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73 Source: Freight Analysis Framework Highway Capacity Analysis Methodology Report, April 2002, Figure 2. Figure 8.6. Freight Analysis Framework highway network assignment. flows are not available for public release). States also can iden- nus. In 2001 its gross state product was approximately $365 tify key flows to major trading partners. Metropolitan and billion. The 1997 CFS showed $286 billion of goods ship- rural areas also may use the commodity flows for county or ments originating in New Jersey, representing 224 million local planning purposes. tons. The 1997 CFS also indicated that 73% of those ship- ments by value and 85% by weight were moved by truck. New Jersey is noted for its output of chemicals, pharma- Performance Measures ceuticals, machinery, and a host of other products, including Transportation system performance measures available electronic equipment, printed materials, and processed from the FAF are limited primarily to truck vehicle-miles of foods. Bayonne is the terminus of pipelines originating in travel by highway level of service. Truck travel times can be Texas and Oklahoma, and there are oil refineries at Linden imputed based on relationships between volume, capacity, and Carteret. Today, telecommunications and biotechnology and speed. FAF outputs can support estimation of a variety of are major industries in the state, and the area near Princeton other performance measures. has developed into a notable high-tech center. Finance, ware- housing, and "big box" retailing also have become important 8.6 Case Study New Jersey to the state's economy, attracting corporations and shoppers Statewide Model Truck Trip and to a large extent reversing New Jersey's onetime role as a Table Update Project suburb for commuters to New York City and Philadelphia. An extensive transportation system, concentrated in the Background industrial lowlands, moves products and a huge volume of in- terstate traffic through the state. Busy highways like the Garden Context State Parkway and the New Jersey Turnpike are part of a net- Geographically, New Jersey is among the smallest states in work of toll roads and freeways. New Jersey is linked to the union, yet it ranks ninth in terms of total population and Delaware and Pennsylvania by many bridges across the first in terms of population density. New Jersey's density is Delaware River. Traffic to and from New York is served by rail- even greater than that of the Netherlands, the most densely way and subway tunnels and by the facilities of the Port populated country in Europe. New Jersey is a major industrial Authority of New York and New Jersey. These include the center and an important transportation corridor and termi- George Washington Bridge, the Lincoln and Holland vehicular
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74 tunnels, and three bridges to Staten Island. Airports are oper- Framework ated by many cities, and Newark Airport (controlled by the Port The original truck trip table for the New Jersey Statewide Authority) ranks among the nation's busiest. Shipping in New Model was estimated through the use of commodity data Jersey centers on the ports of Newark Bay and New York Bay provided by DRI-McGraw Hill. Truck trips were estimated by areas, notably Port Newark and Port Elizabeth, with relatively converting the tonnage data into truck trips using custom minor seagoing traffic on the Delaware River as far north as algorithms provided by Gellman Research. These trips were Trenton. estimated at the county level and then disaggregated to the zonal level using employment data. New Jersey previously Objective and Purpose of the Model had a commodity flow-based model and the truck trip table was developed outside the modeling process and imported As part of a study titled Effects of Interstate Completion and into the model system. Other Major Improvements on Regional Trip Making and The revised New Jersey Truck Model was developed at the Goods Movement undertaken by the New Jersey Department zonal level using traditional modeling techniques. It was of Transportation (NJDOT), a truck trip table was developed assumed these techniques would provide a reasonable esti- to study truck trips as one component of the statewide trans- mate of short distance, delivery-type trips not within the portation model. A major impact on regional truck trips was commodity-based trip table. The zonal-level trips were esti- expected after the completion of I-287 in northern New mated as a function of employment by type, the number of Jersey and the completion of the remaining section of I-295 households, and area type. The distribution of these trips was in the Greater Trenton Area. The revised New Jersey Truck performed with standard gravity model techniques. Model is an update of the previously existing truck trip model.20 Flow Units General Approach As a truck model, the flow units are average weekday truck trips and volumes. Model Class As a truck model, the New Jersey Truck Model develops Data highway freight truck flows by assigning an O-D table of Forecasting Data freight truck flows to a highway network. The O-D table is produced by applying truck trip generation and distribution BASE AND FORECAST YEAR SOCIOECONOMIC DATA steps to existing and forecast employment or other variables For trip generation, the observed data was obtained from of economic activity for analysis zones. A detailed description a number of sources. At highway-based external zones, exter- of the Truck Model, including its components is included in nal trips were generated using observed data and 24-hour Section 6.3. count data provided by several agencies, including the NJDOT, the New York Department of Transportation, the Modes Delaware Department of Transportation, and the Delaware Valley Regional Planning Commission. The observed data for By definition, truck models like New Jersey's deal with intermodal terminals were more difficult to obtain. Since freight served only by the truck mode. most of the needed information was proprietary in nature, the available data were fairly aggregate. The observed data for Markets all rail intermodal terminals in the New York metropolitan area were estimated by site using information provided by the Analysis of the trip table and the assignment results from the New York/New Jersey Port Authority. In addition, 1990 U.S. previous truck model indicated that key market segments crit- Census Bureau data was used to obtain sociodemographic ical to painting a comprehensive picture of truck travel in New information. This information was supplemented with dis- Jersey were missing. Primary commodity flows were included cussions with Port Authority staff and then allocated to the in the data, but not the subsequent truck trips used to distrib- individual rail intermodal terminals. ute the commodities to the individual users and retail outlets. Excluded were distribution-related truck traffic as well as other EXTERNAL MARKETS flows, such as express air delivery services and municipal water. The revised New Jersey Truck Model was developed to include For rail and marine intermodal terminals near Philadel- all these important components of truck traffic. phia, data was obtained from the Pennsylvania Intermodal
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75 Management System Phase II report provided by Delaware Commodity Groups/Truck Types Valley Regional Planning Commission. In several cases, com- No commodity groups were used. Trucks were split into modity tonnages were converted to equivalent truck trips by two categories based on weight, medium and heavy. Medium Gellman Research Associates. Truck trips from South Jersey trucks were defined as all two-axle, six-tire trucks with weights port facilities near Philadelphia were obtained through dis- generally between 8,000 and 28,000 pounds. Heavy trucks cussions with Delaware River Port Authority staff and local were defined as all trucks with three or more axles and weights operators. For Kennedy International Airport in New York, greater than 28,000 pounds. crude estimates of truck trips and overall commodity ton- nages were available. Trip Generation Modal Networks The revised trip generation process divided external truck FREIGHT MODAL NETWORKS trips into three categories in order to provide a flexible method for resolving inconsistencies between aggregate com- The existing New Jersey Statewide Model's highway network modity flows and survey data. External trips were designated was used for the revised truck model without modification. as either external-external (E-E) through-trips or external- internal (E-I) trips with at least one stop inside the statewide INTERMODAL TERMINAL DATA model region. External-internal trips were then further strat- ified into singular E-I trips or external trips that stopped at a For rail and marine intermodal terminals near Philadel- truck terminal and then continued their trip, eventually phia, data was obtained from the Pennsylvania Intermodal leaving the region. These trips were referred to as external- Management System Phase II report. In several cases, com- internal-external (E-I-E) trips. modity tonnages were converted to equivalent truck trips. The revised New Jersey Truck Model also focused on Truck trips from South Jersey port facilities near Philadelphia major truck trip generators that would be poorly represented were obtained through discussions with Delaware River Port by employment-based trip generation equations. These spe- Authority staff and local operators. For Kennedy Interna- cial generators were categorized into two groups. The first tional Airport, crude estimates of truck trips and overall group covered all large generators that carried commodity commodity tonnages were available. flows (in the form of containers or trailers) out of the region. Large generators were generally intermodal facilities (rail Model Development Data intermodal yards, ports, and airports) and were designated as "external zones" or entry points into the region. The trip generation and distribution rates and coefficients The second category of special generators was geared to in- were developed using survey data ternal sites that would service primarily local truck trips. This category was initially designed to include sites such as land- Conversion Data fills, pipeline terminals, petroleum refineries, truck terminals, and warehouses. The final model restricted this category to Because truck models that forecast daily truck trips require truck terminals, warehouses, and pipelines. no conversion factors, no data was necessary. Under the revised approach, truck trips generated at the external boundary of the five region statewide model would Validation Data be estimated with data provided by the individual state departments of transportation and selected agencies. The re- See the section on model validation. vised approach also utilized the available survey data to the maximum extent possible. For many external zones at the Model Development major interstate routes, cordon surveys were available to estimate trucks by vehicle type as well as type of movement Software (through, internal-external, external-internal). At other loca- The revised truck model was developed using TRANPLAN tions, only daily traffic estimates were available to control software and custom FORTRAN scripts. In addition, travel into the region. spreadsheets also were used for the model development. The trip generation process estimated truck trips generated These modules will be discussed more fully in the individ- within the five region study area as well as in the adjacent re- ual sections on the model components in the revised truck gions. Internally, trip generation was performed at the zonal model. level using employment, households, and truck terminals as
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76 the independent variables. For trips generated outside the not provide any information on the actual route and/or region, a series of external zones was developed that repre- intermediate transfer points. sented entry points into the region. These entry points Since it was not possible to estimate the E-I-E trips directly, included both stations at major highways at the border of the these trips were estimated by assuming that 25% of the E-E region, as well as intermodal terminals within the region. trips on interstate facilities were E-I-E trips. This process was The revised trip generation process was structured to esti- limited to external zones representing interstate highways mate truck trips primarily as a function of employment. since it was assumed that long-distance truck travel would Special generators, in the form of truck terminals, ware- most likely approach the region using these routes. In addi- houses, and pipeline terminals, were utilized for conditions tion, it was anticipated that major trucking firms would where the typical employment relationships would poorly locate their major terminals near these facilities, which would estimate truck trips. In addition, the truck terminals served as increase the likelihood that these trips would use the inter- attractors for a portion of the long-haul truck trips entering state routes. the study area from the adjacent regions. Truck trips were After removing the E-E and E-I-E truck trips from the ex- generated separately for medium and heavy trucks. ternal truck counts, the remaining truck trips were designated Total external trip travel was divided into three categories as highway-based E-I trips. These trips then were divided into in order to provide a flexible method for resolving inconsis- both the medium and heavy truck categories based on survey tencies between aggregate commodity flows and survey data. data. E-I trips also were generated at the intermodal facilities, External trips were designated as either E-E (through trips) or since the airports, rail yards, and ports were designated as E-I. E-I trips were further stratified into singular E-I trips or external entry points into the modeled region. The majority external trips that stopped at a truck terminal and then con- of all rail intermodal truck trips was assumed to be E-I, as tinued on, eventually leaving the region. These trips were were most of the air intermodal movements generated by the called E-I-E trips. regional airports. Using the available survey data, a significant Wherever possible, truck trip surveys were used to allocate portion of all the port and airport intermodal traffic also was truck trips to the E-E market segment. The revised forecast- designated as E-I trips. ing process was developed to utilize the survey data in an The calibration process yielded the following equation: efficient and flexible manner. The process was structured to have two layers of E-E travel patterns. These patterns form EITRKPi = 0.003192 * (EITRKj /TIMEij **2.0) - 0.00998 the basis of simulating E-E truck trips across the region. The where first layer, referred to as primary E-E patterns, included E-E EITRKPi = Percentage of truck trip ends at internal zone i movements obtained from all survey-related information. that are E-I, The second layer, called secondary E-E patterns, provided EITRKj = Volume of E-I truck trips at external station j, movements based on the analyst's professional judgment. and The truck trip generation program processed both sets of TIMEij = Travel time from internal zone i to external sta- these patterns, allowing the primary patterns to govern sec- tion j. ondary patterns in the case of duplicate movements. Total E-I trips were calculated by subtracting the estimated The regression results provided a statistically significant E-E trips from the total truck volumes at each external zone. model with an R-squared value of 0.43. Due to this low value, This calculation was performed for each truck type. As part the coefficients from the Delaware Valley Regional Planning of the revised truck trip generation process, a procedure was Commission (DVRPC) regression were adopted for use in developed to estimate a portion of the E-I trips that went to this model. an intermediate transfer point, such as a truck terminal of a The attraction equation was stratified by truck type. This major trucking company. At this location, cargo would be was performed since it is assumed that there should be some transferred between vehicles for subsequent shipment. After variation in the E-I attraction percentages for each zone by leaving the truck terminal, these trips were assumed to con- truck type. The final attraction equation is: tinue traveling to an external zone in order to reach a final EITRKPim = 0.003192 * (EITRKj/TIMEij ** EXPm) - 0.00998 destination outside the region. These trips are the E-I-E trips. The E-I-E trips were created to account for a perceived where inconsistency between survey data and commodity data. The EITRKPim = Percentage of trip ends for truck mode m at survey data accounts for the final destination of the truck trip, internal zone i that are E-I, but not the ultimate destination of the commodity being EITRKj = Volume of E-I truck trips at external station j, shipped. In contrast, the commodity data has the true origin TIMEij = Travel time from internal zone i to external and destination of the commodity being shipped, but does station j, and
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77 EXPm = Exponential term for truck type m (heavy = 2, This data for each of the five regions was prepared for the medium = 2.1). 1990 base year using several data sources. Within New Jersey, Pennsylvania, and Delaware, demographic data was provided The revised truck trip generation process requires employ- from the existing metropolitan planning organization mod- ment data by type and household data for each of the inter- els. For New York, this data was obtained from the 1990 U.S. nal study area zones. The employment types used for the New Census Bureau Census Transportation Planning Package Jersey Truck Model are shown below, where SIC refers to the data. Table 8.20 shows the internal truck trip generation rates. Standard Industrial Classification: The final element of internal truck trip generation is spe- cial generator sites. The revised trip generation approach · Retail (SIC Codes 52-59); provided a mechanism to independently simulate major · Industrial (SIC Codes 20-39); truck trip generators that would be poorly represented by · Public (SIC Codes 91-98); employment-based trip generation equations. For internal · Office (SIC Codes 60-89); and trips, special generators related primarily to local truck trips · Other (SIC Codes 1-19, 40-51). were coded in several ways. First, a special generator could Table 8.20. Internal truck trip rates (New Jersey Department of Transportation Statewide Model). Model Final New Phoenix Washington, San Francisco Jersey Truck Variable (1991)a D.C. Vancouverb (1993)c Model Equations and Coefficients (Heavy Trucks) Retail Employment 0.0615 0.0300 0.0001 0.0590 Industrial Employment 0.0833 0.0300 0.0665 0.0293 0.0800 Public Employment 0.0400 0.0200 0.0220 0.0384 Office Employment 0.0053 0.0200 0.1640 0.0220 0.1207 Total Employment 0.0112 Households 0.0210 0.0202 a Trucks over 28,000 pounds attraction rates only. b Trucks over 44,000 pounds. c Assumed three- and four-axle truck rates are "heavy truck" production rates only. Model Final New Phoenix Washington, San Francisco Jersey Truck Variable (1991)a D.C. Vancouverb (1993)c Model Equations and Coefficients (Medium Trucks) Retail Employment 0.2213 0.1700 0.0212 0.0140 0.1264 Industrial Employment 0.1665 0.1400 0.0212 0.0110 0.0522 Public Employment 0.0100 0.0400 0.0212 0.0460 0.0032 Office Employment 0.0354 0.0100 0.0212 0.0105 0.0202 Total Employment 0.0324 Households 0.1145 0.0400 0.0041 0.0240 Source: URS Greiner Woodward Clyde, "Statewide Model Truck Trip Table Update Project," prepared for the New Jersey Department of Transportation, January 1999. a Trucks between 8,000 and 28,000 pounds attraction rates only. b Trucks between 9,000 and 44,000 pounds. c Assumed two-axles are "medium trucks" production rates only.
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78 be designated as one of several special categories for which The Port Newark/Elizabeth Port complex is an extremely default trip generation rates were available. Currently, only large generator of truck trips. Information provided by the truck terminals and pipeline terminals are available as Port Authority indicates that approximately 17,000 trucks default special generators. In addition to these categories, a enter or exit the site on a daily basis. For this reason, the dis- generic special generator field is provided for each zone in tribution calibration also focused on replicating the travel order to code zone-specific generators that have truly unique patterns generated by the port traffic. Table 8.22 shows the characteristics. estimated and observed distribution of truck trips related to Port Newark/Port Elizabeth. Trip Distribution Commodity Trip Table For the revised New Jersey Truck Model, truck trip distribu- tion was performed with standard gravity model techniques, A commodity trip table was not used. using highway travel time to represent the spatial separation between zones. Mode Split Internal trip distribution was performed using a synthetic data set derived from the 1991 Phoenix Truck Model Update Because the model only addresses freight carried by trucks Project. This data was as an observed distribution, adjusted as and the forecasting unit is daily truck trips, not annual tons, necessary to establish a reasonable target for the calibration this step is not needed. process for both medium and heavy truck trips. Trip distri- bution for E-I and E-I-E trips was based on truck cordon Flow Unit and Time Period Conversion surveys conducted by the Port of New York/New Jersey and NJDOT. The survey-based distribution patterns were modi- Because the model class only addresses freight carried by fied to yield average travel times approximately 30% less than trucks and the forecasting unit is daily truck trips, not annual the observed times. tons, this step is not needed. Intermodal E-I trip distribution was performed as a sepa- rate process. This was necessary since observed patterns, in Assignment terms of average travel times, were significantly different from E-I highway-based observed data. The E-I intermodal The highway assignment of the daily truck table was an distribution was based on an attractiveness measure devel- equilibrium multiclass process that loaded the daily auto and oped using truck terminals, warehouses, and industrial truck trips by type to the highway network. Prior to the actual employment. An average observed travel time of 37.2 assignment, the network links were posted with the free flow minutes was used for all intermodal trips, including those speed and capacities necessary for the TRANPLAN equilib- generated by the intermodal rail yards and airports, since dis- rium routine. For all toll links, capacities were set to zero. For tribution data for these facilities was not available. Table 8.21 all time penalty links such as left turn movements, the time shows observed truck trip distribution. values were hard-coded into the assignment control and the Table 8.21. Truck distribution average time in minutes. Truck Trip Type Internal-Internal External-Internal Total Study Medium Heavy Medium Heavy Medium Heavy San Francisco (Alameda County) 16-24 22-31 54 59 Phoenix (Maricopa County) 12 19 Vancouver 12 18 New Jersey Cordon Surveys 44 52 77 84 New Jersey Observed Values 14.6 26.3 60.3 74.4 Current Estimates 18.2 32.9 51.7 76.7 Source: URS Greiner Woodward Clyde, "Statewide Model Truck Trip Table Update Project," prepared for the New Jersey Department of Transportation, January 1999.
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79 Table 8.22. External and internal trip origins for Port Newark/Port Elizabeth. Heavy Truck Medium Truck Total Truck Origin of Trip Volume Percent Volume Percent Volume Percent Observed Bergen 364 5.22% 125 7.63% 489 5.68% 3.99% Essex 652 9.36% 256 15.63% 908 10.55% 14.49% Hudson 1,060 15.21% 413 25.21% 1,473 17.12% 19.20% Hunterdon 47 0.67% 20 1.22% 67 0.78% 0.00% Middlesex 674 9.67% 288 17.58% 962 11.18% 4.35% Monmouth 38 0.55% 13 0.79% 51 0.59% 0.36% Morris 62 0.89% 19 1.16% 81 0.94% 1.09% Ocean 13 0.19% 5 0.31% 18 0.21% 0.00% Passaic 328 4.71% 138 8.42% 466 5.41% 0.36% Somerset 103 1.48% 43 2.63% 146 1.70% 0.00% Sussex 0 0.00% 0 0.00% 0 0.00% 0.00% Union 279 4.00% 109 6.65% 388 4.51% 7.61% Warren 3 0.04% 0 0.00% 3 0.03% 0.36% New York City Remainder 114 1.64% 72 4.40% 186 2.16% 5.80% Orange 3 0.04% 0 0.00% 3 0.03% 0.72% Atlantic 0 0.00% 1 0.06% 1 0.01% 0.36% Cape May 0 0.00% 0 0.00% 0 0.00% 0.36% Cumberland 1 0.01% 0 0.00% 1 0.01% Salem 0 0.00% 0 0.00% 0 0.00% 0.36% Gloucester 3 0.04% 2 0.12% 5 0.06% Camden 20 0.29% 7 0.43% 27 0.31% 0.36% Burlington 13 0.19% 5 0.31% 18 0.21% 0.36% Mercer 41 0.59% 16 0.98% 57 0.66% 1.09% Others 3,150 45.21% 106 6.47% 3,256 37.83% 36.77% Total 6,968 100.00% 1,638 100.00% 8,606 100.00% 100.00% Source: URS Greiner Woodward Clyde, "Statewide Model Truck Trip Table Update Project," prepared for the New Jersey Department of Transportation, January 1999. capacity was set to zero. With this approach, the time penalty done to limit total medium VMT. The total truck trip is ap- was held constant for each iteration of the assignment. The proximately 3.9% of total trip generation in the region. time penalties were used only for medium or heavy truck trips. The assignment simultaneously loaded the auto trips, medium truck trips and heavy truck trips. The loading of each Trip Distribution of these trip types was restricted to links permitted to carry The trip distribution validation required several adjust- these vehicle types. Toll links for each vehicle type also were ments to the modeling process. In order to provide reason- coded in the network for all toll facilities in New Jersey. able travel times, it was necessary to adjust the highway travel skim estimates. This adjustment was performed by reducing Model Validation the speed for non-freeway facilities in the central business dis- trict and urban area types. For the suburban and rural area Trip Generation types, speeds were reduced 10% on expressway facilities and Using the Phoenix values and definitions as a starting 25% on all other facilities. point, truck trips were estimated and summed together with A penalty of 10 minutes was assessed for all skims that uti- the truck terminal special generators. As part of the overall lized the trans-Hudson bridges between New Jersey and New validation, it became necessary to substantially reduce the York. These penalties are considered as surrogates for both the trip generation rates for medium trucks. This was primarily impacts of tolls and excessive congestion at these facilities. A
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80 set of corrective K-factors was added to the E-I highway-based Modal Assignment truck trips. These K-factors were applied specifically for The validation of the revised model approach focused pri- external stations on the western side of Philadelphia not marily on aggregate VMT statistics by facility type and area included in the model and approaching New Jersey via I-78. type. The validation provided separate summaries of trips by K-factors also were applied to the reverse movement to reduce vehicle type, including medium, heavy, and total trucks, as similar trips moving in the other direction. The K-factors were well as total vehicles. Site-specific validation analysis was per- included directly in the trip distribution controls. formed for key interstate facilities and major river crossings. This validation analysis indicates that the model is replicating observed statistics reasonably well at the aggregate level. Mode Choice Overall, the regionwide estimated VMT for the truck high- Not applicable for this class of model since no mode split way assignment was 3.9% greater than the observed VMT. As component is included. shown in Table 8.23, comparisons by area and facility type Table 8.23. Model estimates of truck VMT by area and facility type. Central Business District Urban Suburban Rural 1 2 3 4 Heavy Truck Percentages Freeway 1 8.5 11.0 12.0 10.5 Expressway 2 7.5 8.0 11.5 8.0 Principal Divided 3 6.0 10.0 6.0 7.5 Principal Undivided 4 5.8 6.0 5.5 6.0 Major Divided 5 4.7 7.0 5.0 6.0 Major Undivided 6 4.6 7.0 4.0 5.0 Minor 7 4.5 8.0 5.0 4.0 Collector-Local 8 4.5 8.0 5.0 4.0 Medium Truck Percentages Freeway 1 1.1 1.4 1.6 1.4 Expressway 2 1.0 1.0 1.5 1.0 Principal Divided 3 1.6 2.6 1.6 2.0 Principal Undivided 4 1.5 1.6 1.4 1.6 Major Divided 5 1.2 1.8 1.3 1.6 Major Undivided 6 1.2 1.8 1.0 1.3 Minor 7 1.5 2.6 1.7 1.3 Collector-Local 8 1.5 2.6 1.7 1.3 Total Truck Percentages Freeway 1 7.4 9.6 10.4 9.1 Expressway 2 6.5 7.0 10.0 7.0 Principal Divided 3 4.4 7.4 4.4 5.5 Principal Undivided 4 4.3 4.4 4.1 4.4 Major Divided 5 3.5 5.2 3.7 4.4 Major Undivided 6 3.3 5.2 3.0 3.7 Minor 7 3.0 5.4 3.3 2.7 Collector-Local 8 3.0 5.4 3.3 2.7 Source: URS Greiner Woodward Clyde, "Statewide Model Truck Trip Table Update Project," prepared for the New Jersey Department of Transportation, January 1999.
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81 were also made. For each of the area types, the assignment Model to produce aggregate-level VMT statistics by facility difference was within 5%, while comparisons by facility type type and area type for use in planning and air quality studies. indicated that the differences were mostly within the ± 10% range. At the regional level, the assignment differences for Performance Measures and Evaluation both medium and heavy trucks were within 1%, which is quite reasonable. By area type, the differences between both In order to gauge how the model performs and reacts to truck types were within approximately 10%, while by facility policy changes such as toll increases and network changes, the type the differences were within 20%. In general, the model study performed three types of sensitivity analyses: replicates heavy truck trips with less variation than medium truck trips, which is important considering that the heavy · Toll Sensitivity Run: To mimic the toll increase for trucks truck VMT is a higher percentage of total VMT than the in the New Jersey Turnpike at the end of 1991; medium truck category. Finally, Table 8.24 shows the exam- · I-287 Completion: To analyze the impact of the comple- ination of the root mean square error (RMSE) term. The tion of the northern section of I-287 on the highway net- percent deviations are smaller for the large volume roadways, work; and but increase in magnitude as traffic decreases. · Trenton Complex Completion: To analyze the impact of the Trenton Complex Projection on the highway network. Model Application The toll sensitivity analysis was performed by doubling the As of this writing, the revised New Jersey Truck Model is truck toll costs along the New Jersey Turnpike. Figure 8.7 being used as a component of the Statewide Travel Demand shows the results of the toll sensitivity run. The before and Table 8.24. RMSE by volume group. Number of Average Average RMS Percent Volume Group Observations Observations Estimate R-Squared Percent Deviation Total Traffic > 80,000 30 90,270 88,224 0.5812 7.8 6.0 70,001-80,000 12 71,989 70,937 0.7864 26.9 20.0 60,001-70,000 43 64,724 67,357 0.1050 22.5 18.2 50,001-60,000 54 55,209 57,900 0.0055 22.8 18.5 40,001-50,000 94 44,963 48,682 0.1177 32.6 24.3 30,001-40,000 159 34,295 38,763 0.0063 41.8 30.5 20,001-30,000 232 25,323 26,359 0.0002 44.9 26.9 10,001-20,000 485 13,955 15,718 0.1684 51.9 35.9 1-10,000 1,077 5,211 5,863 0.3159 78.5 50.8 Total 2,185 17,050 18,411 0.8334 48.4 29.4 Total Trucks > 8,000 32 10,738 10,840 0.5336 21.1 13.9 7,001-8,000 13 7,455 5,639 0.0312 39.2 33.4 6,001-7,000 55 6,493 5,778 0.1891 31.7 23.4 5,001-6,000 56 5,446 4,576 0.0585 28.8 25.0 4,001-5,000 82 4,464 4,271 0.0179 27.6 21.8 3,001-4,000 122 3,438 3,078 0.0244 37.9 29.0 2,001-3,000 107 2,501 2,788 0.0068 78.1 44.6 1,001-2,000 285 1,414 1,585 0.0771 65.1 45.4 1-1,000 1,373 368 440 0.3820 105.4 65.1 Total 2,125 1,442 1,447 0.8100 64.0 35.0 Source: URS Greiner Woodward Clyde, "Statewide Model Truck Trip Table Update Project," prepared for the New Jersey Department of Transportation, January 1999.