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11 1 Data Trip Generation Multimodal P&A File 2 Figure 4.2. Trip generation. passenger models, except that the employment data are strat- Commerce's Bureau of Economic Analysis 1996 input-output ified into more employment categories. This process provides tables and tailored to the commodity group being estimated. more accuracy for truck travel, allows for a direct relationship Production and consumption tonnages for special genera- between the commodities being estimated in the external trip tors like seaports, airports, and other intermodal transfer model that captures truck flows in and out of the state, and terminals are obtained directly from the port or terminal for helps allocate these commodities to traffic analysis zones the base year. The commodity flow tonnages for external within the state. The stratification of employment data is usu- zones are obtained from the commodity flow database and ally by Standard Industrial Classification (SIC) codes at the are disaggregated at the TAZ or county level based on the dis- two-digit level. The trip rates are usually adjusted during tribution of employment within each TAZ or county. model calibration based on local or regional knowledge of As shown in the Section 8.8 case study, the Indiana Com- truck trip ends. Section 5.6 includes a detailed discussion of modity Transport Model includes 21 commodity groups SIC codes, and Table A.2 in Appendix A shows the corre- considered important to the state. The trip generation equa- spondence between STCC codes and the Standard Classifica- tions were developed based on a regression of data available tion of Transported Goods SCTG. from the Bureau of Transportation Statistics' 1993 Com- The external truck trips (E-I and I-E) entering and leaving modity Flow Survey (CFS). The Nebraska Statewide Freight the state are derived from observed data at external stations Forecasting Model also uses the 1993 CFS data to develop a and truck survey data. These data are disaggregated to the trip production model. However, IMPLAN software pro- TAZ level based on percent distribution of various employ- vided input-output coefficients that were used to derive trip ments by industry within each internal TAZ. Some truck attraction equations. The Vermont Statewide Freight Study models also use commodity flows that have either their ori- uses O-D data from the TRANSEARCH database, organized gin or destination within the state boundary. This process at the two-digit STCC level to build the trip tables. In addi- involves the conversion of commodity flow tonnage to truck tion to commodity flows, the Vermont study uses roadside trips. The through trips (E-E) that pass through the state with surveys, motor carrier surveys, and data from interviews with both origin and destination outside the state are added to the key shippers to develop the trip tables. The Iowa State- external truck trips as well. wide Freight Transportation Model also uses the Reebie Trip rates are applied to socioeconomic data and also are TRANSEARCH commodity data, organized by Bureau of used for truck terminals and intermodal facilities in conjunc- Economic Analysis zones at the two-digit STCC level. The tion with observed truck trips at airports, seaports, and rail Nebraska, Vermont, and Iowa models are not the subject of terminals. case studies in Section 8.0, but are cited in the References sec- The commodity-based trip generation model includes a set tion of the Toolkit. Section 5.6 describes STCC codes in of annual or daily trip generation rates or equations by com- greater detail. modity, providing annual or daily flows as functions of TAZ or county population and disaggregated employment 4.3 Trip Distribution data. The Florida Intermodal Statewide Highway Freight Model, described in Section 8.9, uses Reebie Associates' As shown in Figure 4.3, the trip distribution model com- TRANSEARCH freight database to derive linear production ponent produces the production and attraction file for zones and consumption equations for 14 commodity groups. The to forecast a table of freight flows between all geographic independent variables are primarily population and employ- zones. The trip distribution model also requires some infor- ment by SIC at the county level for the State of Florida. The mation about the degree of difficulty for freight to travel employment categories are based on the U.S. Department of between all zones. With the exception of single mode models