Appendix B5 PT Model Validation

Transcription

1 Appendix B5 PT Model Validation

2 PT Model Validation Report Sheffield and Rotherham Public Transport Model Report for Sheffield City Council August 2009

3 Document Control Project Title: MVA Project Number: Document Type: Directory & File Name: Sheffield and Rotherham Public Transport Model C37688 Report C:\Documents And Settings\Jblythe\My Documents\Workingfiles\Workspace.Mva.Co.Uk\PT Model Validation Model.Doc Document Approval Primary Author: James Blythe Other Author(s): Reviewer(s): John Allan Formatted by: Distribution Issue Date Distribution Comments 1 19/06/09 John Allan Draft for Review 2 09/07/07 Julie Meese Draft for Review 3 04/08/09 Final Report 4 28/09/09 Updated Final Report

4 Contents 1 Introduction Background Sheffield and Rotherham Public Transport Model Contents of this Report Overview DfT Guidance on Validation Approach to Calibration and Validation Model Specification Introduction Cube Voyager Study Area and Zoning Methodology Modelled Time Periods Network Definition Fares Matrix Development Assignment Parameters Modification to Assignment Parameters Data Used Introduction Supply Data Demand Data Calibration and Validation Data Network Validation Introduction Network Checks Public Transport Service Validation Vehicle Flows Route Choice Conclusions Matrix Validation Introduction Comparison of Matrices with Count Data Comparison of Matrix with Annual Patronage Estimates from other sources Conclusions 6.3 Sheffield and Rotherham Public Transport Model 1

5 Contents 7 Assignment Validation Prior to Matrix Estimation Introduction Assignment Parameters Bus Occupancy Validation Supertram Occupancy Validation Rail Boarding and Alighting Validation Conclusions Assignment Validation After Matrix Estimation Introduction Matrix Estimation Effect of Matrix Estimation Bus Occupancy Validation Supertram Occupancy Validation Rail Boarding and Alighting Validation Conclusions Assignment Validation With Crowding Introduction Bus Occupancy Validation Supertram Occupancy Validation Rail Boarding and Alighting Validation Conclusions Conclusions Overview Demand Data Supply Data Model Parameters and Algorithms Validation Conclusion 10.2 Sheffield and Rotherham Public Transport Model 2

6 Contents Tables Table 2.1 DMRB Validation Report Structure 2.2 Table 3.1 Bus Time Factors 3.5 Table 5.1 Comparison of Bus Journey Times with Observed Data 5.3 Table 5.2 Morning Peak Public Transport Sub-Mode Choice for Selected Journeys 5.5 Table 6.1 Modelled and Observed Cordon Crossing Comparisons 6.2 Table 6.2 Matrix Validation 24 Hour Public Transport Demand 6.3 Table 7.1 Assignment Parameters 7.3 Table 7.2 Percentage Difference Between Screenline and Cordon Flows and Observed Counts - Prior to Matrix Estimation 7.5 Table 7.3 Percentage of Screenline Links with an Observed Flow Greater than 150 where Modelled Flow is within 25% of Observed Flows Prior to Matrix Estimation 7.6 Table 7.4 Percentage Difference Between Modelled and Observed Supertram Cordon Flows Prior to Matrix Estimation 7.7 Table 7.5 Percentage of Supertram Links with an Observed Flow Greater than 150 where Modelled Flow is within 25% of Observed Flows Prior to Matrix Estimation 7.7 Table 7.6 Percentage Difference between Modelled and Observed Passengers Entering and Leaving Stations Prior to Matrix Estimation 7.8 Table 7.7 Percentage of Rail Links with an Observed Flow Greater than 150 where Modelled Flow is within 25% of Observed Flows Prior to Matrix Estimation 7.8 Table 8.1 Matrix Estimation Confidence Levels 8.2 Table 8.2 Matrix Totals Before and After Matrix Estimation 8.3 Table 8.3 Mean Trip Lengths Before and After Matrix Estimation (kilometres) 8.5 Table 8.4 Percentage Difference Between Screenline and Cordon Flows and Observed Counts After Matrix Estimation 8.6 Table 8.5 Percentage of Screenline Links with Observed Flow Greater than 150 where Modelled Flow is within 25% of Observed Flows After Matrix Estimation 8.7 Table 8.6 Percentage Difference Between Modelled and Observed Supertram Cordon Flows After Matrix Estimation 8.8 Table 8.7 Percentage of Supertram links with an Observed Flow Greater than 150 where Modelled Flow is within 25% of Observed Flows After Matrix Estimation 8.8 Table 8.8 Percentage Difference between Modelled and Observed Passengers Entering and Leaving Stations After Matrix Estimation 8.9 Table 8.9 Percentage of Rail Links with an Observed Flow Greater than 150 where Modelled Flow is within 25% of Observed Flow After Matrix Estimation 8.9 Table 9.1 Percentage Difference Between Screenline and Cordon Flows and Observed Counts After Matrix Estimation 9.2 Table 9.2 Percentage of Screenline Links with Observed Flow Greater than 150 where Modelled Flow is within 25% of Observed Flows After Matrix Estimation 9.3 Table 9.3 Percentage Difference Between Modelled and Observed Supertram Cordon Flows After Matrix Estimation 9.4 Table 9.4 Percentage of Supertram links with an Observed Flow Greater than 150 where Modelled Flow is within 25% of Observed Flows After Matrix Estimation 9.4 Table 9.5 Percentage Difference between Modelled and Observed Passengers Entering and Leaving Stations After Matrix Estimation 9.5 Table 9.6 Percentage of Rail Links with an Observed Flow Greater than 150 where Modelled Flow is within 25% of Observed Flow After Matrix Estimation 9.5 Sheffield and Rotherham Public Transport Model 3

7 Contents Figures Figure 3.1 Sheffield and Rotherham Transport Model Zoning System 3.2 Figure 3.2 Model Zones in Sheffield City Centre 3.3 Figure 3.3 Model Zones between Sheffield and Rotherham Urban Centres 3.3 Figure 3.4 Model Zones along Penistone Road Corridor 3.4 Figure 3.5 Bus and Rail Fare Tables 3.9 Figure 3.6 Public Transport Wait Curve (All Services) 3.12 Figure 4.1 Public Transport Survey Locations in Sheffield City Centre 4.3 Figure 4.2 Public Transport Survey Locations in Rotherham Town Centre 4.3 Figure 4.3 Bus and Tram Occupancy Survey Locations 4.5 Figure 4.4 Rail Station Entry and Exit Count Locations 4.6 Figure 5.1 Comparison of Morning Peak Observed and Modelled Bus Vehicle Flows 5.3 Figure 5.2 Comparison of Inter-peak Observed and Modelled Bus Vehicle Flows 5.4 Figure 5.3 Comparison of Evening Peak Observed and Modelled Bus Vehicle Flows 5.4 Figure 8.1 Morning Peak Trip Length Distributions 8.3 Figure 8.2 Inter-peak Trip Length Distributions 8.4 Figure 8.3 Evening Peak Trip Length Distributions 8.4 Appendices A B C D E F G H I J K L M Summary of Public Transport Services Average Fare Calculation Creating Matrices from the Observed Public Transport Survey Data Infilling Unobserved Movements using the Gravity Model Value of Time Calculation Derivation of Wait Curves Pre-Matrix Estimation Validation Tables Post-Matrix Estimation Validation Tables Impact of Matrix Estimation Derivation of Crowding Curves Crowding Model Validation Tables Voyager Public Transport Processes and Algorithms Sheffield and Rotherham Bus Rapid Transit: Stated Preference Research Sheffield and Rotherham Public Transport Model 4

8 Summary Background In autumn 2008, Sheffield City Council (SCC) commissioned MVA to update the suite of models that form the Sheffield and Rotherham Multi Modal Model (SRTM). The purpose of this report is to set out the tasks and methodology undertaken to develop the VOYAGER public transport model from the existing TRIPS public transport model, and to undertake the calibration and validation of the updated public transport model. Fitness for Purpose This report demonstrates that the updated 2008 Sheffield and Rotherham Public Transport Model (SRPTM3) validates well against the criteria provided in the Department for Transport s Transport Analysis Guidance (TAG) and is therefore a robust tool for testing public transport schemes. The validation of the model has been carried out following the guidance given in TAG, and a summary of the findings of this report are set out below: The network and lines files have been validated to show that they reflect the public transport supply, modelled bus journey times replicated observed times, modelled bus vehicle flows match observed flows and modelled route choice is reasonable. Matrix validation checks reported here suggest that demand matrices are of the right order of magnitude. Model validation, following matrix estimation, satisfies the criteria for public transport model validation laid out in TAG. The model validates well in terms of bus occupancies, Supertram occupancies and the number of passengers entering and leaving rail stations and the matrix estimation process has resulted in only relatively minor changes to the distribution of demand. The model validates well after the application of the crowding model. Public Transport Supply The public transport supply representation in SRPTM3 has been developed using the following sources: the highway network and speeds are taken from the latest version of the Sheffield and Rotherham SATURN model (SRHM3); the rail, Supertram, walk links and centroid connectors have been taken from the previous version of the public transport model (SRPTM2); bus speeds have been calculated using highway speeds and calibrated to match observed times; public transport services have been taken from the latest timetable information; and public transport fares have been calculated from observed data in the South Yorkshire Concessionary Fares Monitoring surveys. Public Transport Demand The public transport demand has been developed using the following data and methods: Sheffield and Rotherham Public Transport Model i

9 Summary forward and reverse direction matrices have been created from the origin/destination surveys of passengers waiting to board services at bus stops, rail stations and Supertram stops in Sheffield City Centre, Rotherham Town Centre and Meadowhall; and the infilling of unobserved matrices has been undertaken using gravity model techniques, with target trip ends being calculated using TEMPRO trip rates. Voyager Public Transport Software SRPTM3 has been developed using the public transport module of Citilabs Cube Voyager software. The use of Cube Voyager allows for the smoothest transfer from the previous version of the model, with utilities to transfer networks and lines files from the TRIPS format already existing. The key features of the Cube Voyager public transport software are: the process of network simplification to create walk access and egress legs, as well as transfer legs between services, which allows for shorter route building and assignment run times; the derivation of a discrete set of routes between zone pairs; the evaluation of the reasonable routes between zone pairs, and the calculation of the probability of each route being used; the provision of methods to model highly complex fare structures, and mixtures of fare structures; the loading of trips to the network using the probabilities of using each route; and the modelling of crowding and capacity restraint, both in terms of the impact of travelling in crowded vehicles and the ability to board vehicles. Modelled Time Periods The base year for SRPTM3 is 2008, and the model covers three one hour time periods: Morning peak hour: Inter-peak hour: average of Evening peak hour: Assignment Parameters The assignment parameters used in the route enumeration and route evaluation processes have been developed using the following stages: the consideration of recommendations for assignment model parameters within TAG; the consideration of recommendations for assignment model parameters within the software manual; the consideration of assignment parameters used in the previous version of the model (SRPTM2); the use of stated preference research to determine in-vehicle time parameters; and Sheffield and Rotherham Public Transport Model ii

10 Summary the calibration of assignment model parameters following the comparison of modelled flows with observed counts. Sheffield and Rotherham Public Transport Model iii

11 1 Introduction 1.1 Background In autumn 2008, Sheffield City Council (SCC) commissioned MVA to update the suite of models that form the Sheffield and Rotherham Multi Modal Model (SRTM) The updated suite of models (SRTM3) was developed from the earlier suite of models (SRTM2), developed by MVA in July 2008, which comprises the following elements: a SATURN highway assignment model; a TRIPS public transport assignment model; a DIADEM demand model; and a park-and-ride sub-model SRTM3 comprises the following elements: a SATURN highway assignment model SRHM3; a VOYAGER public transport assignment model SRPTM3; and a bespoke demand model SRDM The purpose of this report is to set out the tasks and methodology undertaken to develop the VOYAGER public transport model from the existing TRIPS public transport model, and to undertake the calibration and validation of the updated public transport model. The updated public transport model is referred to throughout this report as SRPTM Sheffield and Rotherham Public Transport Model SRPTM3 was developed from the earlier version of the Sheffield and Rotherham Public Transport Model (SRPTM2), which was developed by MVA in July 2008 with a base year of The development of the model was reported in Sheffield and Rotherham Public Transport Model Validation Report An interim version of the model (SRPTM1 base year 2006) was built without the collection of new survey data, and was based heavily on demand data from the South and West Yorkshire Multi-Modal Study (SWYMMS) public transport model, developed in This interim model was developed as part of SRTM1 to support the Sheffield & Rotherham Bus Rapid Transit (Northern & Southern Routes) and Penistone Road Smart Route Outline Business Cases to the Regional Transport Board and to undertake initial option testing for the River Don District Masterplan SRPTM2 utilised new public transport surveys that were undertaken during 2007 to update the demand matrices, and featured an update to the public transport supply. SRPTM2 has been used to support initial option testing for the Sheffield & Rotherham Bus Rapid Transit (Northern & Southern Routes) and Penistone Road Smart Route Major Scheme Business Cases to the Department for Transport. It has also been used to develop proposals for other major transport / land-use related projects such as City Centre Masterplan Review Phase 1.. Sheffield and Rotherham Public Transport Model 1.1

12 1 Introduction The updated suite of models (SRTM3) will be used, in conjunction with a new South Yorkshire / Sheffield City Region Strategic Transport Model (SYSTM+), to support future transport policy and strategy development, post Local Transport Plan 2 (LTP2) Throughout the remainder of this note the versions of the Sheffield and Rotherham Models will be referred to as follows: SRTM1 The interim version of the model built in 2006, featuring a DIADEM demand model and TRIPS public transport model; SRTM2 - The final version of the model built in 2007, featuring a DIADEM demand model and TRIPS public transport model; and SRTM3 - The 2008 version of the model, featuring a TRAM demand model and Voyager public transport model. 1.3 Contents of this Report Following this introductory Chapter the remainder of the Report is structures as follows: Chapter 2 provides an overview of the requirements for calibration and validation of public transport models, and sets out our approach to calibration and validation for SRPTM3. Chapter 3 outlines the specification for the model. Chapter 4 outlines the data used in updating the model. Chapter 5 describes the validation of the public transport network. Chapter 6 describes the validation of the public transport matrices. Chapter 7 describes the validation of the model prior to the application of matrix estimation techniques. Chapter 8 describes the validation of the model following the application of matrix estimation techniques. Chapter 9 describes the validation of the model following the application of the crowding model. Sheffield and Rotherham Public Transport Model 1.2

13 2 Overview 2.1 DfT Guidance on Validation Model validation has followed the advice given in TAG Unit issued in January This unit provides detailed guidance relevant to all areas of road and public transport assignment modelling. The section of particular interest to the validation of public transport models is Section 10 The Validation of Public Transport Passenger Assignment Models. The recommended approach to validation can be summarised as: trip matrix validation comparison of sector-sector movements in the demand matrices with observations of passenger flows obtained across entire screenlines and cordons; network validation checks of the accuracy of the coded network; service validation checks of coded PT services and flows against observed counts and timetables; and assignment validation comparison of modelled and observed passenger flows across cordons and screenlines, and passengers boarding and alighting in urban centres The latest DfT guidance does not give advice on reporting structure. We have therefore followed earlier DfT guidance 1 which recommended that the validation report for public transport models follows the structure set out in Volume 12 Section 2 Appendix B of the Design Manual for Roads and Bridges (DMRB). The elements required of the validation report, as specified in DMRB, are set out in Table 2.1 along with the location of the information in this report. 1 Major Scheme Appraisal in Local Transport Plans Part 3: Detailed Guidance on Forecasting Models for Major Public Transport Schemes June 2002 Sheffield and Rotherham Public Transport Model 2.1

14 2 Overview Table 2.1 DMRB Validation Report Structure DMRB Requirement Location A description of the model and its development (including evidence of the fit achieved to calibration data, and a description of any sensitivity tests undertaken, and their results) Chapter 3 A description of the data used in building and validating the model Chapter 4 Evidence of the validity of the network employed, including journey times Chapter 5 A validation of the trip matrices employed Chapter 6 A validation of the trip assignment Chapters 7 and 8 A validation of any other special features (e.g. higher tier model inputs, trip end models, mode choice models etc) A present year validation, if appropriate Not Applicable Not Applicable DfT guidance on the following topics is summarised in the remainder of this section under the following headings: trip matrix validation; network validation; services validation; assignment validation; and calibration techniques. Trip Matrix Validation Comparison of sector-to-sector trips in matrices with observed cordon/screenline counts should be reported. It is recommended that at this level of aggregation the differences between assigned and counted flows should in 95% of cases be less than 15%. Network Validation The accuracy of the coded network geometry should be systematically reviewed. Services Validation Modelled public transport journey times should be compared with timetables. No specific guidance on what constitutes acceptable is offered in this regard Modelled flows of public transport vehicles should be compared with roadside counts. Sheffield and Rotherham Public Transport Model 2.2

15 2 Overview Assignment Validation The validation of the assignment model should involve comparing modelled and observed: passenger flows across screenlines and cordons, usually by public transport mode and sometimes at the level of individual service; and passengers boarding and alighting in urban centres Modelled screenline flows should be within 15% of the observed flows, while individual flows should be within 25% except where observed flows are particularly low (less than 150 passengers per hour). It is also recommended that a check between modelled and annual public transport patronage is carried out (where available) to demonstrate that the general scale of patronage is correct. Calibration Techniques TAG Unit recommends techniques with which the assignment model may be calibrated to produce a higher degree of validation. These are: adjustments to the zone centroid connector times and costs; adjustments to network and service details; adjustments to in vehicle time factors; adjustments to walk and wait time factors; adjustments to interchange penalties; adjustments to trip loading algorithm parameters; path building and trip loading algorithm changes; and segmentation of demand These are presented roughly in the order in which they should be considered. Any adjustments must be plausible TAG unit also states that matrix estimation may be used to adjust the trip matrices if the matrices do not validate satisfactorily. It recommendeds that the changes brought about by the matrix estimation process are examined to check for particular distortions. In the case of distortions being introduced into the matrices, then the count data being used as constraints should be checked for consistency. 2.2 Approach to Calibration and Validation One consideration that influenced the design of the public transport model was its application as an input to a strategic demand model being developed in support of Major Scheme Business Cases. It is also likely to be used to undertake initial assessments of more localised public transport investment and demand changes The demand matrices have been produced from the survey data undertaken between Spring and Autumn 2007, with unobserved movements infilled using Gravity Model techniques. Sheffield and Rotherham Public Transport Model 2.3

16 2 Overview Matrix estimation has been used within the Voyager transport modelling software suite to improve the fit between modelled and observed passenger flows In summary, our approach to model calibration and validation was as follows: Undertake network validation (checking of network geometry and service coding; bus and rail speeds; route choice and public transport mode share for selected journeys). Undertake matrix validation prior to matrix estimation (involving the comparison of sector-sector matrices with corresponding screenline counts and matrix totals with SYPTE patronage estimates. Undertake assignment validation prior to matrix estimation (comparison of modelled and observed bus occupancy; and rail and Supertram boarding and alighting counts). Review assignment parameters and algorithms as listed in paragraph to assess if validation could be improved. Apply matrix estimation. Repeat assignment validation. Sheffield and Rotherham Public Transport Model 2.4

17 3 Model Specification 3.1 Introduction This chapter sets out the structure of the public transport assignment model and the values of model parameters that have been used. The remainder of this chapter discusses: an introduction to the Cube Voyager public transport assignment software; the study area and extent of the model; modelled time periods; network definition; the representation of fares; matrix development methodology; Voyager public transport processes and algorithms; assignment parameters; wait curves used in assignment; crowding curves used in assignment; and further modifications of assignment parameters made during calibration. 3.2 Cube Voyager SRPTM3 has been developed using the public transport module of Citilabs Cube Voyager software. Previous version of the Sheffield and Rotherham Public Transport Model were built using TRIPS public transport software, which is no longer supported by Citilabs. Therefore, it was decided that the updated public transport model would be moved to the Cube Voyager software The use of Cube Voyager allows for the smoothest transfer from the previous version of the model, with utilities to transfer networks and lines files from the TRIPS format already existing. The key features of the Cube Voyager public transport software are listed below and the algorithms involved in the Voyager Public Transport processes are described in Appendix L: the process of network simplification to create walk access and egress legs, as well as transfer legs between services, allows for shorter route building and assignment run times; the derivation of a discrete set of routes between zone pairs; the evaluation of the reasonable routes between zone pairs, and the calculation of the probability of each route being used; the provision of methods to model highly complex fare structures, and mixtures of fare structures; the loading of trips to the network using the probabilities of using each route; and Sheffield and Rotherham Public Transport Model 3.1

18 3 Model Specification the modelling of crowding and capacity restraint, both in terms of the impact of travelling in crowded vehicles and the ability to board vehicles. 3.3 Study Area and Zoning Methodology The zone system used in the Sheffield and Rotherham Transport model (SRTM3) is common to all elements of the model; the highway model, the public transport model and the demand model. The zone system and study area, which covers the districts of Sheffield and Rotherham, are shown in Figure 3.1. The zone system is very fine in the centres of Sheffield and Rotherham, the zones in the centre of Sheffield being shown in Figure 3.2, and between the urban centres shown in Figure The zone system that has been employed in the SRTM3 suite of models has been developed from that used in SRTM2, with an additional 15 zones along the Penistone Road corridor mainly in the Hillsborough area. These amendments have been made as a result of detailed analysis along this important corridor, which will be subject to a Major Scheme Business Case submission in the future. The additional zones included in SRTM3 mean that there are a total of 525 zones within the model, of which 496 are within the study area. The revised zone system on the Penistone Road corridor is shown in Figure 3.4. Figure 3.1 Sheffield and Rotherham Transport Model Zoning System Sheffield and Rotherham Public Transport Model 3.2

19 3 Model Specification Figure 3.2 Model Zones in Sheffield City Centre Figure 3.3 Model Zones between Sheffield and Rotherham Urban Centres Sheffield and Rotherham Public Transport Model 3.3

20 3 Model Specification Figure 3.4 Model Zones along Penistone Road Corridor 3.4 Modelled Time Periods The base year for SRPTM3 is 2008, and the model covers three one hour time periods: Morning peak hour: Inter-peak hour: average of Evening peak hour: Network Definition The network used for the Voyager public transport network in SRPTM3 has been developed from the latest SATURN network (SRHM3) and from the SRPTM2 public transport networks. The process employed to create the networks is outlined below: the SRHM3 SATURN model was used to create a file containing link and node information, including distance and time information, which was used to build the public transport network in Voyager; the rail, Supertram and walk links from SRPTM2 were converted to a format suitable for input to Voyager and reviewed and amended where necessary; and the centroid connectors from SRPTM2 were converted to a format suitable for input to Voyager and reviewed and amended where necessary. Sheffield and Rotherham Public Transport Model 3.4

21 3 Model Specification It should be noted that the rail links in SRPTM2 contained a significant number of links outside of the study area, basically covering all parts of the rail network in the North of England. In order to cut down the size of the network, all links that carried services that were not considered to be relevant to movements to/from/within the study area were removed from the network. Bus Speeds The speeds on the highway links in the network were taken from the SATURN model, with it being recognised that the time it takes buses to traverse a link will be a function of the time it takes a car to traverse the link. Buses will travel along the link at lower average speeds than cars, as they will need to stop and pick up/set down passengers, but will still pick up all of the delay experienced at junctions. Therefore, the bus time on links has been calculated using the formula set out below: Bus Time = (Free Flow Time Factor) + Turn Delay Where the link includes a bus lane, this is assumed to allow the bus to get to the front of the traffic queue. In this case the bus only picks up the transient delay at the junction, that is the delay caused by slowing down to look for other traffic or the time waiting for a green traffic signal. For bus lanes the formula is amended as follows: Bus Time = (Free Flow Time Factor) + Transient Delay In the cases where bus priority is made available at traffic signals, it will be possible to remove the transient or turn delay from the calculation of bus time, giving buses just the link time The factor which is applied to the free flow time has been calibrated using timetable times and observed bus journey times. The factor was first calculated by comparing the timetabled time on links with the time taken using the free flow time and turn delay, the factor being calculated as an average for all links in certain speed categories. The factor was then adjusted to better match the observed bus journey times. The final factors used in the model are set out below: Table 3.1 Bus Time Factors Link Free Flow Speed Bus Time Factor Less than or equal to 30mph 1.4 Between 30mph and 40mph 1.6 Between 40mph and 50mph 2.0 Greater than 50mph 1.5 Sheffield and Rotherham Public Transport Model 3.5

22 3 Model Specification Bus Services The bus services in SRPTM3 were transferred from SRPTM2 and updated. The transfer converted from TRIPS to Voyager format automatically and included the route timings that had been coded in the previous version of the model. The bus services have been thoroughly reviewed for SRPMT3 and updated using timetable information correct in October The process of reviewing and updating the lines files included the following stages: the checking of the services coded into the model, with new services added and withdrawn services removed; the checking of the coded headways against the current service headways; and the reviewing of the coded timetable times and the speeds on the network implied by the timetable information As the bus speeds in SRPTM3 are required to change in line with changes in car speeds over time, the final bus service definitions had the timetable information removed. As noted earlier, the timetable information was used to calibrate the factors to apply to the free flow speed of links, these factors being used to calculate bus speeds in the calibration and validation of the model. Rail and Supertram Services The rail and Supertram services have been transferred from SRPTM2 and the routes, headways and journey times checked against the latest timetable information. The rail services in the model include those to all of the major centres around the study area for which demand has been observed, including Manchester, Chesterfield, Derby, Leeds, and Doncaster All rail services that were not considered to be relevant to movements to/from/within the study area were removed from the network, in line with the removal of the links from the network A summary of all public transport services included is provided in Appendix A. Centroid Connectors and Walk Links The centroid connectors were taken directly from SRPTM2, updated to the revised zone system and reviewed as necessary during the calibration of the model. Centroid connectors have a speed of 4.8kph to reflect walking speed, except those with a length of greater than 1.6km which were coded with a speed of 45kph to reflect access by a mechanised mode. These long centroids are used in external zones, with the length of the link reflecting the need to travel to a mainline station to catch a rail service into the study area Walk links were added in all the major centres in developing SRPTM2, with particular attention given to access and egress arrangements at rail stations. Walk links to railway stations were coded so that entry and exit counts could be allocated to a single link to assist matrix estimation. These walk links were reviewed during the calibration of the updated public transport model. Sheffield and Rotherham Public Transport Model 3.6

23 3 Model Specification Non Transit Legs The process of network simplification in Voyager involves the creation of non-transit legs to represent access to public transport services, interchanges between services and walk only journeys. These are generated in Voyager from the possible routes in the network for each of the different types of non-transit legs. Further detail on the generation and use of these non-transit legs is provided in Appendix L. For SRPTM3 non-transit legs have been generated as follows: for access to bus up to 5 access legs from each zone with a maximum walk time of 25 minutes that use up to 5 network links; for access to rail only one access leg from each zone with a maximum walk time of 65 minutes that use up to 20 network links (some special case zones have been allocated two access legs); for access to Supertram only one access leg from each zone with a maximum walk time of 20 minutes but no restriction on the use of network links (some special case zones have been allocated two access legs); for transfer legs only two legs from each node with a maximum walk time of 5 minutes but no restriction on the use of network links; and for walk only trips a maximum walk time of 10 minutes but no restriction on the use of network links In undertaking the calibration and validation of the model, some special cases have been added to reflect special circumstances that exist requiring additional non-transit legs. In particular zones in Sheffield City Centre, Rotherham Town Centre and at Meadowhall have been connected into a location where buses depart from in each direction out of those centres. This ensures that journeys starting/ending in these areas have reasonable access to the relevant public transport service. 3.6 Fares It is important that the modelling of public transport includes an accurate representation of the fare involved in making a journey, as this is an important part of the cost of a journey and the choice of which route or mode to use in making the journey. Voyager includes a framework in which most fare systems can be either directly or approximately represented. Voyager also has the facility to model different types of fare system within a single public transport network, which PT-TRIPS was unable to do. The following fare systems are available within Voyager: flat fare systems, where the same fare is applied irrespective of the distance travelled; distance based fare systems, where the fare varies according to the total distance travelled; zonal based fare systems, where the fare is based on either the highest and lowest fare zones crossed, the number of fare zones crossed, fares specified for journeys between each zone or the accumulation of fares associated with each zone. Sheffield and Rotherham Public Transport Model 3.7

24 3 Model Specification An important factor in the decision of which fare systems to use for each mode is the data available to construct the fare tables. The only fares data that has been available for use in SRPTM3 is data collected by South Yorkshire Passenger Transport Executive (SYPTE) as part of their monitoring of concessionary fares. The data that has been provided by SYPTE is summarised below: for bus journeys the boarding fare and fare per kilometre for adult tickets calculated from observations in the concessions monitoring data, this data being available by operator; for Supertram journeys the adult fare between stops on each Supertram line where trips have been sampled in the data; for rail journeys the adult fare between stations on each rail line where trips have been sampled in the data; for each mode the average adult fare observed; and for each mode the average fare observed over all passengers, and data on the number of journeys made by ticket type Given the data available for modelling fares in SRPTM3, and the fare systems that are available within Voyager, the best fare system for modelling each mode was chosen: for bus, distance based fare systems have been employed with a boarding fare and fare per kilometre coded for each operator; for Supertram a zonal fare system has been developed, with the fare and zone system derived from the adult fares between stops, with stops grouped into zones where the fares are the same; and for rail, a distance based fare system has been employed with a boarding fare and fare per kilometre estimated using the fares data available and the distance between stations where fare has been observed In each case, the fare system represents the average fare paid. The average fare must account for the proportion of passengers who pay each of the available fares such as adult full fare, adults season ticket fare, child fare and free concessions. The fare system has been calculated in two steps: a fare table was calculated for full adult fare; and the adult fare was modified by the ratio of the average adult fare (averaged over the entire Sheffield and Rotherham data set) to the average fare The resulting fare tables for bus and rail are shown in Figure 3.5. As tram fares are based on a zonal matrix based fare system, it is not possible to show the tram fares in the same way. Further detail on the calculation of the fare tables is provided in Appendix B. Sheffield and Rotherham Public Transport Model 3.8

25 3 Model Specification Fare ( ) Distance (km) First Powells Sheffield Community Transport TM Travel Stagecoach OTH RAIL Figure 3.5 Bus and Rail Fare Tables 3.7 Matrix Development A summary of the stages employed in developing the public transport matrices is given below: The origin/destination surveys of passengers undertaken at bus stops, rail stations and Supertram stops were used to create forward direction (boarding in those locations) matrices, using expansion factors calculated from the boarding counts undertaken at the same locations. Reverse direction matrices were synthesised by reversing the forward direction interview records, and allocating them to a return time based on observed data. These were expanded to the alighting counts undertaken at the survey locations. Double counting of trips was removed from the survey data. Matrix smoothing techniques were applied to the survey matrices to eliminate the lumpiness associated with survey matrices. Gravity Model techniques were used to infill the unobserved and partially observed movements in the public transport matrices The processes involved in developing the forward and reverse direction observed matrices from the survey data is described in Appendix C, along with the processes to remove double counting from the observed matrices and to smooth the observed matrices. The Sheffield and Rotherham Public Transport Model 3.9

26 3 Model Specification development and application of the gravity model to infill the unobserved and partially observed movements is described in Appendix D. 3.8 Assignment Parameters The parameters listed below were defined for the assignment process, as described in this section: value of time; in-vehicle time factors; walk and wait time factors; boarding and interchange penalties; and wait curves. Value of Time A behavioural value of time for assignment purposes has been derived from TAG unit utilising local public transport mode and purpose splits. The calculated value of time per hour is 4.97 in the morning peak, 4.65 in the inter-peak and 5.13 in the evening peak (2008 prices, 2002 values). Each journey purpose has an average value of time that does not vary over the day, the difference in the value of time in each of the three time periods reflects the different journey purpose splits that exist in each period. An outline of their derivation is included in Appendix E. In-vehicle Time Factors The in-vehicle time factors used within the model have been derived from a stated preference study, undertaken by MVA, which was designed to estimate the value of bus rapid transit vehicles compared to conventional buses and Supertram. The results of this study are presented in the report Sheffield and Rotherham Bus Rapid Transit: Stated Preference Research, the Executive Summary of which is attached as Appendix M, the report being available on request. The study concluded that given an in-vehicle time factor of 1.0 for bus, Supertram has an in-vehicle time factor of It is considered that the in-vehicle time factor for rail is likely to be similar to that assumed for Supertram. The research referred to above did not consider the value of rail as an alternative to bus and so local data on the value of rail is not available. Other recent studies have considered the in-vehicle time factor for rail to be the same as tram, and therefore a starting value of 0.78 has been assumed for review as part of the calibration process In applying the in-vehicle time factors in SRPTM3, it has been recognised that the time spent in the vehicle should not be valued at less than the actual time. Therefore, the in-vehicle time factor for tram and rail has been set to 1.00, and for bus to Walk and Wait Time Factors Current government advice on appropriate factors to apply to out of vehicle time for modelling purposes, as set out in TAG Unit is to use a factor of about 2 for both walk Sheffield and Rotherham Public Transport Model 3.10

27 3 Model Specification and wait times. Other research and studies suggest that the walk time weighting may range from 1.3 to 2.1 and the wait time between 1.3 and The walk and wait time factors used as a starting point for SRPTM3 were those used in SRPTM2. Therefore, walk and wait time factors of 1.9 were set initially and were reviewed as part of the calibration and validation of the model. Boarding and Interchange Penalties Boarding and interchange penalties are generalised cost adjustments applied in the assignment process to represent the perceived inconvenience of using a particular sub-mode or interchanging between services. These penalties are defined by sub-mode in the case of boarding penalties and by sub-mode pair in the case of interchange penalties, and are applied in addition to any walk and wait time Boarding penalties and interchange penalties have to be considered together because boarding penalties are applied at every boarding of a vehicle including those that constitute an interchange between vehicles. The scale of the appropriate penalty to apply is affected by the approach used to represent the walk links that represent access to stations and bus stops. In SRPTM3 these links have been coded with average walk speeds applied to the distance to the station yielding the walk-time to the station. The walk times therefore omit the time taken to access the correct platform. This time is built into the boarding penalty Previous studies and government advice recommend boarding penalties of 2-10 minutes, and interchange penalties of anything from 2-15 minutes. The calculation of the in-vehicle time factors from the stated preference survey has taken into account all of the perceived advantages of different modes, and therefore a mode specific boarding penalty has not been used for the initial assignments. No boarding penalty has been applied to tram to tram interchanges, which generally involve standing at the same platform that you have arrived at. However, an interchange penalty of 2.5 minutes has been applied to rail to rail interchanges, and an interchange penalty of 5 minutes has been applied to all other interchanges. Wait Curves Voyager uses wait curves to calculate the time a passenger will wait for a service from the service headways. Voyager has the facility to apply different wait curves for the first boarding of a public transport service and to subsequent boardings of public transport services. However, the consideration of wait time that are likely to occur within the Sheffield and Rotherham area concluded that the same wait curve should be used for the first and subsequent boardings. The derivation of the wait curves for SRPTM3 is discussed in Technical Note 3, which is attached to this Report as Appendix F The calculated wait time reflects both the time spent waiting for the service and the inconvenience of having to schedule one s journey around infrequent services. The model uses a wait curve derived from the Passenger Demand Forecasting Handbook (PDFH). This assumes wait time of half headway up to a 10 minute headway (which would imply random arrival of passengers) and then tapers the wait time so that a 30 minute headway implies a 12 minute wait time and a 2 hour headway implies a 27.5 minute wait time. This reflects the fact that passengers are unlikely to wait very long times for relatively infrequent services. The PDFH wait curve used in this model is shown in Figure 3.6. Sheffield and Rotherham Public Transport Model 3.11

28 3 Model Specification Wait Time (minutes) Headw ay (m inutes) Figure 3.6 Public Transport Wait Curve (All Services) As detailed in Appendix L, the Voyager Route Enumeration process does not use the wait curve to generate the set of discrete routes to be used in the loading process. Instead, half headway is used to estimate the waiting times for each service. It is therefore important to set an appropriate maximum wait time to avoid routes that use infrequent service being eliminated from the choice set due to the long wait times. Therefore, a maximum wait time of 15 minutes is used in the route enumeration phase in order that such routes are considered during route evaluation. 3.9 Modification to Assignment Parameters In the course of the calibration process it was necessary to modify some of the assignment parameters. A summary of the changes and refinements applied is presented in Chapter 7. Sheffield and Rotherham Public Transport Model 3.12

29 4 Data Used 4.1 Introduction SRPTM3 has been built using data sets compiled from various sources, mainly in the form of data collected specifically for previous versions of the model, but also in the form of public transport monitoring data that has been collected more recently. This Chapter describes the data that has been used in the update of the model to 2008, which fall broadly into three categories: transport supply data - the representation of road, public transport and walk networks; and inventories of public transport services, including routes, fares, headways and operators; transport demand data - the numbers of people who would like to travel between the zones in the model; and calibration and validation data - observed data against which the model outputs are checked, including counts of passengers and the time taken to make particular journeys. 4.2 Supply Data Networks Road, Rail and Walk The road network was developed from the latest version of the Sheffield and Rotherham district SATURN highway model (SRHM3) and the previous version of the public transport model (SRPTM2). The road network on which the buses run was taken from the SATURN model and includes all the motorways, all the A, B and C class roads, and some unclassified roads. Every road served by buses is included within the SATURN model, with the exception of some roads where the bus services follow a small loop round an estate before returning to the main road. The validated base year SATURN model has been used to pass link times to the public transport model in the form of the congested time in the network The heavy rail and Supertram networks were taken from SRPTM2. The rail network has been scaled back geographically to contain only the links and nodes relevant to journeys to/from/within the study area The heavy rail and Supertram networks have been linked to the highway network using walk links, transferred from SRPTM2. The walk links have been thoroughly reviewed, with a significant number of improvements being implemented for the Supertram network to reflect accurately the access to the network as part of the development of SRPTM3. Walk links have also been added in the town and city centres to reflect more direct walking routes that exist where there are no highway links. The Voyager network development procedures have also been developed to allow walking in the reverse direction on one-way highway links. Services and Headways The bus service definitions used in SRPTM2 have been thoroughly reviewed for the final version of the PT model, with all service routes and frequencies checked against the Sheffield and Rotherham Public Transport Model 4.1

30 4 Data Used timetables in place in Autumn The description of the Supertram and heavy rail network services were taken from SRPTM2 and thoroughly reviewed and updated to match the current timetables The timings of Supertram and heavy rail services have been taken directly from timetable times and applied to the network. For bus services, the journey time for services is calculated from the speeds contained within the highway network as described in Section Demand Data The use of demand data for building the public transport matrices has been outlined in Section 3.7, with a detailed description of the methodology provided in Appendix C. The datasets that have been used to build the matrices are described below. Public Transport Surveys The public transport matrices have been developed using public transport survey data which was collected for the development of SRPTM2. These surveys were undertaken in between spring and autumn 2007 and took the form of passenger interviews in the following locations, as illustrated in Figure 4.1 and Figure 4.2: Sheffield, Meadowhall and Rotherham railway stations; Meadowhall Supertram stop; all Supertram stops within the City Centre, and also along West Street, Upper Hanover Street and Netherthorpe Road; all bus stops within Sheffield City Centre (including Sheffield Interchange), along West Street and at the Royal Hallamshire Hospital; all bus stops at Meadowhall Interchange; and all bus stops at Rotherham Interchange. Sheffield and Rotherham Public Transport Model 4.2

31 4 Data Used Figure 4.1 Public Transport Survey Locations in Sheffield City Centre Figure 4.2 Public Transport Survey Locations in Rotherham Town Centre Sheffield and Rotherham Public Transport Model 4.3

32 4 Data Used All these surveys involved face-to-face interviews of passengers waiting to board public transport services at each of the locations listed above. These interviews were designed to establish the following details about the journey being made: the postcode of the origin and destination of the journey, or as much detail as possible about the origin or destination; the access and egress mode for the journey; the reason for being at the origin and for going to the destination; the timing of the equivalent journey in the reverse direction, where applicable; and whether a car was available for the journey being made At the same time as the interviews of passengers waiting to board public transport services counts of passengers boarding and alighting services at the same locations were undertaken. In the case of the rail stations, the counts that were undertaken were platform entry and exit counts as the length of some trains makes it difficult to count passengers boarding and alighting. These counts have been used in expanding the interview records to give a full representation of trip making, and have also been used in the validation of the public transport model. South Yorkshire Concessionary Fares Surveys SYPTE have undertaken a survey of passengers to establish the usage of concessionary fares on public transport within South Yorkshire. This survey has resulted in all types of passengers being surveyed, not just those making their journey using concessionary fares. These surveys have recorded the origin and destination stop for the trip being made, the ticket type for the journey, the fare being paid and the service travelled on. This data has been used as the basis for calculating the fare tables, as described in Section Calibration and Validation Data Journey Time Data SCC provided bus journey time data from a series of diagnostic tests on various key corridors. This data has been used to validate the journey times within the model, which are derived from the highway link times in the validated SRHM3 base year SATURN assignments. The use of this data will ensure that the timings used within the model actually reflect the journey times experienced on the buses. Passenger Count Data We calibrated the model to match passenger count data. The counts were carried out at the locations shown in Figure 4.3 and Figure 4.4 They came from the following sources: rail station entry and exit counts at every station within the model area in, provided by SYPTE (Sheffield, Meadowhall and Rotherham undertaken in 2008 other stations in 2006); Sheffield and Rotherham Public Transport Model 4.4

33 4 Data Used platform entry and exit counts undertaken during the public transport surveys, as a proxy for boarding and alighting counts, at Sheffield, Rotherham and Meadowhall stations; bus and tram passenger occupancy counts on the Sheffield and Rotherham LTP monitoring cordons and screenlines; counts of passengers boarding and alighting bus services in Sheffield City Centre, at Meadowhall Interchange and at Rotherham Interchange undertaken during the public transport surveys; and counts of passengers boarding and alighting tram services in Sheffield City Centre and at Meadowhall undertaken during the public transport surveys. Figure 4.3 Bus and Tram Occupancy Survey Locations Sheffield and Rotherham Public Transport Model 4.5

34 4 Data Used Figure 4.4 Rail Station Entry and Exit Count Locations Sheffield and Rotherham Public Transport Model 4.6

35 5 Network Validation 5.1 Introduction This chapter documents the network validation which was undertaken in accordance with the DfT s latest guidance as contained in Chapter 2. The guidance recommends that the following checks of the input public transport supply should be undertaken, each of which is considered within this Chapter: checking network coding; reviewing modelled journey times against timetables and surveys; reviewing coded service vehicle flows against on-street observations; and assessing the reasonableness of modelled route choice. 5.2 Network Checks The network node and link structure was mainly taken from the latest version of the SATURN highway model of the same area (SRHM3) which is a validated model, whose network geometry has been shown to have a high degree of accuracy. The validation of SRHM3 is reported in Sheffield and Rotherham Distric SATURN Model 2008 Model Development Report. The rail and tram networks were taken from SRPTM2, which is again a validated model, and have further been checked to ensure they provide an accurate representation of the rail and tram networks in the study area The walk links and centroid connectors in the model have been taken directly from SRPTM2 and have been checked for accuracy and consistency in the calibration of the model. This checking has concentrated mainly on ensuring an accurate representation of access to major bus stops and rail stations, and ensuring there are centroid connectors to represent access to the network. Any errors were corrected. 5.3 Public Transport Service Validation Lines Coding Checking As reported earlier, the bus services included in the lines files for SRTPTM3 were transferred from SRPTM2. In developing SRPTM3 all bus services have been checked to ensure the routeings, headways and journey times were correct with the current timetables The same checks were undertaken for the Supertram services, which like the bus services, were taken from SRPTM The rail services were also taken directly from SRPTM2 and were checked thoroughly and updated. Journey Time Validation The bus, rail and Supertram journey times were taken directly from SRPTM2 which were based on timetable data, with different timings used in each time period where appropriate. Sheffield and Rotherham Public Transport Model 5.1

36 5 Network Validation For all services, the timings were coded for each of the key stopping locations along the route, and these were thoroughly checked for rail and Supertram in updating the files for SRPTM3. The bus timings were thoroughly reviewed during the development of SRPTM2, and these were updated to reflect service changes in the SRPTM3 lines files Having input the timing points into the public transport lines files the implied speeds along links on services were checked for any running at very high speeds or very low speeds. This is a useful way of checking any errors in coding either the location of timing points, or the time at each particular timing point, to ensure that the timings input to the model are sensible The timing points were not used in the final lines files for buses, instead network speeds were derived from the highway link times. However, the timetable times coded into the lines files were used in the calibration of the factors to apply to car times on links to estimate the bus times on links Observed journey time data has been provided by SCC for the key bus corridors, which have been compared with the modelled journey times for each time period, and the percentage difference calculated. If the modelled journey time over the whole route was within 15% of the observed time then the route was considered OK. The results are shown in Table 5.1 below. Of the 24 routes which were selected, 71% of routes were found to be within 15% in the morning peak compared with 55% in the inter-peak, and 83% in the evening peak The bus journey time surveys were undertaken in the morning and evening peak periods rather than peak hours, but the model represents the peak hour. Additional congestion in the peak hour would suggest that observed times in the peak period are probably faster than in the peak hour. So we would expect the observed peak period times to be a little faster than the modelled peak hour times Unfortunately there were too few observations within the peak hour to calculate a reasonable average The inter-peak observed times are those just outside of the peak time periods and are not an ideal comparison for the inter-peak times within the model. Therefore, it is not considered appropriate to use the inter-peak journey times to validate bus speeds in the network Whilst modelled and observed journey times are within 15% of those observed in most cases, more routes are modelled as being slower than the observed journey time than are modelled faster. There are some instances where the modelled journey times fall outside the 15% range, but it can be said that the model is neither consistently too slow or too fast. In fact, no journey time route is consistently too slow or too fast in the model, demonstrating that the model provides a good representation of journey times on bus routes. Sheffield and Rotherham Public Transport Model 5.2

37 5 Network Validation Table 5.1 Comparison of Bus Journey Times with Observed Data Morning Peak Inter-peak Evening-Peak % of Lines No of Lines % of Lines No of Lines % of Lines No of Lines Slow 13% 3 14% 3 13% 3 OK 71% 17 55% 12 83% 20 Fast 17% 4 32% 7 4% 1 Total 100% % % Vehicle Flows The bus and tram occupancy counts provided by SYPTE from its database of counts also included bus and tram vehicle counts on the Sheffield and Rotherham LTP monitoring cordons and screenlines. In the case of tram, which has a simple network of services, the coding from timetables is considered to be accurate and therefore there is no requirement to further check vehicle flows. However, in the case of buses, the network is much more complicated so the vehicle flows in the network implied by the coded bus lines have been compared against the observed vehicle counts The comparison of the bus vehicle flows within the model against the observed vehicle counts can be seen in Figure 5.1 to Figure Modelled Bus Vehicles Observed Bus Vehicles Figure 5.1 Comparison of Morning Peak Observed and Modelled Bus Vehicle Flows Sheffield and Rotherham Public Transport Model 5.3

38 5 Network Validation Modelled Bus Vehicles Observed Bus Vehicles Figure 5.2 Comparison of Inter-peak Observed and Modelled Bus Vehicle Flows Modelled Bus Vehicles Observed Bus Vehicles Figure 5.3 Comparison of Evening Peak Observed and Modelled Bus Vehicle Flows These figures show that there is a strong correlation between the modelled and observed bus vehicle flows in each of the three time periods. Differences between the two sets of data may be accounted for by changes to timetabled services since the counts were undertaken, Sheffield and Rotherham Public Transport Model 5.4

39 5 Network Validation and the errors in count data caused by inclusion of some non-service, and not in service, buses. 5.5 Route Choice A selection of routes predicted by the assignment model were checked for reasonableness by reporting the proportion of trips assigned to each available path between a selection of zone pairs. The routings were judged to be realistic based on knowledge of the area and reviews of published timetables. Morning peak mode shares for each of the movements investigated are shown in Table 5.2. Table 5.2 Morning Peak Public Transport Sub-Mode Choice for Selected Journeys Route Bus Train Tram Rotherham Town Centre to Sheffield Midland Station 0% 100% 0% Rotherham Town Centre - Sheffield Castle Square 87% 13% 0% Manor Top to Sheffield Castle Square 95% 0% 5% Manor Top to Sheffield Arundel Gate 100% 0% 0% Halfway to Meadowhall 0% 0% 100% Parson Cross to Rotherham Town Centre 100% 0% 0% Rotherham Parkgate to Rotherham General Hospital 100% 0% 0% Dore to Sheffield Castle Square 100% 0% 0% Dore to Sheffield Midland Station 0% 100% 0% Chapeltown to Rotherham General Hospital 100% 0% 0% Chapeltown to Royal Hallamshire Hospital 100% 0% 0% Middlewood to Sheffield Castle Square 0% 0% 100% Hillsborough to Sheffield Castle Square 20% 0% 80% Woodhouse to Sheffield Castle Square 100% 0% 0% Meadowhall to Sheffield Castle Square 0% 0% 100% Meadowhall to Sheffield The Crucible 0% 80% 20% The mode shares on the first two routes illustrate the comparative speed of rail and the impact of the walk links in the model. The rail journey between Rotherham and Sheffield Sheffield and Rotherham Public Transport Model 5.5

40 5 Network Validation takes only 11 minutes whereas the fastest bus takes around 25 minutes. So rail dominates the demand between the two stations, but for destinations further from the station bus increases in popularity because it serves them with shorter walk links Considering the routes between Manor Top and Sheffield City Centre shows a split between Supertram and bus with bus being more dominant, particularly for destinations further away from the Supertram stop. The journey is directly on the Supertram route, but the tram only has a small in-vehicle time advantage with walk times tending to be longer and fares are more expensive. It runs mainly on-street so it must obey the speed limit, but it gains some time benefits over bus because it gets priority at some traffic signals. However, fares and wait time for Supertram are higher making it a less attractive model where bus competes on journey time The route from Halfway to Meadowhall is dominated by Supertram. It is a very long route where Supertram benefits from some very fast sections where it is segregated and runs at about 50mph Bus dominates both the routes between Parson Cross and Rotherham Town Centre, and between Rotherham Parkgate and Rotherham General Hospital. There is a potential route from Parson Cross to Rotherham Town Centre using bus to reach Sheffield and Train to reach Rotherham. However, the trains are relatively infrequent and the interchange from bus to train involves quite a long walk, and the model sensibly forecasts that bus would dominate. The model also reflects the fact that bus is the only reasonable choice for travel between Parkgate and the hospital The route between Dore and Sheffield Castle Square is dominated by bus but the route from Dore to Sheffield Midland Station is dominated by Rail. Dore is on the southern edge of the conurbation on the Southern Trans-Pennine rail route from Manchester. The rail journey time is fast but the services are infrequent (only three services run between 07:00 and 10:00). The key driver in the mode choice decision is the location of the destination in the City Centre, with rail being the better option to the station itself but bus becomes much more attractive to the City Centre The routes from Chapeltown to Rotherham General Hospital and Royal Hallamshire Hospital are dominated by bus in the model, reflecting that there is no reasonable route by an alternative mode The route from Middlewood to Sheffield Castle Square is dominated by tram, but bus takes some mode share on the route from Hillsborough to the same destination. Middlewood tram terminus is only served by tram, the nearest bus services being a considerable walk away, so it is not surprising that tram is dominant. Hillsborough is served by fast and frequent bus services, which clearly are competing with the tram service, and the share between modes will be dependent on the proximity of the origin and destination to tram stops The route from Woodhouse to Sheffield Castle Square is dominated by bus even though there is a station at Woodhouse. The rail service is infrequent and hence unpopular, and the model reflects the low rail patronage observed at the station The route from Meadowhall to Sheffield Castle Square is dominated by Supertram, but for journeys to the Crucible rail becomes the dominant mode with 80% of the journeys. Much of the Supertram route is segregated from traffic and even though the speeds are much lower Sheffield and Rotherham Public Transport Model 5.6

41 5 Network Validation than on the segregated sections of the route to Halfway, the tram does not suffer much congestion. However, it is clear that as the destination gets further from a Supertram stop rail becomes more popular. Bus journey times are longer than those for rail and Supertram, and so bus does not appear attractive for these journeys. The model reflects Supertram s popularity for access to Meadowhall. 5.6 Conclusions The purpose of this Chapter was to report on network checks undertaken as part of the validation of the public transport model. The evidence presented in this Chapter shows that the model has the following characteristics: network coding that reflects the public transport supply within Sheffield and Rotherham; modelled bus journey times replicate observed times in the majority of cases, although the use of timetable data does give rise to some bus routes being slower than the observed journey times as a result of buses travelling quicker than timetable time; coded bus service vehicle flows have a strong correlation with on street observed bus vehicle flows; and modelled route choice for selected zone pairs is reasonable. Sheffield and Rotherham Public Transport Model 5.7

42 6 Matrix Validation 6.1 Introduction The guidance in TAG unit suggests that Matrices should be validated by assignment to the network. Matrix level validation should involve comparisons of assigned to counted passengers across complete screenline and cordons. At this level of aggregation, the differences between assigned and counted flows should in 95% of the cases be less than 15% The guidance continues to suggest that If the matrices do not validate satisfactorily, matrix estimation may be used to adjust the trip matrices to accord more closely with the validation counts. The changes brought about by the matrix estimation process should be examined to check for particular distortions This chapter examines the validation of the initial matrices against the observed screenline and cordon counts by comparing the counts with the assigned flows across the screenlines and cordons from an assignment of the matrices to the network. This Chapter then goes on to compare the matrix totals with estimates of annual patronage on bus, rail and Supertram within Sheffield and Rotherham. 6.2 Comparison of Matrices with Count Data The validation of the initial matrices has been undertaken by comparison with a series of screenlines and cordons, for which observed vehicle occupancy counts are available. Counts from two separate surveys, undertaken by SYPTE during 2006 and 2008 and described in Chapter 4, have been used in the matrix validation: bus and tram passenger counts from the LTP monitoring surveys; and rail passenger boarding and alighting counts at every station in the model area The counts on these screenlines and cordons have been used in the validation of the assignment model, and the comparisons of model flows to counts on these screenlines and cordons are presented in Chapters 7 to 9. The validation of the total flows crossing these screenlines and cordons shows that: the model flows across each of the bus screenlines and cordons are within 15% of the observed count in all instances; the model flows across the inner and outer Supertram cordons are within 15% of the observed count in all cases; and the modelled flows entering and leaving Sheffield and Rotherham stations are within 15% of the observed count in all instances, but this is not the case in Rotherham where all counts are lower than the 150 passenger threshold used to determine whether an individual count should be considered However, the counts for each of the modes have been used to calculate a total observed and modelled cordon crossing count covering all modes for the Sheffield Inner, Sheffield Outer and Rotherham Cordons. In these cordon calculations the station entry and exit counts have Sheffield and Rotherham Public Transport Model 6.1

43 6 Matrix Validation been used as a proxy for cordon crossings, and the Sheffield Outer screenlines have been added together to from one screenline Table 6.1 presents the percentage difference between the total modelled flow and observed flow across each of the cordons for all sub-modes added together. It can be seen from this table that in all instances the modelled flow is within 15% of the observed flow, and therefore the matrix validates satisfactorily at the cordon level. Table 6.1 Modelled and Observed Cordon Crossing Comparisons AM IP PM Sheffield Inner Inbound -4% -2% 2% Outbound 4% -3% -7% Sheffield Outer Inbound -4% -2% -3% Outbound -2% -2% -3% Rotherham Inbound -0% 6% 9% Outbound 10% 4% -2% 6.3 Comparison of Matrix with Annual Patronage Estimates from other sources The guidance suggests checking the overall demand in the model against an independent source. To achieve this we have compared an estimate of daily demand from our models against an estimate of daily demand derived from SYPTE s estimates of annual demand by bus, rail and Supertram for the county. We factored up the modelled demand in the hour models to represent periods, summed the periods to a 12-hour total and factored to 24- hours To estimate Sheffield and Rotherham demand from annual county-wide demand we assumed that all Supertram patronage is from the Sheffield area and that bus and rail demand are directly proportional to the population of Sheffield and Rotherham. A daily patronage level was then estimated by assuming an annualisation factor of 300. Clearly, the estimates on both sides of the comparison include substantial uncertainty because of the assumptions we have had to make, and this check demonstrates only the order of magnitude Total daily travel implied by the demand matrices is slightly higher than an independent estimate derived from annual county-wide estimates of bus, rail and Supertram usage provided by SYPTE county-wide data as shown in Table 6.2. The hour-to-period factors shown in the table were derived from the counts described in Chapter 4. Sheffield and Rotherham Public Transport Model 6.2

44 6 Matrix Validation Table 6.2 Matrix Validation 24 Hour Public Transport Demand Period Matrix Total (hourly) Factor Total Demand (period) Morning Peak 29, ,752 Inter Peak 22, ,378 Evening Peak 25, , Hour 265, Hour 1.1 (12hr>24hr) 292,068 Independent Estimate 251, Although this comparison has shown that the matrix is of the correct order, the accuracy of this comparison could be affected by a number of reasons: inaccuracy of factors used to calculate daily demand from peak matrices; inaccuracy of factor used to calculate weekday patronage from annual patronage; and inaccuracy in the calculation of Sheffield and Rotherham patronage from South Yorkshire patronage. 6.4 Conclusions Both of the matrix validation checks reported here suggest that demand matrices are of the right order of magnitude. Sheffield and Rotherham Public Transport Model 6.3

45 7 Assignment Validation Prior to Matrix Estimation 7.1 Introduction The calibration and validation of the public transport model are described in this and the following two chapters. The approach taken to assignment calibration and validation was to: tabulate comparisons of modelled and observed passenger flows; review assignment parameters to assess if validation could be improved; apply matrix estimation; and repeat tabulations of comparisons of modelled and observed passenger flows Prior matrices (ie matrices not subject to matrix estimation techniques) or the raw data used in matrix creation should be the starting point of any subsequent work to further develop a model. Therefore, it is important to understand the derivation of model data and the limitations of the validation without matrix estimation. This chapter presents the validation of the model prior to the running of matrix estimation processes, with the validation of the model after matrix estimation presented in Chapter 8. The validation of the model following the introduction of crowding modelling is presented in Chapter In general it is more difficult to establish patronage estimates by service or link for public transport than for road links, as for the latter continuous automated counts are often available. Therefore TAG Unit suggests the following validation targets for comparison of modelled and observed passenger flows: modelled flows should be within 15% of the observed values on screenlines and cordons; and modelled flows should be within 25% of individual counts except where observed flows are less than 150 passengers Chapter 4 described the data that was available for assignment validation and this data has been used to produce the validation reports presented in Appendix G and summarised within this chapter. The validation of passenger flows is undertaken for the following: bus passenger flows across the inner Sheffield cordon; bus passenger flows across the outer Sheffield screenlines; bus passenger flows across the Rotherham cordon; Supertram passenger flows across the inner and outer Sheffield cordons; and all boardings and alightings at the six stations in the model area. Sheffield and Rotherham Public Transport Model 7.1

46 7 Assignment Validation Prior to Matrix Estimation 7.2 Assignment Parameters Initial assignments were carried out using the assignment parameters set out in Chapter 3. A number of tests were undertaken to assess whether adjusting any of the parameters, within plausible ranges, would improve model validation. These tests included: varying walk and wait time factors; varying boarding and interchange penalties; varying sub-mode in-vehicle time factors; and varying the sensitivity parameters of the sub-models On the basis of these tests we concluded that a number of the model parameters needed adjusting. Firstly it was decided to add a boarding penalty to all modes in order to reduce the number of discrete routes created for each zone pair. This is needed because the route enumeration process does not use the transfer penalties, meaning transferring between services become too attractive without a boarding penalty. Therefore, all modes were given a 2.5 minute boarding penalty, and so as not to change the interchange penalties 2.5 minutes were taken off each of the interchange penalties The walk and wait time factors were also adjusted because they were having too much influence on the choice between modes. The walk time factor was reduced to 1.6 in all cases, to minimise the impact that walking the extra distance to tram stops had on tram patronage. It was also decided to adjust the wait time for tram, to reflect the better waiting facilities for this mode, a factor that was not taken into account in the stated preference surveys used to calculate the in-vehicle time penalties A summary of the assignment parameters used is shown in Table 7.1. Sheffield and Rotherham Public Transport Model 7.2

47 7 Assignment Validation Prior to Matrix Estimation Table 7.1 Assignment Parameters Parameter Value Value of Time AM Peak per hour Inter-peak per hour PM Peak per hour Walk Time Factor 1.6 Wait Time Factor Bus 1.9 Wait Time Factor Rail and Tram 1.6 Boarding Penalty 2.5 minutes In Vehicle Time Factor Bus 1.28 In Vehicle Time Factor Rail 1.00 In Vehicle Time Factor Supertram 1.00 Transfer Penalty between buses Transfer Penalty between rail Transfer Penalty between tram Transfer Penalty between differing modes 2.5 minutes 0 minutes -2.5 minutes 5 minutes Walk Choice Model Sensitivity Parameter 0.5 Alternative Alighting Choice Sensitivity Parameter Bus Occupancy Validation Bus occupancy counts were undertaken on the following cordons and screenlines during 2008, and have been used in the validation of the public transport matrix: Sheffield Inner Cordon; Sheffield North East Screenline; Sheffield North West Screenline; Sheffield South East Screenline; Sheffield South West Screenline; Sheffield University Screenline; and Rotherham Cordon. Sheffield and Rotherham Public Transport Model 7.3

48 7 Assignment Validation Prior to Matrix Estimation A summary of the bus occupancy validation for the cordons and screenlines is presented in Table 7.2 showing the percentage difference between the total model flow and the total count crossing each cordon and screenline. Table 7.2 also shows the percentage of screenlines and cordons passing the validation criteria that modelled flows should be within 15% of the observed values on screenlines and cordons The fit of modelled flows to counts is quite good on the Sheffield Inner cordon and the Rotherham town centre cordon where flows are very close to being within the target of 15% in 10 out of the 12 cases. A large percentage of demand crossing these cordons was fully observed in the surveys. Elsewhere in the model, where more of the demand is synthetic, the fit is not as good. It can be seen from Table 7.2 that the number of screenlines and cordons passing the validation criteria is only 36% for the morning peak, 64% for the interpeak and 21% for the evening peak. This illustrates a poor validation to the counts for this criteria, with some screenlines and cordon flows being more than 40% different to the observed Table 7.3 shows the percentage of the links on each screenline and cordon where the validation criteria that modelled flows should be within 25% of individual counts except where observed flows are less than 150 passengers is met. This again shows a poor level of validation with the criteria only met for 40% of links in the morning peak, 64% in the interpeak and 38% in the evening peak. Sheffield and Rotherham Public Transport Model 7.4

49 7 Assignment Validation Prior to Matrix Estimation Table 7.2 Percentage Difference Between Screenline and Cordon Flows and Observed Counts - Prior to Matrix Estimation Morning Peak Inter-peak Evening Peak Sheffield Inner Cordon Inbound -17% -12% -7% Outbound -13% -12% -36% Sheffield North East Screenline Inbound 34% -20% -27% Outbound -12% -24% -8% Sheffield North West Screenline Inbound 40% -10% 3% Outbound 47% -14% -15% Sheffield South East Screenline Inbound -19% -4% 17% Outbound 33% 5% -40% Sheffield South West Screenline Inbound -25% -25% -21% Outbound -39% -5% -38% Sheffield University Screenline Inbound -50% -42% -55% Outbound -3% -28% -37% Rotherham Cordon Inbound -3% -1% 26% Outbound -2% -3% -17% Screenlines Passing Criteria 36% 64% 21% Sheffield and Rotherham Public Transport Model 7.5

50 7 Assignment Validation Prior to Matrix Estimation Table 7.3 Percentage of Screenline Links with an Observed Flow Greater than 150 where Modelled Flow is within 25% of Observed Flows Prior to Matrix Estimation Morning Peak Inter-peak Evening Peak Sheffield Inner Cordon Inbound 33% 56% 50% Outbound 44% 70% 17% Sheffield Outer Screenlines Inbound 33% 58% 40% Outbound 38% 73% 42% Rotherham Cordon Inbound 63% 80% 100% Outbound 25% 50% 43% Total 40% 64% 38% 7.4 Supertram Occupancy Validation The Supertram occupancy counts have been provided for an inner and an outer cordon, with each major line counted at each cordon. The comparison of the modelled flow with the observed count is shown in Table 7.4 showing that in the majority of cases the criteria that modelled flows over cordons should be within 15% of the observed flows are not met. Table 7.5 shows the percentage of the inbound and outbound links that satisfy the criteria that modelled flows should be within 25% of individual counts except where observed flows are less than 150 passengers. This criteria is met in only a minority of cases in the peak direction in the peak periods, and that a significant number of cases do not meet the criteria. This once again highlights that the validation prior to matrix estimation is poor. Sheffield and Rotherham Public Transport Model 7.6

51 7 Assignment Validation Prior to Matrix Estimation Table 7.4 Percentage Difference Between Modelled and Observed Supertram Cordon Flows Prior to Matrix Estimation Morning Peak Inter-peak Evening Peak Inner Cordon Inbound -37% -20% -9% Outbound -42% -22% -13% Outer Cordon Inbound -17% -5% -22% Outbound 11% -16% -14% Table 7.5 Percentage of Supertram Links with an Observed Flow Greater than 150 where Modelled Flow is within 25% of Observed Flows Prior to Matrix Estimation Direction Morning Peak Inter-peak Evening Peak Inbound 33% 67% 67% Outbound 40% 50% 33% 7.5 Rail Boarding and Alighting Validation As has been reported earlier in this report, no counts of passengers actually boarding and alighting train services are available. However, station head counts of passengers entering and leaving stations have been made available by SYPTE and have been used in the model validation. Counts of passengers entering and leaving rail stations are available for Sheffield, Meadowhall and Rotherham in Similar counts are also available for the remaining key stations in South Yorkshire that were undertaken in Table 7.6 presents the validation of the model in terms of the passengers entering and leaving each station, which again shows a poor level of validation. It is not considered suitable to consider these stations as a screenline or cordon, so they should really be tested against the individual link criteria. However, Sheffield, Rotherham and Meadowhall could all be considered to be individual cordons equivalent to the bus and Supertram cordons. It should be noted that the observed Rotherham counts are lower than the 150 threshold for considering the validation of individual counts Table 7.7 shows the percentage of the entry and exit counts that satisfy the criteria that modelled flows should be within 25% of individual counts except where observed flows are less than 150 passengers. This criteria only met in some cases in each time period, and the validation of the model is not considered to be satisfactory. Sheffield and Rotherham Public Transport Model 7.7

52 7 Assignment Validation Prior to Matrix Estimation Table 7.6 Percentage Difference between Modelled and Observed Passengers Entering and Leaving Stations Prior to Matrix Estimation Morning Peak Inter-peak Evening Peak Station Entering Leaving Entering Leaving Entering Leaving Meadowhall -36% 59% 3% 9% 24% -34% Rotherham 112% 26% 226% 323% 239% 138% Sheffield -44% -18% -39% -51% -14% -49% Total -27% -9% -23% -23% 9% -23% Table 7.7 Percentage of Rail Links with an Observed Flow Greater than 150 where Modelled Flow is within 25% of Observed Flows Prior to Matrix Estimation Direction Morning Peak Inter-peak Evening Peak Station Entry 0% 50% 100% Station Exit 50% 50% 0% 7.6 Conclusions This Chapter has presented the validation of the model by comparing model flows with observed counts. The comparisons contained within this chapter have shown that the validation of the bus, rail and Supertram flows is not satisfactory when considering the validation criteria presented in TAG The validation comparisons illustrate that it is necessary to adjust the model further to achieve an adequate representation of the passenger flows in the existing situation. A further review of the coded network, including walk links, centroid connectors, and access arrangements was undertaken with some minor revisions made. However this did not provide any significant improvements at a summary level and so in order to improve model validation, it was decided to make use of matrix estimation techniques. The estimation process and results are discussed in the following chapter. Sheffield and Rotherham Public Transport Model 7.8

53 8 Assignment Validation After Matrix Estimation 8.1 Introduction Chapter 7 presented the validation of the model before the use of matrix estimation techniques, and concluded that the validation was not satisfactory compared to the validation criteria set out in TAG. Therefore matrix estimation was employed in order to improve the validation. This process adjusts the demand matrix, by reference to a set of counts and a set of paths, so that the assignment more closely reproduces measured flows This chapter outlines the approach taken to matrix estimation and the effect that this has had on the trip matrices, and then reports the validation statistics in an identical manner to the previous chapter. The detailed validation statistics are presented in Appendix H. 8.2 Matrix Estimation Matrix estimation was carried out using the CUBE program Analyst, utilising the public transport paths taken from the initial assignment. The following data are required for matrix estimation: a prior demand matrix in this case the demand matrices developed as described in Chapter 4; passenger count data for individual links and screenlines; trip end totals for each origin and destination; and confidence levels for each input to the matrix estimation process There will inevitably be inconsistencies between sources of data due to variability of demand on different surveyed days and survey data errors. Matrix estimation, as implemented in CUBE, utilises statistical procedures to establish the demand matrix which is most likely to explain the input data. Confidence levels are assigned to the input data to indicate the relative weight applied to each data item in the statistical matrix estimation process in order to reflect the relative reliability of each data. Confidence levels can be assigned to any combination of: individual matrix cells; trip end totals; individual link counts; and link counts grouped into screenlines or cordons The confidence levels used in the matrix estimation were set based on professional judgement and then adjusted in an iterative process to achieve the best level of fit to passenger count data without unduly distorting the demand matrix. The final confidence settings used are presented in Table 8.1. Sheffield and Rotherham Public Transport Model 8.1

54 8 Assignment Validation After Matrix Estimation Table 8.1 Matrix Estimation Confidence Levels Data Individual Values Grouped Values Bus Counts Supertram Counts Rail Counts Key Centre 50 Rail Counts Other Station 25 Prior Matrix Observed Cells 6 Prior Matrix Unobserved Cells 3 Key Centre Trip Ends 15 Other Trip Ends Effect of Matrix Estimation Matrix estimation modifies the prior matrix to produce a matrix that more closely reproduces observed counts. A number of checks were made to ensure that the matrices are not unduly distorted by the matrix estimation process. These checks included: reviewing matrix changes at sector-sector and total matrix level; comparing trip length distributions and mean trip length of the input and estimated matrix; and reviewing changes to trip end totals due to the estimation process. Matrix Totals The matrix totals before and after matrix estimation are presented in Table 8.2 for each of the modelled time periods. Matrix estimation has changed matrix totals in the range 5-9% during the peak periods and by 4% in the inter-peak. These changes are small and were necessary to correct for shortfalls in observed demand, especially in the case of rail and Supertram which were shown to have lower modelled flows than observed in Chapter Comparisons of the prior and estimated matrices at a sector to sector level are presented in Appendix I. The changes show that the overall sector-sector pattern has not been distorted. Sheffield and Rotherham Public Transport Model 8.2

55 8 Assignment Validation After Matrix Estimation Table 8.2 Matrix Totals Before and After Matrix Estimation Period Before ME After ME % Change Morning Peak 27,949 29,406 5% Inter-peak 21,302 22,063 4% Evening Peak 23,279 25,338 9% Trip Length Distributions Trip length distributions for the prior and estimated matrix are shown in Figures 8.1 to 8.3 which show that the matrix estimation process has only had a minor impact on the trip length distribution. Mean trip lengths are shown in Table 8.3, with there being a slight increase in the average trip length in each time period. This increase in trip length is likely to be a result of the making up of the shortfall in rail and Supertram trips, which will have longer average trip lengths. 20% 18% 16% Percentage of Trips 14% 12% 10% 8% 6% Pre-Matrix Estimation Post Matrix Estimation 4% 2% 0% Trip Length (km) Figure 8.1 Morning Peak Trip Length Distributions Sheffield and Rotherham Public Transport Model 8.3

56 8 Assignment Validation After Matrix Estimation 25% 20% Percentage of Trips 15% 10% 5% Pre-Matrix Estimation Post Matrix Estimation 0% Trip Length (km) Figure 8.2 Inter-peak Trip Length Distributions 20% 18% 16% Percentage of Trips 14% 12% 10% 8% 6% Pre-Matrix Estimation Post Matrix Estimation 4% 2% 0% Trip Length (km) Figure 8.3 Evening Peak Trip Length Distributions Sheffield and Rotherham Public Transport Model 8.4

57 8 Assignment Validation After Matrix Estimation Table 8.3 Mean Trip Lengths Before and After Matrix Estimation (kilometres) Period Before ME After ME Difference AM Peak % InterPeak % PM Peak % Trip Ends Changes to trip end totals as a result of matrix estimation were examined to check for large changes in trips to/from any zones. The findings were as follows: the largest percentage changes in trip ends occur in zones with relatively low trip end totals; and the largest absolute variations in trip ends occur in zones with relatively high trip end totals Plots of trip end totals for each zone before and after matrix estimation are included in Appendix I, annotated with the Y=X line to show how good the correlation is between the two matrices. A close relationship is indicated by the points being close to the line. All the plots contained in Appendix I exhibit a strong correlation between the prior and post matrix estimation matrices illustration that the trip ends were not substantially modified by the matrix estimation process. 8.4 Bus Occupancy Validation A summary of the bus occupancy validation for the cordons and screenlines is presented in Table 8.4 showing the percentage difference between the total model flow and the total count crossing each cordon and screenline. Table 8.4 also shows the percentage of screenlines and cordons passing the validation criteria that modelled flows should be within 15% of the observed values on screenlines and cordons The validation in terms of the bus occupancy counts shows a vast improvement following matrix estimation, with all modelled screenline and cordon flows being within 15% of the observed counts, and two-thirds being within 5% Table 8.5 shows the percentage of links on each screenline and cordon where the validation criteria is met that modelled flows should be within 25% of individual counts except where observed flows are less than 150. Once again, the validation of the individual link flows is showing a vast improvement following matrix estimation with the majority of links satisfying the TAG criteria. It should be noted that on those cordons where not all of the links are meeting this criteria, there is only one link in each instance where this is the case. In the morning peak period 96% of the links with a flow greater than 150 satisfy the criteria, in the inter-peak 98% of links meet the criteria, and in the evening peak 92% of links meet the criteria. Sheffield and Rotherham Public Transport Model 8.5

58 8 Assignment Validation After Matrix Estimation Table 8.4 Percentage Difference Between Screenline and Cordon Flows and Observed Counts After Matrix Estimation Morning Peak Inter-peak Evening Peak Sheffield Inner Cordon Inbound -3% -1% 5% Outbound 0% -2% -8% Sheffield North East Screenline Inbound 5% -1% -3% Outbound -3% -4% 3% Sheffield North West Screenline Inbound 6% 0% 0% Outbound 5% -2% 0% Sheffield South East Screenline Inbound -3% -1% 0% Outbound 2% 4% -3% Sheffield South West Screenline Inbound -2% -4% -2% Outbound -7% 1% -5% Sheffield University Screenline Inbound -6% -6% -8% Outbound 6% -13% -1% Rotherham Cordon Inbound 0% 4% 8% Outbound 10% 3% -4% Screenlines Passing Criteria 100% 100% 100% Sheffield and Rotherham Public Transport Model 8.6

59 8 Assignment Validation After Matrix Estimation Table 8.5 Percentage of Screenline Links with Observed Flow Greater than 150 where Modelled Flow is within 25% of Observed Flows After Matrix Estimation Morning Peak Inter-peak Evening Peak Sheffield Inner Cordon Inbound 100% 100% 88% Outbound 100% 90% 92% Sheffield Outer Screenlines Inbound 100% 100% 100% Outbound 100% 100% 100% Rotherham Cordon Inbound 88% 100% 100% Outbound 75% 100% 100% Total 96% 98% 92% 8.5 Supertram Occupancy Validation The comparison of the modelled Supertram flow with the observed count crossing the inner and outer Sheffield cordons is presented in Table 8.6 showing that the criteria that modelled flows over cordons should be within 15% of the observed is met in all cases. This represents a vast improvement on the pre-matrix estimation assignments where the majority of the cordons flows did not meet this criteria The percentage of Supertram links with an observed flow of at least 150 that have the model flow within 25% are shown in Table 8.7. All of links satisfy the TAG criteria in each time period. Once again this represents a significant improvement on the validation before matrix estimation when at most 67% of the links within a time period were meeting the criteria, and in the peak period only 33% were meeting the criteria in the peak direction. Sheffield and Rotherham Public Transport Model 8.7

60 8 Assignment Validation After Matrix Estimation Table 8.6 Percentage Difference Between Modelled and Observed Supertram Cordon Flows After Matrix Estimation Morning Peak Inter-peak Evening Peak Inner Cordon Inbound -6% -2% 4% Outbound -2% -2% -4% Outer Cordon Inbound 1% 2% -1% Outbound 6% 0% -3% Table 8.7 Percentage of Supertram links with an Observed Flow Greater than 150 where Modelled Flow is within 25% of Observed Flows After Matrix Estimation Direction Morning Peak Inter-peak Evening Peak Inbound 100% 100% 100% Outbound 100% 100% 100% All Counts 100% 100% 100% 8.6 Rail Boarding and Alighting Validation The comparison of the number of persons modelled entering and leaving each station compared with the observed counts is provided in Table 8.8. The validation is vastly improved following matrix estimation, and in the case of Sheffield and Meadowhall stations the modelled flow is within 15% of the observed with in all instances. The modelled flows at Rotherham are outside of the 15% range, but then the observed flows at this station are below 150, and are therefore not expected to achieve even the 25% criteria. The overall number of passengers entering and leaving rail stations is within 6% in each case The proportion of the entry and exit links with an observed flow greater than 150 and the model flow within 25% of the observed are shown in Table 8.9. This shows that the criteria is met in all cases after matrix estimation, representing a significant improvement on the validation in the inter-peak and evening peak periods. Sheffield and Rotherham Public Transport Model 8.8

61 8 Assignment Validation After Matrix Estimation Table 8.8 Percentage Difference between Modelled and Observed Passengers Entering and Leaving Stations After Matrix Estimation Morning Peak Inter-peak Evening Peak Station Entering Leaving Entering Leaving Entering Leaving Meadowhall -7% 10% 3% 3% 13% -11% Rotherham 17% 3% 26% 50% 28% 24% Sheffield -3% -6% -9% -11% -5% -8% Total -1% -4% -3% -3% 0% -6% Table 8.9 Percentage of Rail Links with an Observed Flow Greater than 150 where Modelled Flow is within 25% of Observed Flow After Matrix Estimation Corridor Morning Peak Inter-peak Evening Peak Station Entry 100% 100% 100% Station Exit 100% 100% 100% 8.7 Conclusions As reported in this chapter, applying matrix estimation has led to a much improved validation of bus occupancies, Supertram occupancies and the number of passengers entering and leaving rail stations. This improvement in the validation has been achieved even though the matrix estimation process has resulted in only relatively minor changes to the distribution of demand. Sheffield and Rotherham Public Transport Model 8.9

62 9 Assignment Validation With Crowding 9.1 Introduction Chapter 7 presented the validation of the model before the use of matrix estimation techniques, and concluded that the validation was not satisfactory compared to the validation criteria set out in TAG. Therefore matrix estimation was employed in order to improve the validation, the validation results after matrix estimation being presented in Chapter 8. Following the validation of the model, the crowding model has been applied in order that the capacity available on services, and the levels of crowding, can impact on passengers behaviour This chapter outlines the effect that the application of the crowding model has had on the validation statistics in an identical manner to the previous chapter. Details on the derivation of the crowding curves used in the assignment are provided in Appendix J. The detailed validation statistics are presented in Appendix K. 9.2 Bus Occupancy Validation A summary of the bus occupancy validation for the cordons and screenlines is presented in Table 9.1 showing the percentage difference between the total model flow and the total count crossing each cordon and screenline. Table 9.1 also shows the percentage of screenlines and cordons passing the validation criteria that modelled flows should be within 15% of the observed values on screenlines and cordons. The validation in terms of the bus occupancy counts shows that all modelled screenline and cordon flows are within 15% of the observed counts. Therefore, the application of the crowding model has not had an impact on the validation of the screenline and cordon flows Table 9.2 shows the percentage of links on each screenline and cordon where the validation criteria is met that modelled flows should be within 25% of individual counts except where observed flows are less than 150. It should be noted that on those cordons where not all of the links are meeting this criteria, there is only one link in each instance where this is the case. The validation results in this table are unchanged as a result of the application of the crowding model. In the morning peak period 96% of the links with a flow greater than 150 satisfy the criteria, in the inter-peak 98% of links meet the criteria, and in the evening peak 92% of links meet the criteria. Sheffield and Rotherham Public Transport Model 9.1

63 9 Assignment Validation With Crowding Table 9.1 Percentage Difference Between Screenline and Cordon Flows and Observed Counts After Matrix Estimation Morning Peak Inter-peak Evening Peak Sheffield Inner Cordon Inbound -2% -1% 5% Outbound 1% -2% -7% Sheffield North East Screenline Inbound 6% -2% -3% Outbound -1% -4% 5% Sheffield North West Screenline Inbound 8% 0% 1% Outbound 5% -2% -1% Sheffield South East Screenline Inbound 3% -1% 0% Outbound 3% 5% 3% Sheffield South West Screenline Inbound -2% -4% -2% Outbound -1% 1% -5% Sheffield University Screenline Inbound -8% -6% -8% Outbound 4% -13% -2% Rotherham Cordon Inbound -1% 0% 7% Outbound 4% 0% -5% Screenlines Passing Criteria 100% 100% 100% Sheffield and Rotherham Public Transport Model 9.2

64 9 Assignment Validation With Crowding Table 9.2 Percentage of Screenline Links with Observed Flow Greater than 150 where Modelled Flow is within 25% of Observed Flows After Matrix Estimation Morning Peak Inter-peak Evening Peak Sheffield Inner Cordon Inbound 100% 100% 88% Outbound 100% 90% 92% Sheffield Outer Screenlines Inbound 100% 100% 100% Outbound 100% 100% 90% Rotherham Cordon Inbound 88% 100% 100% Outbound 75% 100% 100% Total 96% 98% 92% 9.3 Supertram Occupancy Validation The comparison of the modelled Supertram flow with the observed count crossing the inner and outer Sheffield cordons is presented in Table 9.3 showing that the criteria that modelled flows over cordons should be within 15% of the observed is met in all cases. The percentage of Supertram links with an observed flow of at least 150 that have the model flow within 25% are shown in Table 9.4. All of links satisfy the TAG criteria in each time period, showing that the crowding model has not had an impact on the validation of the model. Sheffield and Rotherham Public Transport Model 9.3

65 9 Assignment Validation With Crowding Table 9.3 Percentage Difference Between Modelled and Observed Supertram Cordon Flows After Matrix Estimation Morning Peak Inter-peak Evening Peak Inner Cordon Inbound -10% -2% 3% Outbound -4% -2% -7% Outer Cordon Inbound -4% 2% -2% Outbound 5% 0% -5% Table 9.4 Percentage of Supertram links with an Observed Flow Greater than 150 where Modelled Flow is within 25% of Observed Flows After Matrix Estimation Direction Morning Peak Inter-peak Evening Peak Inbound 100% 100% 100% Outbound 100% 100% 100% All Counts 100% 100% 100% 9.4 Rail Boarding and Alighting Validation The comparison of the number of persons modelled entering and leaving each station compared with the observed counts is provided in Table 9.5. The validation is vastly improved following matrix estimation, and in the case of Sheffield and Meadowhall stations the modelled flow is within 15% of the observed in all instances, except for leaving Meadowhall station. The modelled flows at Rotherham are outside of the 15% range, but then the observed flows at this station are below 150, and are therefore not expected to achieve even the 25% criteria. The overall number of passengers entering and leaving rail stations is within 7% in each case The proportion of the entry and exit links with an observed flow greater than 150 and the model flow within 25% of the observed are shown in Table 9.6. This shows that the criteria is met in all cases after matrix estimation, illustrating that the model still validates satisfactorily following the application of the crowding model. Sheffield and Rotherham Public Transport Model 9.4

66 9 Assignment Validation With Crowding Table 9.5 Percentage Difference between Modelled and Observed Passengers Entering and Leaving Stations After Matrix Estimation Morning Peak Inter-peak Evening Peak Station Entering Leaving Entering Leaving Entering Leaving Meadowhall -1% 15% 4% 5% 14% 16% Rotherham 24% 8% 35% 63% 33% 30% Sheffield -3% -5% -8% -10% -6% -8% Total -1% -2% -2% -2% 0% -7% Table 9.6 Percentage of Rail Links with an Observed Flow Greater than 150 where Modelled Flow is within 25% of Observed Flow After Matrix Estimation Corridor Morning Peak Inter-peak Evening Peak Station Entry 100% 100% 100% Station Exit 100% 100% 100% 9.5 Conclusions As reported in this chapter, the application of the crowding model has not had a significant impact on the validation of bus occupancies, Supertram occupancies and the number of passengers entering and leaving rail stations. Sheffield and Rotherham Public Transport Model 9.5

67 10 Conclusions 10.1 Overview This report has described the work undertaken by MVA Consultancy to transfer the public transport assignment model of Sheffield and Rotherham from the TRIPS software suite to Voyager, and to update it for The model has been updated using origin and destination data collected using surveys of public transport passengers in the key centres of Sheffield, Meadowhall and Rotherham The validation of the model prior to undertaking matrix estimation is poor for each of the public transport sub-modes. However, having undertaken matrix estimation, the validation has improved significantly with the model having been shown to be robust and compliant with the validation measures in the detailed guidance. The same applies following the application of the crowding model The model base year is 2008, with the following time periods represented: morning peak hour: inter-peak hour: average of evening peak hour: Demand Data Demand matrices for the PT model were developed using origin and destination surveys of passengers waiting to board bus, rail and Supertram services in Sheffield, Rotherham and Meadowhall. The movements that are not observed in the surveys have been infilled using gravity model techniques Supply Data The supply representation was developed from the previous version of public transport model (SRPTM2). The bus, rail and Supertram services were initially taken from previous version, with the service definitions thoroughly reviewed and updated for Model Parameters and Algorithms Voyager was used to implement the assignment algorithm making use of the network development, route enumeration and route evaluation techniques. Assignment parameters (value of time, walk and wait time factors, boarding and interchange penalties) were set with reference to relevant DfT guidance, with modification during the model calibration process Validation Chapters 5 to 9 report on the model validation in line with the DfT guidance. Model calibration and validation followed the advice given in both TAG unit Road Traffic and Sheffield and Rotherham Public Transport Model 10.1

68 10 Conclusions Public Transport Assignment Modelling and Major Scheme Appraisal in Local Transport Plans Part 3: Detailed Guidance on Forecasting Models for Major Public Transport Schemes The supply side validation, presented in Chapter 5, demonstrates that the model contains a satisfactory replication of the 2008 public transport provision in Sheffield and Rotherham. The following checks were carried out: comprehensive checking of network coding; comparison of modelled bus flows with observed vehicle flows; comparison of modelled and observed journey times on bus corridors; and checking of modelled routeings for plausibility The validation of the demand matrices, presented in Chapter 6, showed that the matrices are of the correct order of magnitude by comparing: demand matrices with screenline and cordon counts; and an estimate of annual patronage from the matrix totals with SYPTE annual patronage estimates Detailed assignment validation of the model before and after matrix estimation was presented in Chapters 7 and 8 respectively, with validation after the application of the crowding model presented in Chapter 9. Validation was undertaken utilising: bus passenger flows across screenlines and cordons; passengers entering and leaving rail stations; and Supertram passenger flows across inner and outer screenlines Conclusion This report has demonstrated that the Sheffield and Rotherham Public Transport Model validated well against the criteria provided in TAG and is therefore a robust tool for testing public transport schemes. The validation of the model has been carried out following the guidance given in TAG, and a summary of the findings of this report are set out below. The network and lines files have been validated to show that they reflect the public transport supply, modelled bus journey times replicated observed times, modelled bus vehicle flows match observed flows and modelled route choice is reasonable. Matrix validation checks reported here suggest that demand matrices are of the right order of magnitude. Model validation, following matrix estimation, satisfies the criteria for public transport model validation laid out in TAG. The model validates well in terms of bus occupancies, Supertram occupancies and the number of passengers entering and leaving rail stations and the matrix estimation process has resulted in only relatively minor changes to the distribution of demand. The model validates well after the application of the crowding model. Sheffield and Rotherham Public Transport Model 10.2

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70 Appendix A Summary of Public Transport Services

71 First Sheffield Non-Overground Voyager Line Number Forward Frequency Reverse Frequency Service Forward Reverse Description AM IP PM AM IP PM Norton Lees - Hillsborough Crystal Peak - Hillsborough Crystal Peak - Hillsborough Sheffield - Millhouses Sheffield - Millhouses City Centre - Darnall Circle Darnall - Shirecliffe Sheffield - Parsons X - Meadowhall A Woodhouse -Bradway Meadowhall - Killamarsh Dore - Crystal Peaks Dore - Sheffield Hillsborough - Jordanthorpe Hillsborough - Norton Lees Firth Park - Meadowhall - Sheffield Firth Park - Meadowhall - Sheffield Sheffield - Stocksbridge (Unsliven) Sheffield - Stocksbridge (Unsliven) A Sheffield - Stocksbridge (Unsliven) Sheffield - High Green Sheffield - High Green Ringinglow - Sheffield Millhouses to Sheffield Fulwood - Sheffield Sheffield - Buxton Sheffield - Castleton Sheffield - Bakewell 1 service x Woodhouse station - Crookes Meadowhall - Tickhill First Sheffield Overground Voyager Line Number Forward Frequency Reverse Frequency Service Forward Reverse Description AM IP PM AM IP PM Stannington - Sheffield Hall Park Head - Sheffield Woodhouse Cross - Wisewood Woodhouse Cross - Loxley Hemsworth - Hillsborough A Hemsworth - Hillsborough Nether Edge - Wybourn Sheff - Halfway Tram Royal Hallamshire - Crystal Peaks Herdings - Shiregreen Circle Herdings - Shiregreen Circle Charnock - Lodge Moor Woodhouse - Crookes Bradway - Parson Cross Sheffield Interchange - Fulwood Jordanthorpe - Ecclesfield Low Edges - High Green Western Bank - Western Bank Bents Green - Stannington Bents Green - Ecclesall Walkley - Birley Walkey - Dyke Vale Totley - Ecclesfield Totley Brook - Ecclesfield X Sheff-M-Roth_Con-Donc

72 Stagecoach Sheffield Voyager Line Number Forward Frequency Reverse Frequency Service Forward Reverse Description AM IP PM AM IP PM Woodhouse - Bradway Sheffield - Chesterfield Sheffield- Chesterfield Chesterfield - Sheffield Woodhouse - Hillsborough Mansfield - Sheffield Ecclesfield - Bents Green High Green - Bents Green Halfway - Fulwood Crystal Peaks - Walkley Crystal Peaks - Sheffield Chesterfield - Sheffield TF Supertram Link: Middlewood to Stocksbridge Sheffield - Penistone Sheffield - Barnsley Other Sheffield Services Voyager Line Number Forward Frequency Reverse Frequency Service Forward Reverse Description AM IP PM AM IP PM Manor Park - Upperthorpe Sheffield - Bakewell 1 service P1 911 Southey - Fox Hill - Southey P2 912 Southey - Fox Hill - Southey /214 Sheffield - Matlock Crystal Peaks - Dinnington Sheffield - Chesterfield Sheffield - Dronfield A Sheff - Wav - Roth Woodhouses Tesco-Sheff Woodhouses Tesco-Uni M Dore - Jordanthorpe FreeBee 9901 Sheffield City Centre Circle Hillsborough - Storrs Hillsborough - Oughtbridge M Harley - Fox Hill/Grenoside Manvers - Chapeltown Manor Top - Sheffield 60 1 service c1 c1 Millhouses - Nether Edge 60 c2 c2 Millhouses - Norton Lees 60 c3 c3 c31 Millhouses - Hemswortth 60 c4 c4 c41 Millhouses - Low Edge 60 M Sheffield - Broomhall HO Northern Hosp to RH Hospital Sheffield - Dronfield s6 62 Hillsborough - Wisewood Sheffield - Bents Green Sheffield - Jordanthorpe Crystal Peak - Sheffield M Sheffield - Firth Park Hillsborough - Bradfield - HB Dirc Hillsborough - Bradfield - HB Dirc Sheffield Interchange - Hillsborough A Sheffield - Langsett - Hillsborough Hillsborough - Rivelin - Hillsborugh

73 Rotherham First Overground Voyager Line Number Forward Frequency Reverse Frequency Service Forward Reverse Description AM IP PM AM IP PM 1 13 Roth Int - Maltby Circle Roth Int - Maltby Circle RothInt - Blackburn Roth - Kimberworth Roth - Asda Meadowhall - Roth - Herringthorpe Rotherham - East Herringthorpe Rotherham - Handsworth - Sheffield Rotherham - Teeton Rotherham - Whiston Circ Rotherham - Thrybergh Rotherham - Wingfield Roth - Kim - Roth Roth - Kim - Roth Rotherham First Non Overground Voyager Line Number Forward Frequency Reverse Frequency Service Forward Reverse Description AM IP PM AM IP PM Rotherham - Maltby Rotherham - Thorpe Hesley Roth - Meadowhall A Roth - Meadowhall Rotherham - High Green - Sheffield Roth - Magna - Sheff Rotherham - Ravensfield Rotherham - Ravensfield Common Sheffield - Rotherham Sheffield - Spink Hill Sheffield - Dinington - Roth A Sheffield - Dinington - Roth B Sheffield - Dinington - Roth Roth - Aston - Dinnington Rotherham - Crystal Peaks M Harley - Chapeltown - Grenoside X Sheffield - Maltby Sheffield - Aston Meadowhall - Dinnington Rotherham - brinsworth Meadowhall 60 60

74 Rotherham Other Voyager Line Number Forward Frequency Reverse Frequency Service Forward Reverse Description AM IP PM AM IP PM Roth - Raw - Roth Circ Roth - Raw - Roth Circ Rotherham - Mexborough - Doncaster Roth - Thyr - Maltby X Roth - Barnsley /19A Roth - Dinn - Worksop B Roth - Dinn Roth - Hoyland - Barnsley Barnsley - Wath upon Dearne - Rotherham Roth - Denaby Main - Donc Roth - Mex - Donc Treeton - Roth - Bram - Roth - Treeton a Dinninton - Doncaster Dinninton - Doncaster Rotherham - Ulley - Maltby Meadowhall - Dinnington Roth - Lea brook 180

75 Rail Services Voyager Line Number Forward Frequency Reverse Frequency Forward Reverse Description AM IP PM AM IP PM Sheff - Leeds via Dearne Sheff - Leeds via Barnsley Huddersfield - Sheffield Sheffield - Leeds via Barnsley (Express) Cleethorpes - Manchester Airport Sheffield - Scunthorpe Sheffield - Doncaster Adwick - Scunthorpe Adwick - Sheffield Sheffield - Hull Hull - Doncaster Sheff - Lincoln Scunthorpe - Lincoln via Sheffield Birmingham - Edinburgh via Leeds Plymouth - Edinburgh via Leeds Reading - Newcastle via Doncaster London St Pancras to Sheffield Norwich - Liverpool Manchester - Sheffield Doncaster - Leeds Nottingham - Leeds Tram Services Voyager Line Number Frequency Description AM IP PM Malin - Halfway Halfway - Malin Herdings - Cathedral Cathedral - Herdings Herdings - Meadowhall 30 Meadowhall - Herdings 30 Middlewood - Meadowhall Meadowhall - Middlewood

76 Appendix B Average Fare Calculations

77 Technical Note Project Title: Sheffield and Rotherham PT Model Update 2008 MVA Project Number: Subject: C37688 Average Bus Fares Note Number: 05 Version: 4 Author(s): Reviewer(s): Neil Benson James Blythe, John Allan Date: 22 April Introduction 1.1 For the Sheffield and Rotherham Public Transport model we need to create fare tables that set out the average fare paid for the distance travelled. In an earlier version of the model, we calculated the fare tables from Electronic Ticket Machine data. For the current update of the model we must calculate the fare table from concessionary fares data. 1.2 We need to create a fare table that relates the average fare paid to the distance. We have been given data with which we can create the fare table for adult single fares. To calculate the table for the average fares we plan to multiply the adult single fares table by a correction factor. The correction factor is the average fare paid by all travellers divided by the average fare paid by those buying for adult single tickets. 1.3 To assist with the calculation of the correction factor, we have been given both the average fare paid by all travellers and the average fare paid by people buying single tickets. But the average fare paid by all travellers is known to contain a small inconsistency. It has been calculated in a way that includes trips made using PTE tickets but excludes the cost of those tickets. 1.4 South Yorkshire Passenger Transport Executive (SYPTE) have provided additional data on travelmaster and other bus revenue, which we have incorporated into our calculation of average fare. The purpose of this note is to set out the calculations we have carried out to generate the correction factor. 1.5 This note has been prepared in conjunction with Technical Note 06, which looks at rail fares and Technical Note 07 which looks at Tram fares. 2 Average Fare 2.1 The following annual data for all ticket types were supplied and are shown in Table 2.1. However, it is known to exclude the cost of pre-paid tickets bought directly from the PTE. Average Bus Fares 1

78 Technical Note 05 Version: 4 Table 2.1 Passengers and Revenue, 2008 All ticket types Adult Single Total Revenue 3,759,007 3,264,037 Passengers 4,381,109 1,671,641 Average Fare A breakdown of passenger totals by ticket type is given in Table 2.2. They show that there are 7.1m bus passenger journeys using Pre-Paid PTE, the travelmaster tickets. However, revenue in Table 2.1 does not include this. In addition revenue from off-bus sales of operators own tickets: Pre-Paid Other, is not fully captured. Table 2.2 Bus Passengers, 2008 Ticket Category Passengers Adults 23,142,615 Blind 255,146 Child 11,566,207 Mobility 5,942,047 Non SY Concs 771,503 Other 4,844,048 Senior Citizens 29,220,229 Student 16/18 3,426,237 Zero Fare 907,578 Pre-Paid (Other) 32,734,701 Pre-Paid (PTE) 7,085,165 Total Demand 119,895, SYPTE provided MVA with additional revenue data: Off-bus sales revenue for pre-paid other is estimated at 1.3m Average Bus Fares 2

79 Technical Note 05 Version: 4 There are 9.3m TravelMaster trips per annum, raising revenue of 7.25m at an average fare of 0.78 across all modes. Of these, 7.1m use bus, which increases bus revenue by 5.5m. 2.4 With additional bus fare revenue of 6.8m per annum, average bus fare is 0.59 for Conclusion 3.1 Based on the data supplied, we will use an average bus fare of 0.59, which gives a correction factor to cash fares of 0.44 Average Bus Fares 3

80 Technical Note Project Title: Sheffield and Rotherham PT Model Update 2008 MVA Project Number: Subject: C37688 Proposed Rail Fares Note Number: 06 Version: 2 Author(s): Reviewer(s): Neil Benson John Allan, James Blythe Date: 22 April Introduction 1.1 For the Sheffield and Rotherham Public Transport model we need to create fare tables that set out the average fare paid for the distance travelled. In an earlier version of the model, we calculated the fare tables from Electronic Ticket Machine data. For the current update of the model we must calculate the fare table from concessionary fares data. 1.2 We need to create a fare table that relates the average fare paid to the distance. We have been given data with which we can create the fare table for adult single fares. To calculate the table for the average fares we plan to multiply the adult single fares table by a correction factor. The correction factor is the average fare paid by all travellers divided by the average fare paid by those buying for adult single tickets. 1.3 To assist with the calculation of the correction factor, we have been given both the average fare paid by all travellers and the average fare paid by people buying single tickets. But the average fare paid by all travellers is known to contain a small inconsistency. It has been calculated in a way that includes trips made using PTE tickets but excludes the cost of those tickets. 1.4 South Yorkshire Passenger Transport Executive (SYPTE) have provided additional data on travelmaster revenue, which we have incorporated into our calculation of average fare. The purpose of this note is to set out the calculations we have carried out to generate the correction factor. 1.5 This note has been prepared in conjunction with Technical Note 05, which looks at bus fares and Technical Note 07 which looks at Tram fares. Proposed Rail Fares 1

81 Technical Note 06 Version: 2 2 Average Fare 2.1 The following annual data for all ticket types were supplied and are shown in Table 2.1. Table 2.1 Rail Passengers and Revenue, 2008 All ticket types Adult Single and Returns Total Revenue 3,759,007 3,264,037 Passengers 4,381,109 1,671,641 Average Fare A breakdown of passenger totals by ticket type is given in Table 2.2. They show that there are 1.1m rail passenger journeys using Pre-Paid PTE, the travelmaster tickets. However, revenue in Table 2.1 does not include this. Table 2.2 Rail Passengers, 2008 Ticket Category Passengers Adults 1,678,559 Blind 4,399 Child 332,951 Mobility 156,568 Non SY Concs 12,398 Other 138,026 Senior Citizens 600,770 Student 16/18 18,331 Zero Fare 3,510 Pre-Paid (Other) 334,419 Pre-Paid (PTE) 1,101,179 Total Demand 4,381, SYPTE provided MVA with additional revenue data: Proposed Rail Fares 2

82 Technical Note 06 Version: 2 There are 9.3m TravelMaster trips per annum, raising revenue of 7.25m at an average fare of 0.78 across all modes. Of these, 1.1m use rail, which increases rail revenue by 0.85m. 2.4 With additional revenue of 0.85m per annum, average rail fare is 1.05 for Conclusion 3.1 Based on the data supplied, we will use an average rail fare of 1.05, which gives a correction factor to cash fares of 0.54 Proposed Rail Fares 3

83 Technical Note Project Title: Sheffield and Rotherham PT Model Update 2008 MVA Project Number: Subject: C37688 Proposed Tram Fares Correction Factor Note Number: 07 Version: 3 Author(s): Reviewer(s): Neil Benson John Allan, James Blythe Date: 22 April Introduction 1.1 For the Sheffield and Rotherham Public Transport model we need to create fare tables that set out the average fare paid for the distance travelled. In an earlier version of the model, we calculated the fare tables from Electronic Ticket Machine data. For the current update of the model we must calculate the fare table from concessionary fares data. 1.2 We need to create a fare table that relates the average fare paid to the distance. We have been given data with which we can create the fare table for adult single fares. To calculate the table for the average fares we plan to multiply the adult single fares table by a correction factor. The correction factor is the average fare paid by all travellers divided by the average fare paid by those buying for adult single tickets. 1.3 To assist with the calculation of the correction factor, we have been given both the average fare paid by all travellers and the average fare paid by people buying single tickets. But the average fare paid by all travellers is known to contain a small inconsistency. It has been calculated in a way that includes trips made using PTE tickets but excludes the cost of those tickets. 1.4 South Yorkshire Passenger Transport Executive (SYPTE) have provided additional data on travelmaster revenue, which we have incorporated into our calculation of average fare. The purpose of this note is to set out the calculations we have carried out to generate the correction factor. 1.5 This note has been prepared in conjunction with Technical Note 05, which looks at bus fares and Technical Note 06 which looks at rail fares. Proposed Tram Fares Correction Factor 1

84 Technical Note 07 Version: 3 2 Average Fare 2.1 The following annual data for all ticket types were supplied and are shown in Table 2.1. Table 2.1 Tram Passengers and Revenue, 2008 All ticket types Adult Single and Returns Total Revenue 10,053,684 3,303,326 Passengers 14,671,700 2,124,392 Average Fare A breakdown of passenger totals by ticket type is given in Table 2.2. They show that there are 1.13m tram passenger journeys using Pre-Paid PTE, the travelmaster tickets. However, revenue in Table 2.1 does not include this. Table 2.2 Tram Passengers, 2008 Ticket Category Passengers Adults 2,129,856 Blind 17,005 Child 1,497,352 Mobility 503,586 Non SY Concs 18,337 Other 383,701 Senior Citizens 2,376,668 Student 16/18 426,335 Zero Fare 68,541 Pre-Paid (Other) 6,115,569 Pre-Paid (PTE) 1,134,661 Total Demand 14,671, SYPTE provided MVA with additional revenue data: Proposed Tram Fares Correction Factor 2

85 Technical Note 07 Version: 3 There are 9.3m TravelMaster trips per annum, raising revenue of 7.25m at an average fare of 0.78 across all modes. Of these, 1.13m use tram which increases rail revenue by 0.88m. 2.4 With additional revenue of 0.88m per annum, average tram fare is 0.83 for Conclusion 3.1 Based on the data supplied, we will use an average tram fare of 0.83, which gives a correction factor to cash fares of 0.54 Proposed Tram Fares Correction Factor 3

86 Appendix C Creating Matrices from the Observed Public Transport Survey Data

87 Technical Note Project Title: Sheffield PT Model Update 2008 MVA Project Number: Subject: C37688 Matrix Building Methodology Note Number: 1 Version: 1 Author(s): Reviewer(s): James Blythe John Allan Date: 07 November Introduction 1.1 The purpose of this note is to outline the methodology employed in the development of the base year matrices for the 2008 update of the Sheffield and Rotherham Public Transport Model. This updated public transport model is known as SRPTM3, and this follows on from the two previous versions of the model which used matrices developed from the following sources: SRPTM1 used matrices from the South and West Yorkshire Multi Modal Model (SWYMMS) public transport model; SRPTM2 used matrices developed from public transport passenger surveys undertaken during 2007 and infill matrices developed from Electronic Ticket Machine (ETM) data. 1.2 SRPTM2 formed part of the SRTM2 modelling system, with the public transport model feeding into a DIADEM demand model, which required public transport matrices segmented by business, commuting and other journey purposes and with car or no car available segmentation. The demand model for SRTM3, which SRPTM3 is a part of, requires a much greater level of segmentation of the matrices, as described in Section The matrices have been built using the public transport passenger surveys undertaken during Not all journeys in the study area will have been observed in these public transport surveys, so infilling of the matrices was also required, which has been undertaken using gravity model techniques. The infilling of the matrices using gravity model techniques is described in Technical Note 12 Infilling of Public Transport Matrices. 1.4 Following this introductory Section, the remainder of this Note is structured as follows: Section 2 describes the public transport surveys undertaken during 2007; Section 3 outlines the segmentation required for the public transport matrices in order to meet the requirements of the public transport and demand models; and Section 4 describes the methodology to be employed in creating observed matrices, at the required segmentation, from the surveys. Matrix Building Methodology 1

88 Technical Note 1 Version: 1 2 Public Transport Surveys 2.1 Public transport survey data was collected for the development of the matrices used in SRPTM2, with face-to-face interviews of passengers waiting to board public transport services undertaken in the following locations: Sheffield, Meadowhall and Rotherham railway stations; Meadowhall Supertram stop; all Supertram stops within the City Centre, and also along West Street, Upper Hanover Street and Netherthorpe Road; all bus stops within Sheffield City Centre (including Sheffield Interchange), along West Street and at the Royal Hallamshire Hospital; all bus stops at Meadowhall Interchange; and all bus stops at Rotherham Interchange. 2.2 These interviews were designed to establish the following details about the journey being made: the postcode of the origin and destination of the journey, or as much detail as possible about the origin or destination; the access and egress mode for the journey; the reason for being at the origin and for going to the destination; the timing of the equivalent journey in the reverse direction, where applicable; and whether a car was available for the journey being made. 2.3 Counts of passengers boarding and alighting services at each of the survey locations were undertaken at the same time as the interviews. In the case of the rail stations, the counts that were undertaken were platform entry and exit counts as the length of some trains makes it difficult to count passengers boarding and alighting. These counts will be used in expanding the interview records to give a full representation of trip making, and will also be used in the validation of the public transport model. 3 Public Transport Matrix Segmentation 3.1 The segmentation of the observed matrices has been undertaken in such a way that the requirements of the demand model are taken into account, as well as the requirements of the public transport model. Separate matrices have therefore been built for the following time periods: pre-am peak hour ( ); AM peak hour ( ); post-am peak hour ( ); early inter-peak average hour ( ); late inter-peak average hour ( ); Matrix Building Methodology 2

89 Technical Note 1 Version: 1 pre-pm peak hour ( ); PM peak hour ( ) and post-pm peak hour ( ). 3.2 Initially matrices were built for the morning peak period ( ), inter-peak period ( ) and evening peak period ( ). The morning peak and evening peak period matrices have been factored to produce the hour matrices, using separate factors by journey purpose derived from the survey data. 3.3 The matrices have the following journey purpose segmentation, with home based matrices built separately for both the from-home and to-home directions: home based work; home based employers business; home based education; home based shopping; home based other; non-home based employers business; and non-home based other. 3.4 As well as being segmented by journey purpose, the matrices have been segmented by the availability or not of a car for the journey being made. 4 Observed Matrices 4.1 As noted earlier, the public transport surveys were used to create demand matrices for SRPTM2. However, the segmentation of the matrices needed to be revisited for SRPTM2, and the opportunity has been taken to review and improve the matrix building process. A number of steps were taken to clean the data before building the matrices previously and this process has not been repeated, so it was assumed that all illogical records and records with missing data have been removed from the dataset. 4.2 The processing of the survey data to build the observed matrices is described in the following sections: the allocation of origin and destination zones to the survey records using the latest zone system; the creation of forward direction matrices from the survey data; the creation of reverse direction matrices from the survey data; the elimination of double counting in the observed matrices; and the smoothing of the observed matrices. Matrix Building Methodology 3

90 Technical Note 1 Version: 1 Allocation of Zone Numbers 4.3 Each of the survey records has previously had grid references added using the postcode for each of the origin and destination locations, and origin and destination zones had also been added. However, the changes to the zone system have required that the allocation of zones be repeated. In order to achieve this, the grid references were used to create points for each of the origins and destinations in turn in ArcGIS, and the origin and destination zone numbers were added by taking the zone number that the origin and destination points lie in. Creation of Forward Direction Observed Matrices 4.4 The public transport surveys have only surveyed a proportion of the passengers boarding services in each location, so in order to get a full set of observed records the interviews have been expanded to match boarding counts. 4.5 The expansion of the survey records to match the counts has been undertaken on the following basis: The bus interview records have been expanded to counts for each service and interview location. If more than one bus service has been stated for an interview record then the record was split between those services. For example, a person that has a choice of four services will have a quarter of an interview record applied to each service. The Supertram interview records were expanded by service and interview location. The service boarded was estimated from the destination of the journey, again with journeys covered by two services being divided between the two services. The rail interview records have been expanded by platform group, a platform group being defined as the platforms which have the same entry/exit. 4.6 In the case of bus services which have low levels of patronage there are instances of hours where there are no observations. In these cases the interview records for the period have been factored to match the period boarding counts. This ensures that the number of journeys within each time period is correct, and the methodology for removing the lumpiness within the matrices, outlined below, will ensure that these records are distributed throughout the time period. 4.7 One of the key considerations to be taken into account in expanding the survey records, particularly given the required level of segmentation, is that there is a need to avoid producing lumpy matrices. In order to minimise the lumpiness of the matrices, the following process was followed to create the matrices for the time periods required by the demand and public transport models: the expansion of the interviews within each modelled hour to match the boarding count for that modelled hour; the creation of matrices for each time period by combining the expanded matrices for the hours within the time period; the calculation of period to hour factors for each journey purpose and hour, and the application of these to the time period matrices to create the hour matrices. Matrix Building Methodology 4

91 Technical Note 1 Version: 1 Creation of Reverse Direction Observed Matrices 4.8 The passenger interview surveys have only captured trips that board services in each of the key centres. The opposite direction of these journeys are not surveyed but have been synthesised by reversing the forward direction records and expanding to the alighting counts that were undertaken at the same time as the interviews. In reversing the survey records the following issues have been taken into consideration: passengers do not necessarily use the same bus stop in the key centres for alighting as they do for boarding; the journey purpose profile for alighting passengers within a half hour time period will not be same as that for boarding passengers within the same time period. 4.9 The reverse direction survey records have been created by transposing the origins, destinations and journey purposes of the forward direction interview records. The full set of transposed records was used in the expansion to the alighting counts for each surveyed hour As highlighted above, the boarding location for a service in a Town or City centre is not necessarily the same as the alighting location. Therefore, the reverse direction records will be expanded to match the alighting counts by service for the whole of a surveyed area. The surveyed areas for the purpose of the reverse synthesis are those listed below: Sheffield City Centre; West Street/University; Meadowhall; and Rotherham Town Centre The expanded forward direction survey data was used to calculate the proportion of the journeys for each journey purpose that occurs in each hour. These proportions were used to allocate the reverse direction journeys for each journey purpose to the return time period Once the reverse direction journeys had been allocated to each hour, they were expanded to match the alighting counts using the same methodology as used for the forward direction matrices. The only differences in the expansion to counts was that for bus the counts were summed by service for the surveyed areas listed above and for rail the alighting counts were summed for the entire station at Sheffield. Elimination of Double Counting 4.13 There are a number of instances where journeys are represented twice in the survey records processed as detailed above. The journeys that are double counted are those that fall into either of the following categories: journeys that interchange in each of the key centres will be observed in the forward direction survey data for the mode they board and in the reverse data for the mode they alight from; and Matrix Building Methodology 5

92 Technical Note 1 Version: 1 journeys that board a service in one key centre and alight in another will be observed in the forward direction at the boarding location and the reverse direction at the alighting location for the same mode In order to eliminate the double counting that arises for the reasons given above, the records that meet the following criteria were multiplied by a half: journeys that board or alight in the key centres that have another public transport mode as the access/egress mode in that key centre; journeys between origins and destinations that are observed in both the forward direction at one key centre and the reverse direction at another, which use the same mode Having eliminated the double counting as described above, the resulting matrices are the matrices of observed movements. Matrix Smoothing 4.16 The use of survey data to produce matrices of observed trip movements produces matrices that are considered to be lumpy. A process of matrix smoothing can be applied to the matrices built in this manner to reduce the lumpiness of the matrices. Matrix smoothing uses a sector system, in this case consisting of approximately 100 sectors, and is based on the following principles: that the number of trips from sector to sector in the observed matrices is correct; and that the number of origins and destinations for each zone is correct The number of trips for a sector to sector movement is then spread amongst the cells within that sector, with the following formula used to calculate the number of trips making each zone to zone movement: Zone Origins Sector to Sector Trips Sector Origins Zone Destinations Sector Destinations 4.18 Matrix smoothing has been applied to the observed matrices created from each of the bus, rail and Supertram surveys. The matrices have then been combined for each of the morning, evening and inter-peak hours to create the observed matrices by journey purpose and car availability. Matrix Building Methodology 6

93 Appendix D Infilling Unobserved Movements using the Gravity Model

94 Technical Note Project Title: Sheffield and Rotherham Transport Model 3 MVA Project Number: Subject: C37688 Infilling of Public Transport Matrices Note Number: 12 Version: 5 Authors: Reviewers: John Allan and Pete Kidd James Blythe Date: 4 June Introduction 1.1 MVA Consultancy was commissioned during October 2008 by Sheffield City Council to update the Sheffield & Rotherham public transport model. The principal changes to the model were an upgrade of the software platform from Citilabs TRIPS suite to their VOYAGER suite, modelling of crowding on PT services, the use of SYPTE data in place of bus operators electronic ticket machine data, and refined segmentation of demand to fit with an enhanced demand model. 1.2 A key input to the demand matrices were the public transport surveys undertaken in Sheffield City Centre, Rotherham town centre and Meadowhall during These surveys formed the basis of the demand matrices produced for the previous version of the model (SRPTM2), in which ETM data was used to infill the matrices for those movements for which travel patterns were not fully observed. The ETM data used in the previous version of the model provided a comprehensive record of journeys across the area. However, a change to the methodology for infilling the matrices was required for the new model (SRPTM3) because the local bus operator s who supplied the ETM data wanted to hold a veto over what schemes the model could be used to investigate in the future. The client found this constraint unacceptable and therefore preferred an alternative approach to infilling the matrices. 1.3 At the outset of the project it was envisaged that the observed demand matrices would be infilled using additional survey data held by the SYPTE. Although not as comprehensive as the ETM data the expectation was that the SYPTE data would be adequate for the purpose of infilling the matrices. Unfortunately, late in the project it was found that the SYPTE data was not appropriate for infilling the matrices and an alternative method for infilling the PT matrices was required. 1.4 MVA Consultancy proposed therefore using gravity models to infill the unobserved data, another approach that TAG [Unit Par ] suggests is acceptable. Gravity models were estimated from the observed demand matrices and applied, whilst controlling trip-ends to row and column totals derived exogenously using NTEM trip rates. K-Factors were calculated to ensure that the models reproduced demand totals for cells in the matrix which had been fully observed. In a final step, the fully-observed demand matrices were merged with the synthetic demand estimates, for cells in the matrix which had not been fully observed. MVA Consultancy Infilling of Public Transport Matrices 1

95 Technical Note 12 Version: 5 argue that this approach produces PT matrices that are sufficiently robust to make them fit for the purpose of appraising Sheffield and Rotherham s upcoming Major Schemes. The main reasons for this are: the origin-destination surveys capture the most important movements relating to the schemes we wish to appraise using the model; and for the remaining movements, the surveys captured sufficient information for us to calibrate gravity models successfully. The trip-end estimates used in the gravity models are sufficiently robust. 1.5 The objective of this note is to set out details of the adopted methodology and its outturns. The Chapters have been structured as follows: Chapter 2 overall methodology; Chapter 3 trip end derivation; Chapter 4 estimation and application of gravity models; and Chapter 5 outturns and sense checks; 1.6 Chapter 6 provides a summary of this note, whilst Appendix A presents further details of the methods used to estimate the trip-ends. 2 Overall Methodology The Upcoming Major Scheme Business Cases 2.1 Sheffield City Council and South Yorkshire PTE are planning to submit three Major Schemes to DfT during 2009, which will be appraised using SRTM3. These are summarised as follows: Penistone Road Quality Bus Corridor the provision of bus lanes along the A61 Penistone Road combined with junction improvements to afford increased priority to buses as well as decrease delays for general traffic. The scheme stretches south from Wadsley Bridge to Sheffield City Centre. Bus Rapid Transit Routes two routes connecting Sheffield City Centre with Rotherham Town Centre. The schemes use high quality vehicles with limited stops and increased segregation. The Northern route runs via Meadowhall and the Southern route via Waverley. Observed Demand Matrices 2.2 An extensive survey programme capturing the key public transport trips across the Sheffield & Rotherham districts was undertaken in 2007, counting and interviewing passengers waiting to board buses, trams and trains. They aimed to capture details of the numbers of public transport trips and details of the origins, destinations and purposes. They covered the three main transport interchanges in the Study area: Sheffield City Centre, Meadowhall and Rotherham Town Centre. Figure 2.1 shows the locations of the surveys, which are in the key places for jobs, leisure and entertainment in the study area. Although not exhaustive these surveys focussed upon the area covered by Sheffield s Economic Masterplan and so future year public Infilling of Public Transport Matrices 2

96 Technical Note 12 Version: 5 transport passenger growth is expected to occur mainly in the places where the surveys fully observed demand. These movements correlate well with those impacted by the three upcoming major scheme business cases that primarily involve getting people to and from Sheffield City Centre, and the public transport surveys were specifically designed to capture those movements. Rotherham Meadowhal l Sheffield Figure 2.1 Location of PT Surveys and Major Scheme Routes 2.3 Once double counting was removed, the surveys captured 58% of the total trips in the Sheffield and Rotherham conurbation (see Table 2.1). Table 2.1 Observed Trips Compared to TEMPRO Totals 2.4 TEMPRO Fully Observed % Fully Observed 239, , Looking at this another way, the survey data fully observes 50% of the cells representing movements within the conurbation and about 58% of the non-zero cells, as can be seen in Table 2.2. In the estimate of the proportion of non-zero cells that have been fully observed, the number of cells that should be non-zero is itself an estimate. It has been estimated by identifying the cells where public transport trips can be made with less than 2 changes of vehicle. Infilling of Public Transport Matrices 3

97 Technical Note 12 Version: 5 Table 2.2 Fully Observed Cells in the Study Area Fully Observed Study Area All % Study Area Non-zero % 123, ,025 50% 213,776 58% 2.5 The fully observed proportion of the trips affected by the major schemes is likely to be much higher. Earlier work on the BRT scheme has shown that Sheffield City Centre, Rotherham Town Centre and Meadowhall account for the majority of the patronage. We have fully observed all trips to and from these places. For the Penistone Road route, the city centre is also a key destination. We believe that the surveys have captured a high proportion of trips affected by the major schemes. Infilling the Matrices 2.6 Despite the public transport surveys capturing all public transport movements passing through the urban centres of Sheffield and Rotherham and Meadowhall Interchange a proportion of travel across the conurbation remained unobserved. In order to avoid giving the bus operator a veto on the use of the model, we couldn t make use of ETM data to infill the matrices and couldn t survey on board the buses. An alternative would be to interview at all bus stops across the conurbation but this would be prohibitively expensive. A synthetic method for estimating the number of trips making movements not captured by the surveys was therefore required. For this project, the approach taken to infilling the observed public transport matrices involved: deriving trip ends using NTEM/TEMPRO as a data source; calibrating gravity models to the observed data; applying gravity models, whilst controlling forecasts to the exogenously derived trip ends; deriving K-Factors to ensure synthesised demand for cells considered fully-observed matched the observed demand; and merging the fully-observed demand with synthesised demand for cells not considered fully-observed. 2.7 The following sense checks were made in checking the plausibility of the final demand matrices; inspection of calibrated gravity models parameters; implied trip lengths (for both observed and synthetic sections of the matrices); and fit of the modelled link flows to counts following an initial assignment. 3 Trip End Derivation 3.1 We considered two options for the way in which we would use gravity models to estimate the numbers of trips making movements not captured by the surveys: gravity model using the Partial Matrix Technique; and Infilling of Public Transport Matrices 4

98 Technical Note 12 Version: 5 gravity model using trip-ends. 3.2 When employing the partial matrix technique, gravity model calibration involves estimating parameters that define the shape of the deterrence curve for observed data alone. In a final step, the gravity model applies this deterrence curve to all cells in the demand matrix (both fully observed and unobserved) producing a fully synthesised trip matrix. We rejected the partial matrix technique approach because we had tried it for the highway matrices and considered that it had not worked well. 3.3 For the highway model, matrices built with this technique contained substantially lower trip making than TEMPRO suggested for the area. Matrix estimation increased them substantially suggesting that the TEMPRO forecast was better. In fact, matrix estimation increased them to an unacceptably large extent, increasing the total by around 30%, suggesting that the partial matrix approach had not produced a good prior matrix. Matrices built with the other approach to gravity models estimating trip ends independently produced a much better fit. Matrix estimation adjusted them by only around 3%. The difference between the two approaches was clear even before matrix estimation, the fit to counts using matrices built from independent trip ends was better than the fit using matrices built from the partial matrix technique. 3.4 Given the problems with using the partial matrix technique for the highway model, we proposed to estimate the trip-ends for the public transport model. A helpful corollary was that the same method would then be used to infill both the highway and public transport matrices, which jointly form the basis of the matrices input to the demand model (SRDM3). The consistency of approach would help to preserve plausible mode-splits at the zonal level. 3.5 To estimate the trip productions, we considered three broad approaches: use TEMPRO trip-end totals to apply adjustment factors to trip-ends inferred using the partial matrix technique; use National Trip-End Model (NTEM) trip-rates; and use trip rates calculated from TEMPRO. 3.6 We chose the third option because it offered an approach that provided control at the level of individual zones unlike the first option and could be achieved with the available data (unlike the second). With access to more refined population data for each zone, we would have opted for using NTEM trip-rates. NTEM uses separate trip-rates for around 70 categories of person. To use them, we would need to know the number of people in each category in each zone. Unfortunately, we did not have this level information available and estimating it would not be possible within the budgets and timescales. 3.7 By calculating trip-rates from TEMPRO, we were able to produce trip rates at a level of disaggregation at which we had data for each zone, whilst maintaining overall consistency with NTEM. TEMPRO was built from the NTEM trip-rates and includes underlying estimates of the number of people in each of the NTEM categories in each of its zones. So our trip-end estimate would be consistent with NTEM trip-rates when aggregated over TEMPRO zones. 3.8 Trip rates were calculated separately for each TEMPRO area (the smallest spatial level that the data permitted). Calculated in this way the trip rates reflected to some extent the level of public transport service provision within an area, as the zoning system used in TEMPRO is designed to distinguish between settlements on the basis of population. Thus, lower trip rates were derived Infilling of Public Transport Matrices 5

99 Technical Note 12 Version: 5 for the more rural areas of the Sheffield and Rotherham districts than the urban areas where accessibility to public transport is superior. 3.9 Appendix A presents a memo discussing our proposed approach to estimating trip ends from TEMPRO. Trip End Estimates 3.10 Our estimates of trip-ends came from trip-rates calculated from TEMPRO, applied to population data derived from Census 2001 with an adjustment to match the latest (2007) mid-year population data and another to match 2008 levels of car ownership The trip-ends calculated for each zone accounted for differences between zones in terms of population, numbers of workers and levels of car ownership. To estimate trip-rates from TEMPRO, we divided TEMPRO productions by the population in TEMPRO. For employer s business trips the number of workers was used in the trip rate calculation rather than the number of people. Separate trip-rates were calculated by mode and car ownership level (carowning and non-car-owning) An issue was found relating to the mix of journey purposes in the fully observed cells filled using the survey data and the infilled cells. As the surveys were focussed on the city and town centres and Meadowhall they over represented work, commute, and shopping trips. However, in reality this was not a problem because we treated each journey purpose separately. The tripends calculated for each journey purpose were given the correct overall mix of journey purposes and the correct numbers of infill trips were calculated for each row and column in the matrix by subtracting the observed data from the totals. Table 3.1 Proportions of Observed and Fully Observed Trips Journey Purpose TEMPRO Fully Observed % Fully Observed Home Based Work 70,506 46, Home Based Employers Business 2,789 1, Home Based Education 43,050 3,777 9 Home Based Shopping 43,340 53, Home Based Other 63,442 17, Non-Home Based Employer s Business 1,595 1, Non-Home Based Other 14,374 14, TOTAL 239, , A further problem related to a mismatch between the estimates of the trip-ends compared to the fully observed data. For home based shopping, the number of observed trips exceeded the Infilling of Public Transport Matrices 6

100 Technical Note 12 Version: 5 trip ends estimated by TEMPRO. We examined the matrix building process and confirmed that double counting of trips was dealt with correctly. We believed the mis-match was due to the trip rates being derived from NTEM/TEMPRO, which have been estimated from a relatively small sample in NTS. Roughly 8000 households were used to generate trip rates by journey purpose and mode spread across 72 person categories and 10 types of settlement At a greater level of aggregation, the estimates are more likely to be representative. For the gravity model, we re-weighted the combined home-based-shopping and home-based-other journey purposes using a journey purpose split derived using data from the South Yorkshire Household Travel Survey (SYHTS). This helped smooth out the impacts of the low sample size of the home based other trips. The Trip Rates 3.15 Out method for deriving trip-ends required estimates of trip-rates. We couldn t use the SYHTS to estimate trip-rates because we could not be sure that people in the sample who recorded no travel did not in fact travel i.e. they did not simply neglect to respond to the questionnaire. We therefore calculated trip-rates using data from TEMPRO. In fact, the TEMPRO trip productions we used in calculating trip-rates were themselves calculated by applying trip-rates from NTEM to population data. However, we could not use the NTEM trip-rates directly because we did not have population data segmented into the appropriate categories. That said, by calculating our trip-rates from TEMPRO we ensured that the overall trip making matched the trip-making that would be forecast by applying NTEM trip-rates directly. Furthermore, the trip rates we calculated and the population data we used captured the main variation between zones We used different trip-rates by mode, echoing the approach used in NTEM. Trip-rates by mode are expected to remain constant over time as long as they account for car ownership levels. The decline in public transport trips over time is explained by people moving into household categories with higher car ownership levels which have lower public transport trip rates. The TEMPRO estimate accounts for changes in car ownership and household category. By calculating our trip rates from TEMPROs estimate of 2008 trip-ends, we are confident in the overall mode split implied by our car and public transport trip-end forecasts By way of example, Table 3.2 presents the from-home trip rates we used for the main urban and rural areas of Sheffield in the morning peak. It can be seen that the trip rates for the urban area are consistently higher than those for rural area, reflecting the superior public transport service provision in these areas. For commute, shop and other trip rates are higher for those without a car available than for those who are car available. This reflects the fact that those who are car available are more likely to undertake such trips by car than by public transport. Table 3.2 Example from-home morning peak trip rates for the urban and rural areas of Sheffield Urban Rural CO C1+ C0 C1+ HBW Infilling of Public Transport Matrices 7

101 Technical Note 12 Version: 5 HBEB HBED HBS HBO Table 3.3 presents example trip rates by purpose and time period for the urban area of Sheffield. For commute, from-home trip rates are highest in the morning peak and fall progressively through the day. The reverse pattern is seen for to-home trips. A similar pattern is seen for education although a higher proportion of to-home trips are made in the inter-peak than the pm period as schools typically finish for the day before 4pm. In contrast, trip rates for shop and other purpose are highest in the inter-peak, when people have a higher propensity to make such trips. Table 3.3 Example from-home and to-home trip rates by time period for the urban area of Sheffield From-home To-home AM IP PM AM IP PM HBW HBEB HBED HBS HBO The Population Data 3.19 The population data for each zone was calculated in the following way: Census 2001 data was allocated from output area to zone level by allocating data to postcodes then aggregating postcode level data to model zones The 2001 zonal level data was factored to 2007 by applying the ratio of population in 2007 to population in 2001, at the finest level available - lower super-output area. The car ownership levels were then adjusted from 2001 levels to 2008 levels using the ratio of 2001 to 2008 in the study area in TEMPRO The estimates of population therefore accounted for the changes in overall population and population by zone. They accounted for changes in the overall level of car ownership but they didn t fully account for differential changes in car ownership levels between zones. For the most Infilling of Public Transport Matrices 8

102 Technical Note 12 Version: 5 part, this is likely to be perfectly acceptable. In general, the character of housing areas will not change substantially over a few years the rich areas will tend to remain rich and the poor areas tend to remain poor. Areas with high car ownership will retain high car ownership. We believe that there are no better data sets that could improve the method for estimating car ownership level changes Once specified these trip rates were applied to zonal population data to produce production tripends by time period, journey purpose and car ownership. Attraction trip-ends were then produced using suitable attraction weights defined separately for each journey purpose Table 3.4 shows the variables used for each purpose in producing the production totals and also as weights in producing the attraction trip-ends. Although we initially tried various attraction weights they yielded far too few trips to the city centre and so we finally settled on using PT travel to work data from the Census for all purposes other than education for which we used resident population. Table 3.4 Production and Attraction variables Production Denominator Attraction Weight HBW Workers PT travel to work HBEB Workers PT travel to work HBED Population Resident population HBS Population PT travel to work HBO Population PT travel to work NHBEB Jobs PT travel to work NHBO Population PT travel to work 4 Estimation and Application of Gravity Models 4.1 As explained above, given the problems with using the partial matrix technique for the highway model, we proposed to estimate the trip-ends for the public transport model. TAG s guidance on the coverage of the survey required for the gravity model relates to the partial matrix gravity model: TAG Unit , paragraph states The partial matrix gravity model works best when the proportion of cells with missing non-zero numbers of trips is low, typically less than 30%. If the partial matrices are sparse, with data missing in more cells than present, consideration should be given to either using data from a pre-existing model or conducting more surveys 4.2 The key issue here is that this guidance relates to the gravity model applied in partial matrix mode. Since we did not use the gravity model in that mode, we did not need such Infilling of Public Transport Matrices 9

103 Technical Note 12 Version: 5 comprehensive coverage. This is because the partial matrix mode seeks to simultaneously estimate trip-ends and infill the partially observed cells. Since we supplied the trip-ends in our implementation of the gravity model, we needed only to infill the partially observed cells, which is clearly less demanding. It was therefore not necessary for our surveys to fully observe the 50% to 70% cells referred to above. 4.3 In partial matrix mode, the gravity model seeks to infer trip-ends totals from the observed cells and then distribute the trip-end totals across the matrix. Much like a game of Sudoku, this can only be achieved if a large enough proportion of the cells have been filled. The technique suffers a problem known as disconnectivity. Continuing the Sudoku analogy, the problem occurs where there are too few observed cells providing bridges between different rows and columns, so islands form within the matrix where the infill data is self-consistent within an island but not between islands. 4.4 By supplying the trip-end totals for every row and column, we make the bridges, so the target coverage of fully observed cells need not be so comprehensive. For this method, the requirement was that fully observed cells covered sufficient different trip lengths to allow the calibration of the deterrence curve to the trip length distribution. 4.5 For most journey purposes, we had enough observations to expect that this requirement would be met. However, for Employer s Business we had less than 100 in each peak, but over the whole day we had almost 250 observations. Although the sample size is small there are other reasons why this is not too much of a problem. In reality, few employer s business trips are undertaken by bus and the longer distance rail trips will be screened out of the gravity model process. The employer s business gravity model will therefore not need to treat large numbers of employer s business trips only about 1% of the total demand. In fact, a plausible deterrence function could not be calibrated to this small sample of observed data. A parameter was therefore imported from the DfT s guidance on accessibility, which calibrated parameters to match the distributions in the National Travel Survey 4.6 Taking all this into account, the chosen approach was to calibrate gravity models to the observed data in the same way as the partial matrix technique but not to go on to infer trip ends from the same data. Instead trip-ends were estimated independently and used in the production of the infilled matrices. This approach placed less reliance on the survey data and therefore required a smaller survey. In comparing the two approaches, the key question was therefore which produced the most plausible trip-ends. We believe that trip-ends are superior if estimated independently using a further data set. 4.7 As described in an earlier section to estimate trip-ends, we used a different approach for the production (home) end and attraction (non-home) end of the trips. The process was driven by the production end where we applied trip rates to population data to estimate the numbers of trips produced. At the attraction end, we are less certain about the actual numbers of trips attracted so the attraction trip-ends were expressed as attraction weights applied to the production totals. 4.8 The gravity models included only trips to/from and within the study area. For trips produced within the study area the trip-ends supplied to the gravity model were the product of the trip rate and the population data. For trips produced outside the study area, the trip-ends included only those trips entering the study area, which were obtained not from trip rates but from the survey data. Infilling of Public Transport Matrices 10

104 Technical Note 12 Version: The only exception to these rules was for zones towards the edge of the Rotherham district for which significant trips leave the study area (mostly attracted to Barnsley, Doncaster or Worksop). For these zones, the production trip ends were scaled down for input to the gravity model to only include those trips with attraction end within the study area. Trips attracted to zones beyond the study area were added back onto the matrices after the fully observed and synthetic matrices had been combined. Calibrating Gravity Models 4.10 We calibrated gravity models using the Citilabs software MVGRAM. In each case, the models were set up with productions (home end) as rows in the matrices and attractions as (non home end) columns, so the matrices for the to-home journey purposes needed transposing. We tried to calibrate models at various levels of segmentation to find the most disaggregate level at which the data could support the calibration: journey purpose by car ownership level and by time period; journey purposes by time period; and journey purpose (all-day) The parameters were imported for employers business and education. For employers business, the sample size was too small to support the calibration. For education, the city centre focus of the survey distorted the results because such a small proportion of the school places are in the city centre For each journey purpose and time period combination, the output of the calibration process was a pair of calibrated parameters X 1 and X 2. These parameters determined the shape of the deterrence curve. The form of the curve was given by the equation: where F ( C ) = C ij X1 ij e ( X 2C ij ) F(C ij )=cost deterrence for zone i to zone j C ij =generalised cost for zone i to zone X 1, X 2 = coefficients to be calibrated Checking how well the gravity models performed involved checking the output distributions of trips against generalised cost and trip length. Checks were undertaken at each of the subsequent processes. We were looking for the classic shape of the curve (see Figure 4.1), similar to a log-normal distribution, rising to a peak at a quite short trip-length and skewed to have a long tail. We compared distributions in different parts of the trip matrix and across the journey purposes expecting to see short mean trip lengths for education and shopping, medium for commute, and long for employer s business and other. Infilling of Public Transport Matrices 11

105 Technical Note 12 Version: 5 Demand Generalised Cost or Trip Length Figure 4.1 Typically Deterrence Function with Positive X 1 Value and Negative X Table 4.1 lists the parameters that were estimated by time period and purpose. Note that values for employers business and education could not be estimated and were therefore imported from DfT s guidance on accessibility Infilling of Public Transport Matrices 12

106 Technical Note 12 Version: 5 Table 4.1 Calibrated X 2 Parameters by time period and purpose AM IP PM HtW WtH HtEB EBtH HtED EDtH HtS StH HtO OtH NHBEB NHBO Applying Gravity Models 4.15 Having estimated trip-ends, the next step was to apply the gravity models, using the estimated trip productions, trip attraction weights, and calibrated parameters X 1 and X Intra-zonal demand for public transport was not synthesised for the smaller models zones that cover the main urban areas of Sheffield and Rotherham as non-car intra-zonal demand for travel within these zones is included in the slow mode matrices. However, public transport intrazonals trips were synthesised for the larger zones covering the more rural areas of the Sheffield and Rotherham districts. Attraction Weight Adjustment 4.17 After running the gravity model once, the attraction weights were adjusted to try to match the demand to Sheffield City Centre, Rotherham Town Centre and Meadowhall that was fully observed. For these zones, the attraction trip ends aggregated across these zones needed to match the observed demand. K-Factors 4.18 After running the gravity model a further time, a set of sector level K-factors were calculated to ensure consistency between the synthetic data and the observed data. This step was important Infilling of Public Transport Matrices 13

107 Technical Note 12 Version: 5 for enabling us to overwrite the synthetic data with observed data whilst preserving the estimated trip-end totals. The K-Factors were calculated at sector level (see Figure 4.2), where the sectors define which movements can be considered fully observed. After this stage, the distributions were checked Figure 4.2 shows the 17 sector system used to define which movements were fully-observed. Allocating zones in this way to spatial corridors allowed us to specify relatively easily which sector to sector movements would have been observed fully and which would not. Sheffield is built on a series of hills which means orbital routes are very difficult. Such travel is therefore forced to travel through the city centre where the surveys were undertaken. Movements across the town centres which could be done on a through bus were said to be not fully observed as an interchange would need to have taken place for a trip to be observed by our surveys. Sector 1 is Sheffield City Centre, Sector 2 Meadowhall, Sector 3 Rotherham Town Centre and Sector 17 outside Sheffield & Rotherham districts Table 4.2 shows a matrix where a 1 indicates that movement is fully observed. Table 4.2 Fully observed movements are marked with a 1 for the 17 sector system Infilling of Public Transport Matrices 14

108 Technical Note 12 Version: 5 Figure Sector System Defining Fully Observed Movements Infilling of Public Transport Matrices 15