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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. "5.4 Forecasting Data." 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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24 Figure 5.4. Freight Analysis Framework highway network. Intermodal Terminals National Transportation Atlas Database (NTAD) Intermodal terminals are facilities for transferring freight The National Transportation Atlas Database (NTAD) is a from one mode, such as truck, to another mode, such as rail. collection of GIS data layers in 1:1,000,000 scale developed by Knowing the location of these terminals is important when the U.S. Department of Transportation and other Federal assigning a complete freight shipment from an initial origin agencies. The NTAD is available from the Bureau of Trans- to its ultimate destination. It also is important in forecasting portation Statistics Mapping Center at http://www.transtats. the behavior of freight since freight is neither produced nor bts.gov/mappingcenter.asp. consumed at these terminals but merely transshipped. The Bureau of Transportation Statistics provides data on the 5.4 Forecasting Data location and attributes of these intermodal terminals, includ- ing the type of commodity handled. While intermodal freight Population is often considered freight moving in sealed containers, the Population data used in freight forecasting is typically used intermodal terminals include all facilities where freight ­ in traditional transportation forecasting. This includes both including bulk shipments ­ changes modes. a base and a forecast horizon year or years for a variety of TAZ. For areas outside of the state study area, population Bureau of Transportation Statistics Intermodal data can be obtained from the U.S. Census Bureau, which Terminals typically is the basis for base year passenger transportation forecasting. Forecasts of national population only are avail- The Bureau of Transportation Statistics Intermodal Termi- able through commercial vendors. nal Facilities data set contains geographic data for freight trans- fer facilities in the United States. Attribute data includes the Employment modes serving the facility, the name of the railroad (if any) serving the facility, the type of cargo, and the direction of the While employment data are typically used in passenger transfer. The database provides location and attribute infor- transportation forecasting, the level of industry detail is mation for use in national and regional network analysis insufficient for freight forecasting. Industry information is applications. Attribute data are extracted from a variety of rail- developed from mandatory quarterly ES-220 submittals by road and port carriers operators and associations. Data reflects employers to state employment security agencies and used by conditions at facilities in 1995-1996 and is subject to frequent the U.S. Bureau of Labor to compute unemployment statis- change. Some facilities may be dormant or permanently closed. tics. However processed, the data released to the public is The intermodal terminal database is available from the aggregated to suppress confidential information. That data, Bureau of Transportation Statistics Mapping Center at available from the Census Bureau's County Business Pat- http://www.transtats.bts.gov/mappingcenter.asp. terns, is described below. More geographic detail is available