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NCHRP Report 606: Forecasting Statewide Freight Toolkit (2008)
National Cooperative Highway Research Program (NCHRP)

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Horowitz, Alan, Cohen, Harry, Pendyala, Ram, Transportation Research Board. "6.3 The Truck Model." NCHRP Report 606: Forecasting Statewide Freight Toolkit. Washington, DC: The National Academies Press, 2008.

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Front Matter (R1-R10)
Chapter 1 - Introduction (1-2)
2.2 Statewide Freight Forecasting (3-3)
2.3 Freight Terminology (4-4)
3.1 Freight Policy Needs (5-7)
3.2 Available Methods (8-8)
4.1 Direct Factoring (9-9)
4.2 Trip Generation (10-10)
4.3 Trip Distribution (11-11)
4.4 Mode Split (12-13)
4.5 Traffic Assignment (14-14)
4.6 Economic/Land Use Modeling (15-15)
5.1 Model Development (16-19)
5.2 Flow Conversion (20-22)
5.3 Network Data (23-23)
5.4 Forecasting Data (24-24)
5.5 Validation Data (25-25)
5.6 Classification Schemes (26-26)
6.1 The Direct Facility Flow Factoring Method (27-28)
6.2 The Origin-Destination Factoring Method (29-30)
6.3 The Truck Model (31-31)
6.4 The Four-Step Commodity Model (32-32)
6.5 The Economic Activity Model (33-34)
7.2 Performance Measures for States' Primary Needs (35-35)
7.4 Recommended Toolkit Performance Measures (36-41)
8.1 Development of a Forecasting Model Template (42-43)
8.2 Case Study Minnesota Trunk Highway 10 Truck Trip Forecasting Model (44-46)
8.3 Case Study The Heavy Truck Freight Model for Florida Ports (47-53)
8.4 Case Study Ohio Interim Freight Model (54-62)
8.5 Case Study Freight Analysis Framework (63-72)
8.6 Case Study New Jersey Statewide Model Truck Trip Table Update Project (73-81)
8.7 Case Study SCAG Heavy-Duty Truck Model (82-91)
8.8 Case Study Indiana Commodity Transport Model (92-100)
8.9 Case Study Florida Intermodal Statewide Highway Freight Model (FISHFM) (101-109)
8.10 Case Study Cross-Cascades Corridor Analysis Project (110-118)
8.11 Case Study Oregon Statewide Passenger and Freight Forecasting Model (119-129)
References (130-130)
Bibliography (131-133)
Acronyms (134-135)
Appendix A - Commodity Classifications (136-145)
Appendix B - Tool Components and Forecastable Performance Measures (146-151)
Appendix C - References with Mode Components (152-158)
Abbreviations used without definitions in TRB publications (159-159)

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31 on the availability of a highway network model from other sources. If no statewide highway network or other vehicle trip tables are available from a statewide travel demand model or other source, a rules-based assignment model is used. If a highway network is available or can be developed, a freight truck only assignment can be used. If a highway network and other vehicle trip tables are available, a multiclass assignment can be used. Case Studies and References Two case studies demonstrate the O-D factoring method: the Ohio Freight Model Case Study and the Freight Analysis Framework Case Study. These are described in Sections 8.4 and 8.5, respectively. Oklahoma Model ­ This model and forecast system were developed in 2000 by TranSystems Corporation. It is a con- ventional model based on Reebie TRANSEARCH data. Kentucky Corridor Model ­ This model was developed in 1997 by Wilbur Smith Associates.12 The network and base data were updated in 2001 by Wilbur Smith, without chang- ing the model methodology.13 Figure 6.3. The truck model. 6.3 The Truck Model forecasts both passenger and goods movement and conse- Description quently use a simultaneous assignment of truck trips with As shown in Figure 6.3, truck models use the trip genera- automobile trips. tion and distribution model components to produce a table Truck models follow a three-step process of trip genera- of truck trips and uses assignment model components to tion, trip distribution, and traffic assignment. The truck types assign that table of truck trips. As truck models address only often considered in a truck model are broadly classified into the single mode of trucks, they do not require a mode split light, medium, and heavy trucks based on gross vehicle component. weight (GVW) ratings. Although weight-based, these classi- Truck models usually attempt to account for all shipments fications are loosely correlated to other defining characteris- of goods, including local delivery. Freight truck volumes, as tics of trucks, which are described in the Quick Response freight is defined in most data sources such as the CFS and Freight Manual.3 TRANSEARCH, dominate in rural areas between distant cities. Truck models that include local delivery are more use- · Light trucks are defined as vehicles with four or more tires ful for states with closely spaced or contiguous urban areas. and two axles, with a GVW of less than 16,000 pounds. For this reason, the sole example of a state truck model · Medium trucks are defined as single-unit vehicles with six identified for inclusion in this Toolkit was developed for or more tires and two to four axles, with a GVW of 16,000 New Jersey, the most densely populated state in the nation. to 52,000 pounds. However the nation's largest metropolitan planning organi- · Heavy trucks are defined as double-unit, triple-unit, or zation, the Southern California Association of Governments, combination vehicles with five or more axles, with a GVW is included as an example of a statewide truck model because greater than 52,000 pounds. it uses the same techniques and the region's geographic and population size is greater than that of many states. Truck Using these definitions, medium trucks directly correlate models are more commonly a component of urban travel to single-unit trucks collected in truck surveys and heavy forecasting models. trucks directly correlate to double- and triple-unit trucks. Truck models obviously cannot analyze shifts between The truck counts do not usually separate light trucks from modes, since by definition they include only the truck freight passenger cars and are sometimes estimated as part of pas- mode. They are usually part of a comprehensive model which senger vehicle travel.