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82 Figure 8.7. Impact of toll increase on trucks. after counts were obtained from the New Jersey Turnpike 8.7 Case Study SCAG Heavy-Duty Authority Traffic Volume Between Interchanges Summary. Truck Model In general, the results point to a similar trend between the model's prediction and count data. Background Figure 8.8 shows the results of the I-287 completion sensi- Context tivity analysis. Two sets of traffic counts were collected: The SCAG is the largest association of governments in the United States. SCAG functions as the MPO for six counties: Traffic counts just before the project was opened and traf- Los Angeles, Orange, San Bernardino, Riverside, Ventura, fic counts just after the project was opened; and and Imperial. This region encompasses a population exceed- Annual average daily traffic (AADT) counts along sections ing 15 million people in an area of more than 38,000 square of the New Jersey Turnpike and Garden State Parkway. miles. SCAG has a Regional Transportation Model (RTM) that is The traffic volumes estimated by the model match the used in preparing forecasts of traffic volumes and speed and counts with a reasonable degree of accuracy. is used in transportation conformity analysis to demonstrate The results of the Trenton Complex Project are shown in that air quality reductions required by the State Implementa- Figure 8.9. The total traffic volumes estimated by the model tion Plan for Air Quality are being achieved. While the RTM after the opening of the Trenton Complex match the counts had estimates of truck volumes and speeds, the California Air with a reasonable degree of accuracy. Resources Board (CARB), concerned about the impact of Overall, the model performs reasonably well and produces mobile source emissions on regional air quality, has been results reasonable for policy testing. actively pursuing improvements to emissions models for

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Figure 8.8. Impact of I-287 opening.

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Figure 8.9. Impact of Trenton Complex opening.

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85 heavy-duty trucks. (CARB defines a heavy-duty truck as a and the techniques employed in the model, it is considered truck with a gross vehicle weight of 8,500 pounds or more.) a suitable case study for the statewide truck model class. A de- A way was needed to improve the SCAG RTM to properly tailed description of the Truck Model is provided in Section 6.3. characterize truck traffic by route and time of day, and to identify the impacts of roadway conditions on route choice Modes by different types of trucking operations. Accordingly, a new component of the model was developed that provided these The HDT Model is designed to develop forecasts of HDT additional capabilities. This component is called the SCAG in the following three GVW categories: Heavy-Duty Truck (HDT) Model. 1. Light-heavy: 8,500 to 14,000 pounds GVW; 2. Medium-heavy: 14,000 to 33,000 pounds GVW; and Objective and Purpose of the Model 3. Heavy-heavy: over 33,000 pounds GVW. The HDT Model provides a methodology that can be inte- grated with the SCAG Regional Model to forecast HDT Markets activity and associated VMT for the SCAG region. The main objectives of the HDT Model are as follows: The model is specifically designed to forecast truck move- ments for air quality conformity determinations in the six- To characterize truck activity in terms of truck trips linked county SCAG region. As such, it produces VMT estimates for to goods movement, intermodal facilities, interregional the three truck weight classifications identified above. The truck traffic, regional distribution traffic, and intraregional HDT Model employs socioeconomic data by TAZ, with em- truck traffic; ployment data broken down into further detail by SIC code To understand and develop the relationships between to better estimate commodity flow demand that corresponds truck trip generation and different types of economic to truck travel demand. The industries or employment types activity and develop appropriate forecasts of future truck used in this model are retail, wholesale, manufacturing, activity at the TAZ and facility level; agriculture/mining/construction, transportation/utilities, gov- To develop model outputs for HDTs including traffic vol- ernment, and households. umes, VMT, speeds on links, transit times between specific O-D points, etc., to be used to compute mobility perfor- Framework mance indicators; and To implement a simultaneous traffic assignment proce- The HDT Model is fully integrated within the SCAG Re- dure using the TRANPLAN software system. gional Model. As such, HDTs are assigned to the highway sys- tem together with passenger car trips. The result is a forecast of volumes, including truck volumes, on all links on the high- General Approach way network. Model Class Flow Units The SCAG Heavy Duty Truck Model is an example of the truck model class. Fully integrated with the SCAG Regional The model forecasts truck volumes by truck type for each Transportation Model, the HDT Model estimates trip gener- of four time periods: a.m. peak (6:00 a.m.-9:00 a.m.), midday ation, distribution, and traffic assignment for HDTs. It em- (9:00 a.m.-3:00 p.m.), p.m. peak (3:00 p.m.-7:00 p.m.) and ploys truck trip generation rates, and uses a network of night (7:00 p.m.-6:00 a.m.). Though the model uses annual regional highway facilities for truck traffic assignment. The tons for the external trips, these data are converted to average truck traffic assignment process is integrated with the assign- daily traffic (ADT) before the trip assignment process. ment process for light-and-medium duty vehicles in the regional model, so that the effects of congestion on truck route choice are represented. This case study provides an Data overview of the HDT Model and describes how it was used to Forecasting Data generate and distribute HDT trips. The assignment and VMT results for the HDT traffic component of the model are pre- The HDT Model has two major components, internal and sented later in this case study. external. Internal truck trips begin and end inside the SCAG The HDT Model is technically a metropolitan planning or- region while external truck trips have one trip end outside the ganization model. However, given the size of the SCAG region region. Internal trucks are estimated using the socioeconomic

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86 data available at the TAZ level for the year 2000. The Validation Data employment categories used for internal truck trip gen- The SCAG HDT Model trip distribution results were vali- eration are retail, wholesale, manufacturing, agriculture/ dated against the survey data obtained from the truck trip mining/construction, transportation/utilities, government, diaries for the three classes of trucks. The truck trip length fre- and households. quency distributions from the internal trip distribution model were plotted against the observed data by truck class Model Development Data for validation. The California Department of Transportation's 1995 The model coefficients and parameters are specifically Annual Average Daily Truck Traffic on the California State developed for the HDT Model. While the internal truck trip Highway System was used to validate the truck volumes on generation involves deriving truck trip rates from truck sur- screenlines across the region. The screenlines map is shown veys, the distribution model is based on gravity model in Figure 8.10. The truck volumes and VMT were validated parameters unique to this model that are calibrated to observed against the observed truck count data by regional screenlines, truck trip length distributions. subregional screenlines, and volume groups. The standards from NCHRP Report 255: Highway Traffic Data for Urbanized Conversion Data Area Project Planning and Designs, were used for deriving validation targets. Converting commodity flows to truck trips required developing commodity-specific estimates of the portion of tonnage carried in each truck weight class and the average Model Development truck payload for each weight class. These estimates were Software developed using data from Federal Truck Inventory and Use Survey (TIUS) data and various O-D surveys carried out at The TRANPLAN travel demand modeling package was cordon points around the SCAG region. used to build and operate the HDT Model. Source: Califonia Department of Transportation. Figure 8.10. Regional model screenlines.

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87 Commodity Groups/Truck Types The final trip distribution yielded average internal truck trip lengths of 5.592 miles for light-heavy trucks, 12.827 miles for As shown in Table 8.25, the internal truck model estimates medium-heavy, and 23.914 miles for heavy-heavy trucks. trucks by gross vehicle weight and by eight employment cat- egories. The external truck trip model was derived from the commodity flow database that consists of commodities at the Commodity Trip Table two-digit STCC level listed in Table 8.15. The SCAG HDT Model divides the external trips into three types: external-internal, internal-external and external- Trip Generation external. The external trip model is based on a commodity The internal truck trip generation model uses a cross- flow database and forecasts developed by DRI/McGraw Hill classification methodology using one-digit employment and Reebie Associates. This database contains commodity categories by truck type. The trip rates were derived from a flows associated with imports and exports at the county-to- shipper-receiver survey that collected data on the number of county level within California and at the state level for all truck trips generated by different land uses/industry types other domestic and North American flows. The freight flows and related this to employment levels. Shipping and receiv- in the database are expressed in tonnage by three trucking ing rates per employee were determined from the surveys, modes: less-than-truckload (LTL) carriers, truckload (TL) which were used to calculate total trip ends by multiplying the carriers and private carriers. The external truck trips are gen- rates with SCAG employment and household data. The erated and distributed using a combination of commodity distribution of trips by sector was compared against the sur- flow data at the county level and two-digit employment data vey results and data from other studies and necessary adjust- for allocating county data to TAZs. External to external truck ments were made to the trip rates. The trip rates then were trips were developed by adjusting the 2001 regional trans- split into weight classes based on other studies. portation plan 2000 truck tables. Table 8.25 shows the various employment categories and the trip rates used for each category by truck type. TRUCKLOAD AND PRIVATE MODES Commodity flows were allocated to the TAZs largely using Trip Distribution the two-digit SIC employment data at that level. The simplest The trip generation model computes production and allocation process involved outbound flows of manufactured attractions at the TAZ level for the seven employment cate- goods by TL and private truck modes. In this case, com- gories and for households by the three truck weight classes. modities were assumed to move from manufacturing facili- Survey data from truck trip diaries collected generated fric- ties directly to their destination. Flows for a particular tion factors used in the gravity model for the purpose of commodity out of a SCAG county were allocated to TAZs in developing internal truck distribution functions in the distri- that county based on the employment share in the producing bution model. Adjustments then were made to calibrate truck SIC industry. For inbound flows of manufactured goods and movements in the distribution model based on K-factors. farm goods by TL and private truck modes, some freight was Table 8.25. Daily trip rates for internal truck trip generation. Light-Heavy Medium-Heavy Heavy-Heavy Employment Category 8,5000-14,000 Pounds 14,000-33,000 Pounds Over 33,000 Pounds Households 0.0390 0.0087 0.0023 Agriculture/Mining/Construction 0.0513 0.0836 0.0569 Retail 0.0605 0.0962 0.0359 Government 0.0080 0.0022 0.0430 Manufacturing 0.0353 0.0575 0.0391 Transportation/Utility 0.2043 0.4570 0.1578 Wholesale 0.0393 0.0650 0.0633 Other 0.0091 0.0141 0.0030 Note: Rates are per household or per employee in each category. Source: Southern California Association of Governments Heavy-Duty Truck Model.

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88 assumed to move directly to manufacturing facilities for use 4.5 for medium-heavy, and 2.0 to 6.0 for heavy-heavy trucks. in a production process, and the remainder to move to a The congestion factors ranged between 1.0 and 1.3. warehouse for eventual retail distribution. The IMPLAN input-output (I-O) models were used to determine the Model Validation portion of each commodity that falls into these two groups. These models produce I-O tables that can be used to deter- The distribution of total trip ends by employment category mine the commodity inputs per unit output of each industry. was compared with other major truck studies to calibrate and The models first were used to characterize the portion of each validate the truck trip rates. commodity flowing into a county that goes to final demand by consumers and the portion that goes to industry. Flows to Trip Distribution consumers were assumed to pass through distribution ware- houses and were allocated to TAZs based on warehouse space The comparison of truck trip length distributions from the in each TAZ. model against the observed data from the truck surveys served as a criterion for trip distribution validation. These are shown in Figures 8.11, 8.12, and 8.13 for each of the three LTL MODE truck classes. All LTL shipments, inbound and outbound, were assumed to move through an LTL distribution/consolidation facility. Mode Choice Because the number of LTL carriers making external trips is relatively small, these flows were disaggregated based on the Not applicable. exact locations of the LTL facilities. A list of these LTL carri- ers and facility locations was obtained from the 1995 SCAG Modal Assignment interregional goods movement study. The HDT Model was validated against a number of specific parameters. The model estimated Year 2000 truck movements Mode Split across 16 regional screenlines to within 12% of the correspon- Not applicable. ding truck traffic counts (all screenlines combined). All differ- ences on individual screenlines were well within allowable tol- erances established for regional modeling processes. The Flow Unit and Time Period Conversion model estimated 22.4 million VMT by all trucks within the Commodity flows are converted from annual tonnage to SCAG modeling region. This was within 2% of the VMT esti- truck trips by truck weight class by using the TIUS data and mates from the HPMS. O-D surveys performed at cordon points around the SCAG The modal assignment validation results are summarized region. in Table 8.27. The California Department of Transportation's weigh-in- motion stations collect data from along the state highway POST MODEL ADJUSTMENT OF SPEED FOR HDTS system that are used for deriving truck time of day factors by truck class and by direction. The SCAG RTM assumes the same speed for all vehicles traveling on the same roadway segment. For instance, both HDTs and passenger cars are loaded on the same segment Assignment of the roadway and the current model cannot distinguish Truck-specific time period factors, derived from weigh-in- between the lanes that permit HDT travel and those that do motion truck data, were applied to assign daily truck activity not. In order to reasonably represent the slower speeds that to the four model time periods (a.m. peak, midday, p.m. most trucks are traveling, a post model speed adjustment was peak, and night). Trucks were converted into PCEs during made using available Freeway Performance Measurement the assignment phase. The trip assignment process simulta- Project (PeMs) data. neously loaded both HDTs and light-and-medium duty The SCAG RTM did not have a separate network for autos/trucks so that all vehicle types were accounted for in the HDTs, unless a truck-only lane is present. Both HDTs and traffic stream. passenger cars are loaded on the same segment of the road- As shown in Table 8.26, truck PCEs were estimated for each way, regardless of any truck-lane restrictions. Therefore, both link by the product of a grade factor and a congestion factor. HDTs and passenger cars have the same speed on the same The grade factors ranged from 1.2 to 3.6 for light-heavy, 1.5 to output roadway segment.

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89 Table 8.26. Truck PCE factors by GVW and grade. Heavy-Duty Vehicle Passenger Car Equivalent Values by Vehicle Type, Terrain, and Percent Trucks Percent Grade Percent Trucks Length (Miles) 0-2 3-4 5-6 >6 Light-Heavy 0 5 <1 1.2 2 3.6 3.6 0 5 1-2 1.2 2 3.6 3.6 0 5 >2 1.2 2 3.6 3.6 5 10 <1 1.2 2 3.6 3.6 5 10 1-2 1.2 2 3.6 3.6 5 10 >2 1.2 2 3.6 3.6 10 100 <1 1.2 2 3.6 3.6 10 100 1-2 1.2 2 3.6 3.6 10 100 >2 1.2 2 3.6 3.6 Medium-Heavy 0 5 <1 1.5 2.5 4.5 4.5 0 5 1-2 1.5 2.5 4.5 4.5 0 5 >2 1.5 2.5 4.5 4.5 5 10 <1 1.5 2.5 4.5 4.5 5 10 1-2 1.5 2.5 4.5 4.5 5 10 >2 1.5 2.5 4.5 4.5 10 100 <1 1.5 2.5 4.5 4.5 10 100 1-2 1.5 2.5 4.5 4.5 10 100 >2 1.5 2.5 4.5 4.5 Heavy-Heavy 0 5 <1 2 3.3 6 6 0 5 1-2 2 3.3 6 6 0 5 >2 2 3.3 6 6 5 10 <1 2 3.3 6 6 5 10 1-2 2 3.3 6 6 5 10 >2 2 3.3 6 6 10 100 <1 2 3.3 6 6 10 100 1-2 2 3.3 6 6 10 100 >2 2 3.3 6 6 Passenger Car Equivalent Value Adjustment Factors for Highway Congestion Percent Trucks V/C Ratio L-H M-H H-H 0 5 0.0 0.5 1.0 1.0 1.0 0 5 0.5 1.0 1.0 1.0 1.2 0 5 1.0 1.5 1.1 1.2 1.3 0 5 1.5 2.0 1.0 1.2 1.2 0 5 2.0 99.0 1.0 1.2 1.3 5 10 0.0 0.5 1.0 1.0 1.0 5 10 0.5 1.0 1.0 1.0 1.2 5 10 1.0 1.5 1.2 1.3 1.3 5 10 1.5 2.0 1.0 1.2 1.3 5 10 2.0 99.0 1.0 1.2 1.3 10 100 0.0 0.5 1.0 1.0 1.0 10 100 0.5 1.0 1.0 1.0 1.2 10 100 1.0 1.5 1.2 1.3 1.3 10 100 1.5 2.0 1.0 1.2 1.3 10 100 2.0 99.0 1.0 1.2 1.3 Source: Southern California Association of Governments Heavy-Duty Truck Model.

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90 Percent 40 Model Survey 35 Log (Survey) Log (Model) 30 25 20 15 10 5 0 5 10 15 20 25 30 35 -5 -10 Distance (in Miles) Source: Southern California Association of Governments Heavy-Duty Truck Model. Figure 8.11. Trip length frequency curves (light-heavy trucks). However, HDTs are assumed to travel slower than passen- The following section describes how the relationship ger cars because: between HDT speed and average roadway speed was used to conduct post model speed adjustment for the HDT Model. HDTs can only travel on the outside lanes; their choice of travel is relatively limited. SPEED OF THE HDTS ON FREEWAYS The speed on the outside lanes is slowed by vehicles enter- ing and exiting the highway. A total of 9,361 records were selected through the PeMs HDTs accelerate and decelerate more slowly than passen- database. A detailed review of the database revealed some ger vehicles. problems, such as detectors that lacked data or had observed Percent 14 Model Survey 12 Log (Survey) Log (Model) 10 8 6 4 2 0 5 10 15 20 25 30 35 40 -2 Distance (in Miles) Source: Southern California Association of Governments Heavy-Duty Truck Model. Figure 8.12. Trip length frequency curve (medium-heavy trucks).

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91 Percent 12 Model Survey 10 Log (Survey) Log (Model) 8 6 4 2 0 10 20 30 40 50 60 70 -2 Distance (in Miles) Source: Southern California Association of Governments Heavy-Duty Truck Model. Figure 8.13. Trip length frequency curves (heavy-heavy trucks). speeds out of range of reasonably expected values. SAS statis- The R-Square value was 0.98. The t-statistic for the in- tical analysis software programs were used to screen and dependent variable was 417.95. The equation of the result analyze the database. was: Only 3,465 out of 9,361 records were suitable for the analy- Heavy-duty truck speed = 0.31 + 0.9657* average freeway speed sis. The dependent variable was the average speed of the out- side two lanes. The independent variable was the average SPEED OF HDTS ON ARTERIALS speed of all lanes at each detector's location. A simple linear model was used to build the relationship between the There is no reliable data to derive the speed of HDTs on ar- dependent and independent variables. terials, although their speed is slower than that of passenger Table 8.27. Comparison of truck volumes and counts on regional model screenlines. Count Volume Model Volume Difference Percent Allowable per Screenline (ADT) (ADT) (Model-counts) Difference NCHRP 1 61,870 73,778 11,908 19% 31% 2 106,041 118,760 12,719 12% 25% 3 59,381 59,610 229 0% 30% 4 65,344 61,901 (3,443) -5% 29% 5 84,261 93,010 8,749 10% 23% 6 73,546 73,778 232 0% 28% 7 52,893 46,866 (6,027) -11% 36% 8 84,400 82,117 (2,283) -3% 26% 9 29,135 28,712 (423) -1% 40% 10 20,495 23,118 2,623 13% 46% 11 15,762 14,879 (883) -6% 52% Total 653,128 676,529 23,401 4% N/A Source: Southern California Association of Governments Heavy-Duty Truck Model.