is 500km. Vehicles used on road pick-up and delivery are B-Double and Articulated Truck. The payload of these vehicles has been simulated at 80% of total capacity. 125

142 Hence, payload for B-Double and Articulated Truck in these scenarios would be 35 tonnes and 24.5 tonnes, respectively. Scenarios thirteen and fourteen would use Type A train and similarly scenarios fifteen and sixteen would use Type B train, and scenarios seventeen and eighteen would use Type C train. Fuel Consumption (Lt.) Road Pickup and Delivery Intermodal Transfer Rail Line Haul Scenario 13 Scenario 14 Scenario 15 Scenario 16 Scenario 17 Scenario 18 Figure Total fuel consumed in scenario 13 to scenario 18 Figure 7.11 shows the total fuel consumed by scenarios thirteen to eighteen. The scenario eighteen has the high energy consumption due to large amount of freight being transferred compared to scenario thirteen (or Scenario 17). Between scenario thirteen to eighteen, the road pick-up and delivery fuel consumption comprises of larger portion of total fuel consumption. However, the actual distance travelled by rail-line haul is 1.67 times higher than total of road pickup and delivery leg Efficiency (Lt./1000 NTK) Scenario 13 Scenario 14 Scenario 15 Scenario 16 Scenario 17 Scenario 18 Figure Energy efficiency between scenario 13 and

143 This section also shows that B-Double when used with Type C train would provide the most efficient freight moving option Scenario nineteen to twenty-four (route remain constant with varying vehicle properties) Scenario nineteen to twenty-four operates on 400 km long rail-line haul and 400km road pick-up and delivery legs; with 80% payload for both road vehicles. Three types of train discussed above carry freight on rail line-haul link. Figure 7.13 illustrates the variation in total energy consumption across scenarios nineteen to twenty-four. It portrays the step pattern increment in total energy consumption. Although the length of road and rail legs is equal, the road fuel consumption comprises of between 73% and 76% of total fuel consumption. Fuel Consumption (Lt.) Road Pickup and Delivery Intermodal Transfer Rail Line Haul Scenario 19 Scenario 20 Scenario 21 Scenario 22 Scenario 23 Scenario 24 Figure Total fuel consumed in scenario 19 to scenario Efficiency (Lt./1000 NTK) Scenario 19 Scenario 20 Scenario 21 Scenario 22 Scenario 23 Scenario 24 Figure Energy efficiency between scenario 19 and scenario

144 Figure 7.14 depicts the total fuel performance of scenarios between nineteen and twenty-four. The difference in energy efficiency between Scenario twenty-one and twenty-three is much less. This could be attributed to low contribution of rail fuel consumption (on total fuel consumption) Scenario twenty-five to twenty-eight (Road alone movements) This section discusses the fuel performance of scenarios on which freight moves on road only. Two types of road vehicle are considered with varying payload (80% and 100%). The total freight moving distance was fixed to 800 km. The alignment for road line-haul movement was considered more relaxed compared to road pick-up and delivery link (Refer Table 7.2 in Section 7.2). The performance of road vehicles on 800km long road line-haul is shown in Figure Fuel Consumption (Lt.) Straight Section 450 Grade + Curve Section Grade Section Curve Section Articulated Truck B Double Articulated Truck B Double 80% Payload 100% Payload Figure Fuel Performance of road vehicle on road line-haul link The efficiency of road vehicles is presented in Figure As expected, it shows that Articulated Truck would be more energy efficient when used in full capacity compared to B-Double being used on less capacity. 128

145 Fuel Efficiency (Lt./1000 NTK) Articulated Truck (Scenario 25) B Double (Scenario 26) Articulated Truck (Scenario 27) B Double (Scenario 28) 80% Payload 100% Payload Figure Efficiency of road alone haulage 7.4 Overall results The model results presented above for different scenarios illustrates the better efficiency of intermodal freight movement option compared to road alone movement. Furthermore, for the road pick-up and delivery movement the efficiency of the scenarios improved with improvement in the payload ratio for road vehicles. Total Trip Efficiency (Lt./1000 NTK) B Double (100% Payload) B Double (80% Payload) B Double (60% Payload) Articulated Truck (100% Payload) Articulated Truck (80% Payload) Articulated Truck (60% Payload) Rail leg length : Road leg length Figure Fuel efficiency for various combinations with Type A Train Figure 7.17 illustrates the fuel efficiency of total trip when road pick-up and delivery length varied to form a different proportion of total trip length. The later illustrates the results of simulated trips of road vehicles operating in conjunction with Type A 129

146 train (carrying 1389 tonnes of freight). It depicts that when rail leg length is 15 times longer than road pick-up and delivery leg then trip fuel efficiency would improve approximately by 1.7 to 2.1 times (compared to the efficiency of total trip when road and rail leg is equal). Similarly, Figure 7.18 shows the simulated trips carrying 1882 tonnes of freight with operation of Type B train on rail line-haul. It shows that improvement in fuel performance in total freight trip when there is an increment in rail portion of the trip. As expected, the trip comprising Articulated Truck with 60% payload provided the worst case between the scenarios compared. The overall fuel performance improvement, due to variation in rail line-haul portion, ranged from 1.7 to 2.2 times (compared to the efficiency of total trip when road and rail leg is equal). 18 Total Trip Efficiency (Lt./1000 NTK) B Double (100% Payload) B Double (80% Payload) B Double (60% Payload) Articulated Truck (100% Payload) Articulated Truck (80% Payload) Articulated Truck (60% Payload) Rail leg length : Road leg length Figure Fuel efficiency for various combinations with Type B Train 130

147 18 Total Trip Efficiency (Lt./1000 NTK) B Double (100% Payload) B Double (80% Payload) B Double (60% Payload) Articulated Truck (100% Payload) Articulated Truck (80% Payload) Articulated Truck (60% Payload) Rail leg length : Road leg length Figure Fuel efficiency for various combinations with Type C Train Figure 7.19 shows the fuel performance of scenarios operating with Type C train in rail line-haul movement. The later shows the performance of simulated cases with 2188 tonnes of freight movement. The operation of 100% loaded B-Double in combination with Type C train showed the best performance, whereas, 60% loaded Articulated Truck in operation with Type C train showed the worst performance between the scenarios compared in Figure 7.19 Amongst the entire intermodal simulated cases; operation of full loaded B Double with Type C train has shown the best performance; and operation of 60% loaded Articulated Truck with Type A train has shown the worst performance. However, in most of the cases road alone movements with low payload ratio showed even poorer performance than the worst intermodal scenario. Whereas, fully loaded B Double in a road alone movement showed a better fuel performance than combination of 60% loaded Articulated Truck operating in conjunction with Type A train when road pick-up leg and rail line-haul leg were equal in length. 131

148 The typical order of intermodal simulated cases when sorted according to the fuel performance (in decreasing order) is: i. Intermodal movement with fully loaded B-Double on road pick-up and delivery; ii. Intermodal movement with fully loaded Articulated Truck on road pick-up and delivery; iii. Intermodal movement with 80% loaded B-Double on road pick-up and delivery; iv. Intermodal movement with 80% loaded Articulated Truck on road pick-up and delivery; v. Intermodal movement with 60% loaded B-Double on road pick-up and delivery; and vi. Intermodal movement with 60% loaded Articulated Truck on road pick-up and delivery. 132

149 CHAPTER VIII CONCLUSIONS AND FUTURE RESEARCH 8.1 Literature review After comprehensive literature review, the thesis reported the very significant proportion of energy being utilized on land-based freight transport sector all over the world. The review of energy consumed by various transport modes highlighted the rapid increasing trend in road freight energy consumption along with its rise in market share. A complete freight task could involve more than one mode and various combination options. This involvement of more than one mode warranted different phases in energy consumption, along with different modes used. Models developed for estimating the energy consumption for rail, heavy commercial vehicles and light commercial vehicles were extensively reviewed and grouped based on their modelling approach. The literature review explored energy quantification procedure on each segments of a complete freight task. Hence, the research aimed to compare and quantify the energy advantage that one option would have on another. 8.2 Model development and sensitivity of model parameters A complete freight task was divided into four segments for the total energy estimation purpose. These segments are: i. Energy consumed in Pick-up leg of the task; ii. Energy consumed in Line-haul link of the task; iii. Energy consumed in intermodal transfer station (if any); and iv. Energy consumed in Delivery leg of the task Energy consumption in each of the above sections was modelled by segregating them into the modes used. The review of literature showed that the contribution of energy consumed in intermodal transfer process was less significant compared to other section. Hence the energy consumed in this section was modelled based on aggregate value reported in literature. 133

150 The study showed the energy efficiency between the modes varies considerably with the alignment. Hence route alignment was given due consideration during energy estimation. Another important factor that was investigated in the thesis was the payload factor. The road sub-model was improved to reflect the payload contribution on energy consumption. Based on simulation model result for trucks and the literature reviewed, payload factor was determined to vary linearly between the practical load carrying limits of heavy commercial vehicles. Among the various energy influencing parameters, the parameters having a prominent impact on freight corridor level study were considered. For some typical base values, the influencing model parameters and its importance were determined by the sensitivity analysis and the brief summary is shown in Table 8.1. Importance order Rail sub-model Road sub-model 1 Grade Speed and Payload 2 Train Length Grade 3 Speed Congestion 4 Mass Curvature 5 Curvature Roughness 6 Number of axles Table Case study Importance of model parameters on road and rail fuel consumption The developed model was applied to the existing and proposed freight corridors crossing Toowoomba second range. The existing rail and road corridors were compared to the proposed rail and road second range crossing on an energy consumption basis. Based on the total fuel consumed to move a certain amount of freight across the range, the determined fuel performances are shown in Table 8.2, in the order of efficiency. S.N. Mode and Corridor Efficiency (Lt./1000tonnes) 1 Train on Proposed Route B-Double on Proposed Route B-Double on Existing Route Articulated Truck on Proposed Route Articulated Truck on Existing Route Train on Existing Route Table 8. 2 Fuel Performance on proposed and existing corridors 134

151 8.4 Model application: on simulated cases Various simulated cases were developed to illustrate the model application on estimating door-to-door energy consumption. Pick-up and delivery component of freight movement had a major impact on deciding which option is more energy beneficial. When pick-up and delivery legs length consist of larger portion of the total freight movement distance then the efficiency of the movement and the advantage of intermodal freight movement were considerably reduced. Type of train and road vehicle type was varied across the simulated cases so as to illustrate the impact of vehicle properties on door-to-door energy performance. The train properties of three train types (A,B and C) are given in Table 7.3 (Section 7.2). Figure 8. 1 Performance of some simulated cases An example of the results obtained is given in Figure 8.1, which shows that, Type C train when combined with B-Double would provide the best freight moving option. However, there is not much difference in efficiency when B-Double combined with Type A train is compared against Articulated Truck combined with Type C train. The simulated runs (presented in Chapter 7) also showed that fully loaded B-Double in a road alone movement showed a better fuel performance than combination of 60% loaded Articulated Truck operating in conjunction with Type A train when road 135

152 pick-up leg and rail line-haul leg were equal in length. Hence, the performances of both the components of freight movement are important and should be given a due consideration while choosing the energy efficient freight moving option. 8.5 Future Research The research has made numerous assumptions to simplify the estimation and comparison process. The result presented here could be further improved with sufficient data collection for validation purpose. Future research in this field could focus towards reducing the measurement error and increasing complexity of the model, but keeping the final computation relatively simple for end users purposes. The increased complexity could be focused in establishing a better relationship for the negative grade driving condition. Inclusion of accelerating energy demand in the road and the rail sub-models, along with braking energy consumption modelling, would improve the reliability of the model. Future research could focus in including commodity type and interlinking them with volume and weight that could be carried on different types of vehicles. A limited class and speed range between 70 to 105 km/hr were used for determining payload correction factor for the road sub-model. The model could be further improved with in-depth study of payload correction factor and its variation across the speed and vehicle class. With those improvements in the model, it could be implemented on case study corridor with more reliability. The accuracy could be further improved with additional data on speed profile, congestion level and roughness on those study corridors. By adding other vehicle operating cost factors on both the sub-models, the developed model and tool could be used as a decision making tool especially to plan a new corridor and maintain or restructure the existing corridors. 136

153 APPENDICES

154 References ABARE. (1993). "Energy: Demand and Supply projection, Australia, to " Research Report 93.2, Australian Bureau of Agricultural and Resource Economics, Canberra. ABS. (1997). "Australian Transport and the Environment." Australian Bureau of Statistic. ABS. (2002). "Freight Movements, Australia, Summary." Australian Bureau of Statistic. ABS. (2003). "Year Book Australia. Transport, transport equipment." Australian Bureau of Statistic. ACIL. (2001). "Rail in sustainable transport: A report to the Rail Group of the Standing Committee on Transport." ACIL Consulting. Affleck. (2002). "Comparison of Greenhouse Gas Emission by Australian Intermodal Rail and Road Transport." Affleck Consulting, Brisbane. Australian Greenhouse Office (2005). Australian Methodology for the estimation of Greenhouse Gas Emissions and Sinks 2003., ISBN , Department of the Environment and Heritage, May Ahn, K., Rakha, H., Trani, A. A., and Van Aerde, M. (2002). "Estimating Vehicle Fuel Consumption and Emissions based in Instantaneous Speed and Acceleration Levels." Journal of Transportation Engineering. Akcelik, R. (1983). "On the elemental model of fuel consumption." ARR No. 124, Australian Road Research Board. Akcelik, R., Bayley, C., Bowyer, D. P., and Biggs, D. C. (1983). "A hierarchy of vehicle fuel consumption models." Traffic Engineering and Control, 24(10), Apelbaum. (1998). "The Queensland Transport Task: Primary Energy Consumed and Greenhouse Gas Emissions." Apelbaum Consulting Group Pty Ltd. Apelbaum. (2003). "Queensland Transport Facts 2001." Apelbaum Consulting Group PTY LTD. ATC. (1991). "Rail vs Truck Fuel Efficiency: The relative fuel efficiency of truck competitive rail freight and truck operations compared in a range of corridors." Abacus Technology Corporation, Chevy Chase. Balls, A., Ferreira, L., and Bunker, J. (2002). "Quantifying freight movement performances by mode." Research Report 02-06, Physical Infrastructure Centre, Queensland University of Technology, Brisbane. References i

155 Benjamin, B., and Laird, P. "Comparison of AC and DC locomotives." Proceedings of the 2000 Mathematics in Industry Study Group, Bennett, C. R. (2003). "Using the NZVOC model to prepare PEM vehicle operating costs." Data Collection Ltd., Motueka. Biggs, D. C. (1987). "Estimating fuel consumption of light to heavy duty vehicles." AIR 454-1, Australian Road Research Board, Victoria. Biggs, D. C. (1988). "ARFCOM-Models for estimating light to heavy vehicle fuel consumption." ARR No. 152, Australian Road Research Board, Victoria. Bowyer, D. P., Akcelik, R., and Biggs, D. C. (1985). "Guide to fuel consumption analyses for Urban Traffic Management." Report Number 32, Australian Road Research Board, Victoria. BT. (1995). "Comprehensive Truck size and weight study: Phase 1-Synthesis: Energy Conservation and truck size and weight regulations : Working Paper 12." Battle Team, Ohio. BTCE. (1993). "The road freight transport industry: Information paper 38." Bureau of Transport and Communication Economics, Canberra. BTE. (1999). "Competitive neutrality between road and rail." Working Paper 40, Bureau of Transport Economics, Canberra. BTRE. (2002). "Greenhouse emissions from transport - Australian trends to 2020." Report 107, Bureau of Transport (and Regional) Economics, Canberra. Bunker, J., and Ferreira, L. "Assessing Freight Corridor Modal Performance." International Conference on Traffic and Transportation Studies, Guilin, China, Bunker, J., and Giles, R. (2001). "Evaluation of freight corridor mode performance: Brisbane to Cairns Corridor Agencies Survey." Research Report 01-11, Physical Infrastructure Centre, Queensland University of Technology, Brisbane. Chang, M., Evans, L., Herman, R., and Wasielewski, P. (1976). "Gasoline Consumption in Urban Traffic." Transportation Research Record 599. Communication, from the commission to the European parliament and the council (1999). "Intermodality and intermodal freight transport in the European Union. A systems approach to freight transport: Strategies and actions to enhance efficiency, services and sustainability." Official Journal of the European Communities, 42(C 198). Concawe. (1999). "Fuel quality, vehicle technology and their interactions." CONCAWE Automotive Emissions Management Group, Brussels. References ii

156 CSIRO, PPK, and USA. (2002). "Modelling Responses of Urban Freight Patterns to Greenhouse Gas Abatement Measures: Draft Interim Report." CSIRO Division of Building, Construction and Engineering; PPK Environment and Infrastructure; and Transport System Centre, University of South Australia, North Ryde 1670, New South Wales. Dijkstra, W. J., and Dings, J. M. W. (1997). "Specific energy consumption and emission of freight: A comparison between road, water, rail and air." Centre for Energy Conservation and Environment Technology, Delft. EC. (1995). "Towards fair and efficient pricing in transport: Policy options for internalising the external costs of transport in the European Union. Directorategeneral for transport-dg VII." EC (COM (95) 691), European Commission. EC. (1999). "MEET: Methodology for calculating transport emissions and energy consumption." ISBN , European Commission, Luxemburg. EC. (2002). "European Union Energy and Transport in Figures. Directorate General for Energy and Transport." European Commission in cooperation with EuroStat. Edwards, J. L. (1975). "Relationship between transportation energy consumption and urban spatial structure," Doctor of Philosophy, Northwestern University, Evanston, Illinois. Essenhigh, R. H., Shull, H. E., Blackadar, T., and McKinstry, H. (1979). "Effect of vehicle size and engine displacement on automobile fuel consumption." Transportation Research A, 13(A), Ferreira, L. (1976). "Errors in Location/Allocation: Gravity Models." Polytechnic of Central London. Ferreira, L. (1985). "Modelling urban fuel consumption, some empirical evidence." Transportation Research A, 19A(3), FreightInfo. (1997). "A database of national freight flows." FDF Management Pty Ltd., Victoria. Gargett, D., Mitchell, D., and Martin, L. (1999). "Competitive neutrality between road and rail." Bureau of Transport Economics, Canberra. Gojel, J. I., and Watson, H. C. "Relationship between road track cost and heavy vehicle fuel consumption." Fourth International Symposium on Heavy Vehicle Weights and Dimensions, Ann Arbor, Michigan, USA, Greenwood, I. D., and Bennett, C. R. (2001). "Modelling road user and environmental effects in HDM-4." ISBN , Birmingham. References iii

157 Haferkon, F. R. "Container railcar versus container lorry tranport: A comparative study." Urban Transport and the Environment in the 21 st Century, Seville, Spain, Hausberger, S. (2002). "Simulation of Real World Vehicle Exhaust Emissions," Postdoctoral Thesis, Graz University of Technology, Graz, Austria. Houghton, N., and McRobert, J. (1998). "Towards a methodology for comparative resource consumption: modal implications for the freight task." ARR 318, Australian Road Research Boarch Transport Research Ltd. Hoyt, E. V., and Levary, R. R. (1990). "Assessing the effects of several variables on freight train fuel consumption and performance using a train performance simulator." Transportation Research A, 24 A(2), IFEU. (2002). "OMIT-Manual for environmental calculation of international freight transport." 43, Institute for Energy and Environmental research. IFEU, and SGKV. (2002). "Comparative analysis of energy consumption and CO 2 emissions of road transport and combined transport road/rail." Institute for Energy and Environmental research (IFEU) and Association for Study of Combined Transport (SGKV). Jones, C., and Rowat, B. (2003). "A case for investment in rail and intermodal transport." Jorgensen, M. W., and Sorenson, S. C. (1998). "Estimating emissions from railway traffic." International Journal of vehicle design, 20( ), Kagami, M., Akasaka, Y., Date, K., and Maeda, T. (1984). "The influence of fuel properties on the performance of Japanese Automotive Diesels: Diesel engine combustion and emissions." SP-581, Society of Automotive Engineers. Kent, J. H., and Mudford, N. R. (1979). "Motor vehicle emissions and fuel consumption modelling." Transportation Research, 13(A), Kraay, D., Harker, P. T., and Chen, B. (1991). "Optimal pacing of trains in freight railroads: Model formulation and solution." Operations Research, 39(1), Laird, P. (2003)a "Australian transport and greenhouse gas reduction targets." Australasian Transport Research Forum. Laird, P. (2003)b "Greenhouse gas reductions from track upgrading." Paper for conference on railway engineering, Adelaide. Laird, P. (2003)c "Literature survey re energy use in Australian transport." Rail Transport Energy Efficiency and Sustainability CRC for Railway Engineering and Technologies, University of Wollongong. References iv

158 Laird, P., and Adorni-Braccessi, G. (1993). "Land freight transport energy evaluation main report-part One." University of Wollongong, Wollongong. Laird, P., Michell, M., and Adorni-Braccessi, G. "Sydney-Canberra-Melbourne high speed train option." 25 th Australian Transport Research Forum, Incorporating the BTRE Transport Policy Colloquium, Canberra. Lukaszewicz, P. (2001). "Energy consumption and running time for trains. Modelling of running resistance and driver behaviour based on full scale testing," Doctoral thesis, Royal Institute of Technology, Stockholm. Mahoney, J. H. (1985). Intermodal freight transportation, ENO Foundation for transportation, Westport, Connecticut. Maunsell. (1998). "Toowoomba Region Transport Network Study." Mausell Proprietary Limited and Department of Main Roads. Meibom, P. (2001). "Technology analysis of public transport modes," Final report for an industrial PhD fellowship, Technical University of Denmark. Murtishaw, S., and Schipper, L. (2001). "Disaggregated analysis of US energy consumption in the 1990s: evidence of the effects of the internet and rapid economic growth." Energy Policy, 29(15), Parajuli, A., Ferreira, L., and Bunker, J. "Freight Modal Transport Energy Efficiency: A Comparison Model." Conference of Australian Institutes of Transport Research, Adelaide. Passmore, M. A., and Jenkins, E. G. (1998). "A comparison of the coast down and steady state torque methods of estimating vehicle drag forces." SAE , Society of Automotive Engineers. Patel, V. (1999). "Cetane number of New Zealand diesel." ISBN , Office of Chief Gas Engineer, Ministry of Commerce, Wellington. PMFTS. (2000). "Perth Metropolitan Freight Transport Strategy." Perth. Post, K., Kent, J. H., Tomlin, J., and Carruthers, N. (1984). "Fuel consumption and emission modelling by power demand and a comparison with other models." Transportation Research A, 18 A(3), QR. (2001). "Western System Information Pack." Issue 1, Queensland Rail Network Access. QT. (2001). "Guidelines for multi-combination vehicles in Queensland, Form number 1, Version 4." Queensland Transport, Brisbane. R.A., W., Downing, B. R., and Pearce, T. C. (1981). "Energy consumption of an electric, a petrol and a diesel powered light goods vehicle in central London References v

159 traffic." TRRL Laboratory Report 1021, Transport and Road Research Laboratory, Crowthorne, Berkshire. RAC. (2001). "Trends in freight energy use and green house gas emission " The Railway Association of Canada. Richardson, A. J. (2001). "Never mind the Data - Feel the Model A Keynote Paper." International Conference on Transport Survey Quality, Kruger National Park, South Africa. Robl, E. H. (2002). Understanding intermodal: A portable primer on today's multimodal transportation equipment and systems, Durham, NC. Shayler, P. J., Chick, J., Darnton, N. J., and Eade, D. "Generic functions for fuel consumption and engine out emissions of HC, CO and NO x of spark-ignitiion engines." Proceedings of the Institute of Mechanical Engineer. Short, J. "Road freight transport in Europe. Some policy concerns and challenges. European road freight transport in the third millenium." European conference of Minister of Transport, Verona. Sigut, J. (1995). "Road-rail intermodal terminals: Modelling of operating performance," Master Thesis, Queensland University of Technology, Brisbane. Sorenson, S. C. (1998). "Future non road emissions." Technical University of Denmark, Lyngby. Steven, R. K. (2002). "Cross border rail freight transportation: barriers and incentives." Number E-C048, Transportation Research Board of National Academics, Washington D.C. Thoresen, T. (1988). "Review of NIMPAC car fuel consumption algorithms." AIR 384-2, Australian Road Research Board, Vermont South, Victoria. Thoresen, T. (1993). "Survey of freight vehicle costs-1991." ARR 239, Australian Road Research Board, Victoria. Thoresen, T. (2003). "Economic Evaluation of Road Investment Proposals: Harmonisation of Non-Urban Road User Cost Models." AUSTROADS, Sydney. Thoresen, T., and Roper, R. (1996). "Review and enhancement of vehicle operating cost models: Assessment of non urban evaluation models." ARR 279, Australian Road Research Board, Victoria. Tomita, Y. (1997) "An energy consumption model for freight transport in Japan." Urban Transport and the Environment for the 21 st century III, Acquasparta, Italy. Tong, H. Y., Hung, W. T., and Cheung, C. S. (2000). "On road motor vehicle emissions and fuel consumption in urban driving conditions." Journal of the Air and Waste Management Association, 50. References vi

160 Trafico, and ETCAE. (2001). "Road freight transport and the environment in mountainous areas." Technical Report No. 68, Trafico and European Topic Centre Air Emissions. European Environmental Agency, Copenhagen. Van Arkel, W. G., Eichhammer, W., Harmsen, R., and Kets, A. (2002). "Energy efficiency in the Netherlands ( )." ECN Policy Studies, Petten. Vanek, F. M., and Morlok, E. K. (2000). "Improving the energy efficiency of freight in the United States through commodity-based analysis: Justification and implementation." Transportation Research D: Transport and Environment, 5(1), Wang, M. Q. (2000). "GREET 1.5a - Transportation Fuel Cycle Model: Methodology, Use and Results." ANL/ESD-39, Centre for Transportation Research, Energy System Division, Argonne National Laboratory. Wang, W. G., Lyons, D. W., Clark, N. N., and Luo, J. D. (1999). "Energy consumption analysis of heavy duty vehicles for transient emissions evaluation on Chassis Dynamometer." Proceedings of the I MECH E Part D Journal of Automobile Engineering, 213(3), Wang, W. G., Palmer, G. M., Bata, R. M., Clark, N. N., Gautam, M., and Lyons, D. W. (1992). "Determination of heavy duty vehicle energy consumption by a Chassis Dynamometer." SAE Technical Paper Series , International Truck and Bus Meeting and Exposition, Society of Automotive Engineers, Toledo, Ohio. West, B. H., McGill, R. N., Hodgson, J. W., Sluder, C. S., and Smith, D. E. (1997). "Development of data based light duty modal emissions and fuel consumption models." SAE Technical Paper Series , International Fall Fuels and Lubricants Meeting and Exposition, Society of Automotive Engineers, Tulsa, Oklahoma. Williams, T. (1977). "Energy losses in heavy commercial vehicles." TRRL Supplementary Report 329, Transport and Road Research Laboratory, Crowthorne, Berkshire. References vii

161 Appendix A Commodity Classification Commodity classification * Pack Classification Bulk Containerised Other freight Type of Commodity 1 Food and Live Animals Live animals Meat and meat preparations Dairy products and eggs Fish, crustaceans and molluscs and preparations thereof Cereals and cereal preparations Fruit and vegetables; sugar cane Sugar, sugar preparations and honey Feeding stuff for animals (exc unmilled cereals) Coffee, tea, cocoa, spices, margarine and miscellaneous edible products 2 Beverages and Tobacco Beverages Tobacco 3 Crude materials, inedible, except fuels Hides, skins and furskins, raw Oil seeds, oil nuts and oil kernels Crude rubber (inc synthetics and reclaimed) Wood, timber and cork Pulp and waste paper Textile fibres (other than wool tops) and their wastes (not manufactured into yarn or fabric) Crude fertilizers and crude materials (exc coal, petroleum and precious stones) Metalliferous ores and metal scrap Crude animal and vegetable materials not elsewhere specified Dry/Solid Liquid (inc slurry or melted) Gas (inc liquified gas) 6m 12m Other length Unitised Livestock (Uncrated) Vehicles/ Crafts (Empty) Other Appendix A 1

162 Appendix A Commodity Classification Pack Classification Bulk Containerised Other freight Type of Commodity 4 Mineral fuels, lubricants and related materials Coal, coke and briquettes Petroleum, petroleum products and related materials Gases, natural and manufactured 5 Animal and vegetable oils, fats and waxes Animal oils and fats Fixed vegetable oils and fats Animal and vegetable oils and fats, processed, and waxes of animal or vegetable origin 6 Chemical related products not elsewhere specified Organic and inorganic chemicals Dyeing, tanning and colouring materials Medicinal and pharmaceutical products Essential oils and perfume materials; toilet, polishing and cleansing preparations Fertilizers, manufactured Plastic materials, artificial resins and cellulose esters and ethers Explosives and other chemical materials and products Dry/Solid Liquid (inc slurry or melted) Gas (inc liquified gas) 6m 12m Other length Unitised Livestock (Uncrated) Vehicles/ Crafts (Empty) Other Appendix A 2

163 Appendix A Commodity Classification Pack Classification Bulk Containerised Other freight Type of Commodity 7 Manufactured goods classified chiefly by material Leather, leather manufactures not elsewhere specified and dressed furskins Rubber manufactures not elsewhere specified Cork and wood manufactures (exc furniture) Paper, paperboard and articles of paper pulp, of paper or of paperboard Textile yarn, fabrics, made-up articles not elsewhere specified and related products Non-metallic mineral manufactures not elsewhere specified Iron and steel Non-ferrous metals Manufactures of metal not elsewhere specified 8 Machinery and transport equipment Machinery, equipment, apparatus and appliances Road vehicles and other transport equipment 9 Miscellaneous manufactured articles Furniture and parts thereof Articles of apparel and clothing accessories and footwear Professional, scientific and controlling apparatus not elsewhere specified; photographic apparatus, equipment and supplies; optical goods not elsewhere specified; watches and clocks Printed matter, plastic wares, toys and other miscellaneous manufactured articles Dry/Solid Liquid (inc slurry or melted) Gas (inc liquified gas) 6m 12m Other length Unitised Livestock (Uncrated) Vehicles/ Crafts (Empty) Other Appendix A 3

164 Appendix A Commodity Classification Pack Classification Bulk Containerised Other freight Type of Commodity 10 Commodities and transactions not elsewhere specified Mail and postal packages, not classified by commodity Water Special transactions and commodities not classified by kind Animals, live not elsewhere specified Armoured fighting vehicles, arms of war and ammunition therefore; parts of arms not elsewhere specified Coins (other than gold coin) not being legal tender Gold, non-monetary Coins ( being legal tender); ships, boats and floating structures operating temporarily in Australian waters Dry/Solid Liquid (inc slurry or melted) Gas (inc liquified gas) 6m 12m Other length Unitised Livestock (Uncrated) Vehicles/ Crafts (Empty) Other Note: These commodity classification links are in the early stage in the tool developed and need to further developed. Appendix A 4

165 Appendix B Representative vehicles and their characteristics Table Representative vehicles and their characteristics Vehicle Category Maximum Mass GCM (tonnes) Effective Mass GVM/GCM (tonnes) Number of Wheels Fuel P = Petrol D = Diesel Engine Power (kw) Aerodynamic Drag (CD) Frontal Area (Sq m) Basic fuel consumption equation coefficient A B C 1 Utility (2 axle 4 tyre) P , Light commercial van Petrol [P] P , Light truck (2 axle 6 tyre) Petrol [P] P , Light truck (2 axle 6 tyre) Diesel [D] D , Medium truck (2 axle 6 tyre) 8 6 D , Heavy Rigid Truck (3 axle) , Rigid or Articulated 3 Axle Truck D , Articulated truck - 4 Axle D , Articulated Truck - 5 Axle 18 D , Articulated Truck - 6 Axle D , Rigid (3 axle) + 5 Axle Dog Trailer D , Twinsteer + 4 Axle Dog Trailer D , Twinsteer + 5 Axle Dog Trailer D , B double Combination D , Road Train (double) D , A B Combination D , Road Train (triple) D , B Triple Combination D , Double B Double Combination D , Source: Thoresen (2003) Appendix B

166 Table Vehicle No Appendix C Gradient Adjustment Factors Gradient Adjustment Factors Vehicle Type Gradient Speed (km/h) Category Utility 4% (2 axles, 6% tyres) 8% % Light 4% commercial 6% van 8% % Light truck 4% (2 axles, 6% tyres) 8% Petrol [P] 10% Light truck 4% (2 axles, 6% tyres) 8% Diesel [D] 10% Medium 4% truck 6% (2 axles, 8% tyres) 10% Large truck 4% (3 axles, 6% tyres) 8% % Articulated 4% axle 6% truck 8% % Articulated 4% axle 6% truck 8% % Articulated 4% axle 6% truck 8% % Articulated 4% axle 6% truck 8% % Large truck 4% (rigid 3 axle) 6% axle 8% dog trailer 10% Twin steer 4% truck + 6% axle 8% dog trailer 10% Twin steer 4% truck + 6% axle 8% dog trailer 10% Appendix C 1

167 Vehicle No Appendix C Gradient Adjustment Factors Vehicle Type Gradient Speed (km/h) Category B Double 4% (tandem-tri) 6% % % Road train 4% (double) 6% % % A B 4% Combination 6% % % Road train 4% (triple) 6% % % B Triple 4% % % % Double B 4% Double 6% % % Source: Thoresen (2003) Appendix C 2

168 Appendix D Roughness Adjustment Factors Table Fuel Consumption Road Roughness Adjustment Factors (FCGRVF) Stereotype Designation Vehicle Stereotype Speed (km/h) Utility (2 axles, 4 tyres) Light commercial van Light truck (2 axles, 6 tyres), Petrol [P] Light truck (2 axles, 6 tyres), Diesel [D] Medium truck (2 axles, 6 tyres) Large truck (3 axles, 10 tyres) Articulated 3 axle truck Articulated 4 axle truck Articulated 5 axle truck Articulated 6 axle truck Large truck (rigid 3 axle) + 5 axle dog trailer Twin steer truck + 4 axle dog trailer Twin steer truck + 5 axle dog trailer B Double Road train (double) A B Combination Road train (triple) B Triple Double B Double Appendix D 1

169 Appendix E Spreadsheet Tool Description and Users Guide Appendix (Description of Spreadsheet Tool) Appendix E 0

170 Appendix E Spreadsheet Tool Description and Users Guide Input Freight Characteristics Sheet The Input Freight Characteristics sheet allows the user to define, and later identify, the freight characteristics such as type of packing, size of freight and type of commodity. These parameters are to be tallied at first so the user is better informed about the number of containers required to carry the commodity and trips generated for the task. The main aim of this sheet is to make an allowance for such judgement by informing users about the available volume and freight volume. OMIT, a tool developed to calculate the energy consumption and emissions for international freight transport to and from Denmark, has also acknowledged the importance of volume in heavy vehicle transport where the density of the load is less than 333 kg per m 3 (IFEU 2002). Australian Bureau of Statistic classifications, namely Australian Transport Freight Commodity (ATFCC) and Australian pack classification (APC) have been adopted for commodity and freight classification. The ATFCC classifies goods carried by type of commodity while the Australian Pack Classification APC classifies cargo by its pack characteristics, e.g. `in bulk' or `containerised'. A code is to be entered in the identification code cell so as to later identify the movement option/number. On the right of the code identification cell, there is a place to enter the origin place of the freight and destination of the freight, such as Brisbane and Adelaide. Input Road Sheet The Input Road sheet allows the user to input the freight movement characteristics of the pickup, road line haul and delivery sections for each forward and backhaul movement. Backhaul movement will only be considered in the energy efficiency calculation if the data are provided there. Otherwise the comparison would be based on forward movement of the freight which means the tool does not assume full, half or empty backhaul movement on its own. Appendix E 1

171 Appendix E Spreadsheet Tool Description and Users Guide Option code On top left hand corner of the sheet, there is a cell allotted for option code input. The input parameters of this cell would be used to later identify the particular movement among different options involved in the freight movement such as use of B double instead of several semi trailers. Section division Both forward and backhaul movement of freight has been divided into three portions. They are; Pickup (PU) Road Line Haul (RoLH) Delivery (De) Figure E-1 Route division PU06-10 PU01-05 PU11-15 Pick up RoLH Road line haul (RoLH) Road line haul (RoLH) De01-05 De06-10 De11-15 Delivery The pickup section could be identified by abbreviation PU and similarly RoLH for road line haul and De for delivery. In addition, when B accompanies those abbreviations (such as B-PU, B-RoLH and B-De) then it is meant to denote backhaul movement. Hence B-PU means pickup section for backhaul movement. The pickup and delivery have the same type of movement nature. Hence the input sections of pickup and delivery movements are similar. Appendix E 2

172 Appendix E Spreadsheet Tool Description and Users Guide Each pickup and delivery movement is divided into 15 tables, each table corresponds to a movement of a single pick up leg. Table - Pickup Leg1/Delivery Leg 1 (PU01-PU05) This is the first of the 15 tables to input operating characteristics of pickup legs. These type of tables are also used here as an input frame for delivery leg s details, and for both forward and backward movement. This single table is designed to accommodate operating characteristics of single pickup/delivery leg. It would be possible to change the vehicle type even within a single pickup/delivery leg for occasions where vehicles are changed even within one pickup/delivery leg. If there are 11 pickup legs then the user will input operating characteristics in 11 tables and leave the rest empty. Same is true in the case of delivery movement. Table Road line haul (RoLH01- RoLH15) Road line haul movement has been divided into three sections to accommodate maximum of three vehicle combination types comprising one fleet. Each section (distinguished by writing First/second/third of the 3 vehicles in the freight traffic fleet ) is to accommodate the movement data of a single freight movement. The tool only could accommodate three vehicles for one line haul freight movement. Rows and columns of tables Road line haul and Pickup/delivery Rows Each movement is to be divided into homogeneous operation based on similar traffic and terrain characteristics. Each segregated movement is to be entered in a single row of the spreadsheet. For example, if the vehicle travelled at a speed of 60 km/h for the whole trip length then also the trip is to be segregated based on the grade, curvature and congestion condition of the road. These segregated segments are to be input in a separate row. Appendix E 3

173 Appendix E Spreadsheet Tool Description and Users Guide Columns First column of the table (Road line haul, pickup and delivery) contains the unique ID assigned to each segmented task, for example PU03 denotes the third portion of the first pickup leg for forward movement and B-De09 denotes the forth portion of the second delivery leg for backhaul movement. This ID number helps to later identify the energy consumed in that particular section. Second column of the table (Road line haul, pickup and delivery) holds a place to choose a freight vehicle of that section. Whenever a mouse is pointed in those cells there appears a list of vehicles. A number corresponding to the type of vehicle being used is to be entered in the cells of second column. Third column of the table (Road line haul, pickup and delivery) enable input of specific energy consumption (MJ/net tonne-km) of that movement. It is recommended to input the values in the cells (of third column) only in the case of high confidence in specific energy consumption data (known in advance) of that particular section and vehicle type. Whenever any values are input in these cells, the program overwrites the calculated value with the data mentioned in the cells. Fourth column of the table (Road line haul, pickup and delivery) contains cells to input length (in km) of the travel segment. As discussed above, an entire pickup/line haul/delivery travel is divided into homogeneous section. The user is to input the length of each of such homogeneous section in different cells. Fifth column of the table (Road line haul, pickup and delivery) holds a place to input travel speed (in km/h) of that particular homogeneous section being considered in that row. Similarly, sixth, seventh, eighth, ninth and tenth columns of the table (Road line haul, pickup and delivery) holds a place to input payload, volume to capacity ratio, grade percent, curvature and roughness (NRM counts/km) respectively of that particular homogeneous section being considered in that row. Eleventh and twelfth columns enclose rooms to input starting point and ending point of each homogeneous section. For example, if a vehicle is travelling a constant speed from ABC to CDF and then from CDF to EFG, even though the vehicle maintains the Appendix E 4

174 Appendix E Spreadsheet Tool Description and Users Guide same speed, but there is a rise in grade. In such case, the travel segment is divided into two portions and ending point s name (CDF in above example) of first portion will be the same as starting portion of the second section portion. Table intramodal transfer The table has four rows to accommodate four transfer processes in one way movement (forward or backhaul). The freight transferring process (from one vehicle to another) consumes energy as it involves lifting and stacking. The specific energy consumption (MJ/kg or MJ/container) for these processes are open for user input. In the case where the users are not aware of the value, the tool uses default values. The spreadsheet tool gives priority to the MJ/Container value for the estimation of energy consumption in transfer process. The first column of the table (intramodal transfer) is to contain the ID of two sections. These two are the sections between which the transfer process occurs/occurred. For example, if there is a transfer of freight from pickup section (PU15) directly into road line haul section (RoLH01), then the first column should contain PU15 RoLH01. The second column of the table (intramodal transfer) is to contain the exact name of the transfer location, such as the Port of Brisbane. The third and forth columns of the table (intramodal transfer) is to contain the mass involved in the transfer process and container involved in the transfer process respectively. The fifth and sixth columns of the table are open for users if they opt to overwrite the default freight transfer specific value in MJ/kg and MJ/container unit respectively. Input Rail Sheet Rail line haul movement is expected to be accompanied by road legs as discussed previously. The input framework of the road movement segment in Input Rail Sheet is same as in pickup and delivery section of Input Road Sheet. Appendix E 5

175 Appendix E Spreadsheet Tool Description and Users Guide However, in the input rail sheet users can only input the operating characteristics of three pick up/delivery vehicles at once. The input room to enter operating characteristics of each pick up/delivery vehicle is separated by variation in colour. The table following the input pickup table is input table for intermodal transfer. This intermodal transfer table is similar to the intermodal transfer table discussed earlier in case of road transport. Hence, the readers are directed to above section for more detail information about intermodal transfer table. However, unique to the rail operating characteristics, there is a room to enter the shunting energy demand also. The cell allotted for this purpose is few rows below the room allotted to input intermodal transfer detail. Rail line Haul Table The rail line haul table has 140 rows. Each row is to be separated by the change in operating characteristics to the train. These operating characteristics of the train are to be input in the same table, ranging from column 2 to column 5. The first column contains a unique ID assign to each rail line haul movement. These assigned ID are not for users to change. They would help users to later identify the freight moving section. Second column contains space to input train speed, third and fourth column contains room to input route characteristics such as grade and curvature. There are hints provided for proper input of grade and curvature value. Fifth column contains the space allotted for input of distance value between the points of whose operating characteristics are entered in that row. The other adjoining table, at the right side of the rail line haul operating characteristics table, is the input table to enter physical properties of rail. It contains the space for input of train length, efficiency, number of wagons, etc. These are the parameters assumed to remain same for the entire freight movement under consideration. This rail line haul table is followed by intermodal transfer table, which is already discussed above. This intermodal transfer table is followed by delivery leg table. The delivery leg input table is similar to pick up leg input table. Appendix E 6

176 Appendix E Spreadsheet Tool Description and Users Guide Vehicle Characteristics Sheet A set of representative vehicles for road were chosen. The characteristics include vehicle mass, drag area and friction area. For any type of unique vehicle set not included in vehicle characteristics sheet the default value may not give a good estimate of fuel consumption. In such cases the default values could be overwritten by user specified value, provided the user have a good set of data describing the fuel consumption of the chosen vehicle set. Those data are to be used in input sheets rather than vehicle characteristics sheet. Lookup tables Sheet Lookup table sheet contains the information needed to quantify the effect of adjustment factors such as curvature, grade, engine efficiency, roughness and congestion on road fuel consumption. The corresponding data from these tables will be selected to aid in computing fuel consumption. Calculation In calculation sheet, the data from input sheets are used and computed along with data from the lookup table sheet. The sheet contains the necessary instruction to match the input data and data from lookup table. After extracting the information from all the relevant sheets, fuel consumption for the specified section is computed in the calculation sheet and sent to output sheets. Generally users are not to alter the settings and formula of this sheet. Output Road Sheet Output Road Sheet accepts the data from corresponding Input Sheets and Calculation Sheet and display the amount of fuel consumed on each trip segment. The sheet also tabulates the parameters considered for estimating energy consumption and their relative impact ratio. The Output Road Sheet uses the similar format of Input Road Sheet. The Output Road Sheet portrays the fuel consumption figure of each divided route section and energy consumed in transfer process. Appendix E 7

177 Appendix E Spreadsheet Tool Description and Users Guide The first column contains the ID number of travel segment which is same as in Input Road Sheet. The second column shows type of vehicle as per the number selected in Input Road Sheet. Column number three to fifteen shows the different parameters being considered during fuel consumption estimation and their relative magnitude. Column sixteen contains the estimated value of fuel consumed during that particular travelling (for each homogeneous section distinguished by different rows). Output Rail Sheet There is a pickup and delivery leg s fuel consumption description which is expected to be accompanied by road. Hence these sections of Output Rail Sheet are similar to that of Output Road Sheet. The energy performance for the set of operating and train characteristics input in Input Rail Sheet is presented in Rail Line Hail Output Table. The performance of values entered in each row (representing an each segment on the ground) could be identified based on the unique ID (such as RaLH10) and start and end point description made in Input Rail Sheet. A separate table portrays estimated energy consumption for the transferring of freight between two modes. Summary Sheet Summary sheet accepts the energy consumption value estimated in calculation sheet and presented in corresponding Output Sheets and makes the comparison between the options provided (two options at a time, involving road/rail and road). A separate column in the Summary Sheet portrays the effect of full fuel cycle consideration in comparison differentiating the diesel and electricity powered freight movement. The terms in summary sheet are self explanatory and all the values shown are based on the estimated values and user input values. The users are not to enter any values and change the settings of this sheet. NOTE: Not all the subroutine of this spreadsheet has been fully developed. Appendix E 8

178 Appendix F Spreadsheet Tool A CD Appendix F 1

179 Appendix G Vehicle Simulator Result (A Sample) after processing Speed (km/h) GVM Payload Less than tare weight Appendix G 1

180 Appendix G A Screen Capture of Vehicle Run Simulator (Design Pro) Figure G-1 Screen Capture of Design Pro Vehicle Simulator Appendix G 2