National Academy of Sciences | 150 Year Anniversary

Questions? Call 800-624-6242

| Items in cart [0]

The National Academies Press

Rights & Permissions

topleft topright

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

Citation Manager

Horowitz, Alan, Cohen, Harry, Pendyala, Ram, Transportation Research Board. "4.2 Trip Generation." NCHRP Report 606: Forecasting Statewide Freight Toolkit. Washington, DC: The National Academies Press, 2008.

Please select a format:

BibTeX EndNote RefMan


Page
10
bottomleft bottomright
Page
10
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)

Below are the first 10 and last 10 pages of uncorrected machine-read text (when available) of this chapter, followed by the top 30 algorithmically extracted key phrases from the chapter as a whole.
Intended to provide our own search engines and external engines with highly rich, chapter-representative searchable text on the opening pages of each chapter. Because it is UNCORRECTED material, please consider the following text as a useful but insufficient proxy for the authoritative book pages.

Do not use for reproduction, copying, pasting, or reading; exclusively for search engines.

OCR for page 10
10 Table 4.1. Freight model classes by component. Model Component Direct Trip Trip Mode Traffic Economic/Land Model Class Factoring Generation Distribution Split Assignment Use Modeling Direct Facility Of facility Flow Factoring flows Method O-D Factoring Of O-D tables Included Included Method Truck Model Based on exo- Included Not Included genously Applicable supplied zonal activity Four-Step Based on exo- Included Included Included Commodity genously Model supplied zonal activity Economic Based on out- Included Included Included Included Activity Model puts of eco- nomic model flow matrix or (in the absence of a matrix) incorporated trip generation, a production and attraction file for all geo- into a gravity model of shipment distribution. graphic zones, customarily serves as input to other model components used in freight forecasting. However, the pro- duction and attraction file can be useful on its own, showing 4.2 Trip Generation freight trips that end in zones. As shown in Figure 4.2, the trip generation model compo- The trip generation models used in statewide freight fore- nent forecasts the productions and attractions of freight casting include a set of annual or daily trip generation rates movements that begin or end in a geographic zone based on or equations by commodity, providing annual or daily flows the characteristics of that zone. The most common charac- originating or terminating in geographic zones as functions teristic used in trip generation is the employment by industry of TAZ or county population and disaggregated employment that produces and consumes various goods. The output of data. Production and consumption tonnages for special gen- erators like seaports, airports, and other intermodal transfer terminals are directly obtained from the port or terminal for 1 the base year. The commodity flow tonnages for external Data zones are obtained from the commodity flow database and are disaggregated at the TAZ or county level based on the dis- tribution of employment within each TAZ or county. Direct Facility For the truck model class of freight models, trip generation Flowing Factoring is usually calculated separately for internal trips between zones (I-I) and external trips between internal and external zones (E-I, I-E, and E-E). Trip rates are derived from national Link sources such as the Quick Response Freight Manual and/or Volumes regional sources, if available. These are applied to households 5 and employment data to obtain truck trips internal to the state. Different trip rates by truck type are used for truck trip Figure 4.1. Direct productions and attractions. The socioeconomic data used in factoring. a typical truck model are consistent with those data used in