1. INTRODUCTION AND BACKGROUND

Size: px

Start display at page:

Download "1. INTRODUCTION AND BACKGROUND"

Cecilia Shepherd
5 years ago
Views:

2 1. INTRODUCTION AND BACKGROUND Transportation planning has focused on moving people efficiently and safely. Freight demand modeling and assignment have received limited attention with little or no integration into major regional planning efforts. Although the heavy vehicle component of highway flows is relatively small ( generally less than 15% ), it is predominant in affecting both the condition of roads and traffic flow. An important limitation for major freight planning efforts has been the scarcity of comprehensive data sources. The complexity of freight transportation comes in the large number of private shippers, arrangers, transporters, and recipients. Most often, no single party knows all the players and modes that were involved in moving the shipment from a shipper to its ultimate destination. Compiling a database for all movements is an enormous undertaking and hence, there has not been sufficient research in commodity movement prediction. The Commodity Flow Survey (CFS), a major partnership effort between the United States Department of Transportation and the Bureau of the Census, is the first comprehensive effort since the mid seventies, to learn where and how goods are shipped (1). This database contains commodity flow within and between states classified in terms of the type of freight and mode of shipment. Today, many analyses such as vehicle emission studies or pavement deterioration assessment, require a high degree of accuracy for vehicle volumes on links. Sufficient research on predicting the number of passenger cars has been done. A procedure for doing the same with trucks will be necessary and the CFS for 1993 is a good starting point. Geographic information systems (GIS) can be described as a decision support system involving the integration of spatially referenced data in any problem solving environment. GIS are useful analysis and presentation tools in the field of transportation planning, engineering and management. Use of GIS technologies in freight transport planning will enhance conformity with recent efforts and methodologies in other areas of transportation planning and provide the flexibility for understanding the spatial effects of commodity flow. The primary objective of this research is to develop a GIS-based approach for distributing and assigning freight flows in Massachusetts. An intermediate goal is to develop a quantitative methodology for estimating freight traffic on major roads in Massachusetts from newly released inter-state commodity flow data. The following section discusses the implementation of GIS using TransCAD (2) in developing a procedure for assigning freight movements on the major highway corridors in Massachusetts. 2. METHODOLOGY The basic framework for this analysis consisted of dividing Massachusetts into smaller regions and apportioning the freight flow from the neighboring states to these regions using a socio-economic indicator variable. The statewide freight flow data was extracted from the CFS for 1993 and corresponds to tons (in '000s) of commodity shipped by truck between the New England States, New York (NY), New Jersey (NJ) and the rest of the United States as shown in table 1. During analysis, flows corresponding to the rest of the United States into and from Massachusetts (MA) were combined with that of NY. This was because NY separates MA from the rest of United States and flows going into and out of MA have to pass through it. A quantitative procedure was used to convert weights into truck numbers. An origindestination (O-D) matrix for the number of daily trucks between the internal origins ( centroids of the regions created ) and exit points from Massachusetts was constructed. This O-D matrix was then assigned over the major highways in the state and the resulting link volumes were validated against an extrapolated Highway Performance Monitoring Services (HPMS) survey 1

3 count (3). The different assignments considered were All or Nothing, Capacity Restraint and User Equilibrium. In All-or-Nothing assignment, all traffic flow between an O-D pair is assigned to the shortest path connecting the pair. This model is unrealistic in that only one path between every O-D pair is utilized even if there is another path with almost similar travel costs. Also, traffic is assigned without consideration for whether or not there is adequate capacity. Ignorance of highway capacity is advantageous when freight data in tons (000's) needs to be assigned over the network directly. The O-D matrix representing tons of freight flow, when assigned over the network, will result in the weight of commodity on the various links. The Capacity Restraint method attempts to approximate an equilibrium solution by iterating between All-or-Nothing traffic loadings and recalculating link travel times based on the congestion function shown in equation 1 (4). where, v t = t f 1+ α c t = congested link travel time. t f = link free-flow travel time. v = link volume. c = link capacity. α,β = parameters (α = 0.15, β = 4.00). β (1) The Capacity Restraint assignment method does not converge for all links and has the additional problem that results are highly dependent on the number of iterations. User Equilibrium utilizes an iterative process to achieve a convergent solution in which no traveler can improve his/her travel time by shifting routes. TransCAD formulates the User Equilibrium problem as a mathematical program using the Frank-Wolf solution method (5). In each iteration, network link flows are computed, which incorporate link capacity restraint effects and flow dependent travel times using equation 1. A pictorial representation of the analysis procedure is shown in figure 1. A more complete discussion of these steps is presented in the following sections Constructing the highway network. The highway network was extracted from the National Transportation Atlas Databases (NTAD) and consisted of the National Highway Planning Network State, US, and Interstate highways in Massachusetts. The spatial network is shown in figure 2. Because different types of traffic assignments were to be evaluated on this network, attributes such as travel time and capacity were calculated from existing information about the various links. The All-or-Nothing assignment procedure requires only the travel time attribute on the various links, whereas the other two methods require the capacity of the links as well. Travel time was calculated as the estimated time of travel at the speed limit over a highway link. Capacity of the link was calculated from the number of lanes, lane width, grade, and mix of vehicles. The method adopted for this calculation was derived from the 1985 Highway Capacity Manual (6). 2

4 2.2. Division of Massachusetts into smaller districts. The freight data from the CFS for 1993 is aggregated to the state level. Because the intention was to estimate the flows on different links within the state, Massachusetts was divided into smaller regions created by aggregating 5 digit zip-code regions. Using 3 digit zip-code regions proved too large for the analysis and 5 digit zip code regions considered individually would have made it very data intensive. Hence, using 3 digit zip-code boundaries as a guide, 5 digit zip-code regions were combined to create appropriately sized areas. The geographic centroids of these regions were joined to the network with centroidal connectors which allow for flow in a single direction only. The regions considered for analysis and their centroids are shown in figure Commodity aggregation Owing to the heterogeneity of freight, the initial intention was to divide commodity into different categories based on Standard Industrial Classifications (SIC), such as farming, forestry and fisheries, mining, and all other sectors combined into one, and to perform the analyses on each. For such analyses, it would be necessary to apportion flows (as described in the next section), for each category which would involve calculating independent distribution ratios for them. Though these ratios were determinable, the origin-destination matrices for each SIC commodity category was not completely extractable from the CFS for A majority of commodity flow data for the farming, fisheries and forestry, and mining categories were either withheld to avoid disclosure or unavailable due to not meeting publication standards caused by high sampling variability. The other sectors category consisted of upto 95% of all the commodities data and the individual analyses conducted showed that they dominated the results. Hence, all commodity categories were combined and a single analysis procedure was adopted Apportioning the flows between the districts. The statewide flow has to be partitioned to the smaller districts created. Previous research has shown that economic indicator variables such as employment, employment density, and floor space, are good measures for assessing the amount of commodity entering or leaving a given area (7,8 and 9). As an initial indicator total employment was used as shown in equation 2. d i = ei 25 (2) e i= 1 where, d i = Distribution ratio for district i e i = Total Employment (all sectors) for district i As mentioned in section 2.3, individual employment ratios corresponding to different SIC groupings were not necessary for the analysis Construction of Origin-Destination matrix. The centroids of the districts constitute the internal origins and destinations for the matrix. External origins and destinations correspond to intersections of highways with the state border. Because many highways connect any two states, it was necessary to apportion the flow on the various highways based on their propensity and level of service characteristics. As i 3

5 distance to a given entry/exit point increased, probability of using that highway decreased, while interstates were given a higher priority than state and US highways. O-D matrices representing the freight movement in tons between centroids and exit points and vice-versa can be assigned to the highway network. Such assignments were conducted but no source existed for validating the results obtained. Hence, it was imperative that freight tons be converted to truck loads as number of trucks. An O-D matrix with truck volumes, when assigned over the network, will result in the truck flows on the various links. These results can be checked against existing survey counts. Formulae for converting commodity weight into number of annual trucks are given in equations 3 and 4. where, N = ρ N = W i= 13 (3) pv avg i i i= 3 i 4 i= 13 i= 3 i W pw( 1 p ) i i ei N = Total number of all types of trucks for a given commodity weight W. W = Weight of commodity shipped annually between any two O-D pairs (kg). ρ avg = Average density of freight shipped = kg/m 3 (12.5 lb/cu.ft). p i = Average percentage of truck type i (see figure 4). v i = Average volume of truck type i (m 3 ). w i = Average weight of non-empty trucks of type i (kg). p ei = Average percentage of empty vehicles of type i. (4) The theoretical basis for equations 3 and 4 is given in (10) 1) Weight translates into volume for a given density. 2) Empty trucks will bring down average density of goods shipped (ρ avg = kg/m 3 ). 3) Average weight of trucks range from 25% to 35% of the commodity weight they carry (hence total weight of truck in equation 4 = 1.3*W). 4) Trucks of type 4 (i = 4) are busses and are not considered here. This conversion incorporates the effects of various truck sizes and dead haul ( trucks returning empty after delivery ). Using a low density value in equation 3, a deadhead (dead haul) component gets automatically added to each direction of movement into and from the state. Further, the density value corresponds to that of commercial traffic flow as opposed to just freight flow. Hence, this conversion results in the commercial flows for a given commodity weight. Freight density has an inverse relationship with respect to truck number and small changes in it can produce large changes in the latter. Owing to such high sensitivity, this should be calculated to precision. Data from work at the University of Massachusetts (11) were used to estimate the various variables in equation 3 which was used for conversion. Tonnage of freight between various origin-destination pairs were substituted for 'W ' in equation 3 and annual 4

6 number of trucks moving between these pairs was determined. This number was divided by 260, the average number of annual working days, to estimate the daily truck volume between O-D pairs. The average number of annual working days was used as the resulting truck flows were to be compared against peak percentage of commercial traffic. This final O-D matrix was comprised of four basic divisions shown in figure Assigning the freight O-D matrix. Different assignment techniques including All or Nothing, Capacity Restraint, and User Equilibrium were evaluated. Results from these assignments were compared between themselves and also with actual observed data (HPMS survey). All or Nothing assignment showed the counter intuitive result of interstate highways having lower truck counts as compared to the neighboring State and US highways. Capacity Restraint method of assignment has the problems of divergence and a high dependence on the number of iterations. User Equilibrium assignment was chosen over the others as it showed both valid and intuitive results (see figure 6). The results of the above mentioned comparisons are not presented here and it should be noted that the authors were interested in using an assignment method that was both intuitive and produced reasonable results. All three methods without modifications only give approximate assignment results. This is because of the presence of a cyclic relationship between the number of trucks and the classification of the highway. The number of trucks for a given commodity weight is a function of the percentage mix of the type of trucks (p i - see equation 3) which depends on the type of highway. But, the highway characteristics do not come until assignment at which point the number of trucks on them needs to be calculated before hand. Hence, an iterative procedure of assignment using principles of the methods mentioned above needs to be used which forms a part of the future research effort Validation of analysis. The Highway Performance Monitoring Services (HPMS) for Massachusetts contains percent commercial vehicles data for 1990, 1991, and This survey and the Average Daily Traffic (ADT) values for the different highway links were used to calculate the average daily number of trucks. These values were extrapolated across highway segments as shown in figure 7. This was used to assess the validity of the assignment. The difference between the estimated flows and the survey data for the User Equilibrium type of assignment is shown in figure RESULTS AND COMPARISON The validation results show that 81% of links fall into the tolerable (±15%) category. Nine percent of the links show an overestimation and 10% show that the HPMS survey data is higher than what was calculated. A majority of the former links are on I90 (Mass Turnpike) where HPMS data was not available and an average of 10% commercial traffic was assumed. Highways closer to Boston (I495, I95, and I93) showed an underestimation as compared to the HPMS data. This is understandable as commercial traffic in an urban locality is dependent on many other factors including local trips, trip chaining and higher number of smaller trucks. A more localized study is necessary to understand the major traffic generators and attractors. Generally, a high degree of correspondence between observed and calculated flows shows that this line of research is promising. 5

7 4. FUTURE WORK. The aforementioned methodology was the basis for studying freight movement in Massachusetts. The following are some of the research objectives for the future This methodology will be applied to commodity flow data corresponding to National Analysis Transportation Region (NTAR) to NTAR flow. Massachusetts is divided vertically into two approximate halves by NTARs. Commodity flow between states is at a higher macroscopic level as compared to flow between NTARs and higher accuracy in analyses may be possible using the latter. This approach is to be used at the regional level where much more accurate employment data will be available and issues such as close proximity for freight movement can be investigated. The weight to truck number conversion formulae have to be refined and recalibrated with current data sources. The average percentage of different type of trucks (p i - see equation 3) is dependent of the type of highway - Interstate, State or US Highway. With more accurate data, these variables in the equation can be calculated more precisely. The annual commodity flow has been converted to daily truck counts by using a factor of 260 (the number of working days) which needs to be refined. The analyses should be conducted with 365 and 312 days and the results compared. Separate truck flow assignments near urban areas are necessary due to the effects of local trips. Total employment as an indicator variable has not been sufficient in explaining truck movement near urban areas. Introduction of relevant socio-economic variables has to be researched. The railway network in Massachusetts will be added to this highway network and intermodal transfer yards will be modeled as pseudo links connecting these networks. With an understanding of the spatial distribution of freight flows, suggestions for improving efficiency and reducing total shipment time can be researched. 6

8 ACKNOWLEDGMENTS This research was partially funded by the Massachusetts Port Authority. The authors would like to thank Russell Capelle of the Central Transportation Planning Staff in Boston for providing valuable insights and guidance during the course of the research. REFERENCES 1. U.S. Census Bureau; " 1993 Commodity Flow Survey; " issued December 1996, U.S. Department of Commerce, Washington, D.C. 2. TransCAD - Transportation GIS Software, Users Guide. Caliper Corporation, Newton, Massachusetts, Highway Performance Monitoring Services; " Percent Commercial Vehicles data;" issued August 1993, Bureau of Transportation Statistics, U.S. Department of Transportation, Washington, D.C. 4. Traffic Assignment Manual, Bureau of Public Roads, Urban Planning Division, U.S. Department of Commerce, Washington, D.C., Travel Demand Modeling with TransCAD 3.0. Caliper Corporation, Newton, Massachusetts, 1996, pp Highway Capacity Manual, Special Report 209, Transportation Research Board, Washington, D.C., Starkie, D. M., Commercial Vehicles in Urban Transportation, Journal of Institute of Highway Engineering, September Biddle, B., V.J. Siaurusaitis, Truck Transportation Planning, Working Paper, COMSIS, Silver Spring, Maryland, May Memmot, F. W., Applications of Statewide Freight Demand Forecasting Techniques, NCHRP 260, TRB, Washington DC, September National Transportation, Trends & Choices ( To the year 2000 ), U.S. Department of Transportation, January 1977, pp New England Vehicle Classification and Truck Weight Program, Technical Report No.2, New England Transportation Consortium, University of Massachusetts at Amherst, November

9 TABLE 1 : Commodity Flow by truck in New England, New York, New Jersey, and the rest of the US. ( 000's tons ) Destinations Origins CT ME MA NH RI VT NJ NY Rest of US CT --- b ME MA NH RI VT NJ NY Rest of US a Source : Commodity Flow Survey b Data not required for this analysis. Hence not extracted from the source.

10 CFS Census NTAD Origin-Destination data from State to State by truck and commodity type Highway and State geographic files for Massachusetts Economic Indicator(s) Employment Employment density Floor space Division of Massachusetts into smaller regions Joining their centroids to the highway network Origin-Destination matrix for internal origins (centroids) and border exit/entry points Identification of major entry/exit points on the state border Conversion to number of trucks Final Origin-Destination matrix - Number of daily trucks. Traffic Assignment - All-or-Nothing - Capacity Restraint - User Equilibrium. HPMS Commercial vehicle survey data Extrapolate over highway links Validation 81 % - tolerable range of ±15% 9 % - over predicted. 10% - under predicted. Future Work. Figure 1. Analysis procedure

BTS State and Freight Data Products. Freight in the Southeast Biloxi, MS March 19, 2015

BTS State and Freight Data Products Freight in the Southeast Biloxi, MS March 19, 2015 Major BTS Freight Data Programs Commodity Flow Survey (CFS) Freight Analysis Framework (FAF) Cooperative effort with