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47 The capacity restraint assignment involved estimating de- Bypass feasibility studies lays with the BPR curve, which requires free speed and ca- EIS traffic data input. pacity for a link. Free speeds and 24-h lane capacities were set separately by functional class. After an initial traffic as- A major motivation for building the statewide model was signment, capacities were adjusted upward within urban ar- the need to determine the impacts truck traffic has on major eas to account for the sparse network there. Because of the highways. The freight component addresses these needs by rural orientation for the model, the passenger car equivalent forecasting commodity-carrying truck volumes. factor for trucks was one. Wisconsin Statewide Freight Component Summary Both Virginia and Louisiana (Wilbur Smith Associates State population: 5.5 million 2004) implemented essentially two distinct travel forecasting State area: 65,503 square miles models, referred to as the "micro" model and the "macro" Gross state product: $200 billion model. Together these two models create a way to consider No. of zones: 1,875 long trips across states while still working at a sufficiently External zone structure: Halo, states, aggregations of states, detailed scale for trips within the state. BEA regions Internal zone structure: Aggregations of TAZs The purpose of the macro model is to provide information No. of links: 200,000 on trips passing through Virginia or having one end within No. of commodities: 25 categories Virginia. The macro model spans the entire United States and Freight modes: Truck, rail, water (deep and inland), air works at the county level within the state. The macro model cargo has just 204 TAZs, of which 135 are within Virginia. The Production: Employment by industry group macro network has almost 59,000 links. Consumption: IO table, employment by industry group, population The micro model operates within Virginia at the level of cen- Distribution: Gravity expression sus tracts and places. The micro model provides the necessary Mode split: Fixed shares forecasts of travel to satisfy statewide planning needs. (Sources: Truck type split: Fixed shares Wilbur Smith Associates 2003, Virginia's response to the Peer Assignment: Static equilibrium, multiclass Exchange questionnaire 2004, Virginia's response to the Syn- Delay estimation: BPR curves thesis questionnaire 2005, Wilbur Smith Associates 2005b.) Major data: TRANSEARCH Time frame: 2.5 years of development time CASE STUDY 5: WISCONSIN FREIGHT Computation time: 2 h COMPONENT In-house staff: 3 FTEs At the time of this writing, Wisconsin had just finished the Wisconsin's freight component is multimodal and third generation of its travel forecasting model. However, commodity-based. The key database for the model was Ree- documentation of model details had not been completed. bie's TRANSEARCH from 2001 aggregated to BEA This case study is based on a series of interim memoranda, regions. This database was factored into counties using com- the consultant's scope of work, questionnaire responses, and modity flow information for Wisconsin that was assembled interviews with the modeling team. by Reebie in 1996. The following is a list of the commodity groups, each of which consist of whole two-digit STCC Wisconsin's overall statewide modeling effort was designed groups or represent intermodal shipments. to meet these needs: Farm and fish; Long-range plan development (statewide and urban) Forest products; Air quality conformity analysis Metallic ores; Corridor planning for capacity investment, program- Coal; ming, and design Nonmetallic minerals; Modal investments (e.g., introduction of new intercity Food; bus service) Lumber; Traffic forecasting for project design Pulp, paper, allied products; Traffic Impact Analysis Chemicals; Traffic diversion impacts Petroleum or coal products; Modal diversion impacts Clay, concrete, glass, and stone; Congestion mitigation planning--Wisconsin DOT Primary metal products; Intelligent Transportation System "blue route" corridor Fabricated metal products; planning efforts Transportation equipment; Detour simulation analysis Waste or scrap equipment;

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48 Secondary warehousing; Total Rail drayage; Generation Tons Other minerals; Furniture or fixtures; Printed matter; Distribution Tons Other nondurable manufacturing products; by O-D Other durable manufacturing products; Miscellaneous freight; Truck Shares O-D Trucks Hazardous materials; and & Payloads Air drayage. These commodity groups were selected to emphasize those Truck Trucks commodities that were of the greatest economic importance to Assignment by Route Wisconsin and to allow a direct match to industrial categories. FIGURE 26 Structure of Wisconsin's freight component. O-D = Wisconsin's freight component essentially contains the origindestination. major UTP four-steps, as illustrated in Figure 26. The truck network is nearly identical to the passenger car Wisconsin's zone system for freight differs somewhat network within Wisconsin and its halo, as seen in Figure 28. from the passenger component. The zone system consists of This network is very detailed within and near Wisconsin and (1) 1,642 small TAZs within Wisconsin, (2) counties within it spans most of the contiguous United States, except for the a thin halo around Wisconsin, (3) a few states or BEA re- Southeast, at a coarser level of detail (owing to the aggre- gions near Wisconsin, (4) multistate regions for the rest of gated southeast freight zone using Atlanta as a loading the contiguous United States, and (5) four huge zones for the point). This contrasts with the passenger component whose rest of North America (see Figure 27). TAZs within Wis- network extends only into the halo. Wisconsin's truck net- consin and its halo match the passenger component exactly. work is nationwide to "account for global market impacts on FIGURE 27 Wisconsin's freight component zone system.

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49 FIGURE 28 Wisconsin's freight component network. freight movements." Rural portions of Wisconsin contain all Commodity generation equations were developed by linear functional classes that are major collector or higher. Urban regression between commodity production and industrial em- portions of Wisconsin contain all functional classes that are ployment for each commodity group based on county-level collector or higher, except for the counties covered by the data. In a manner similar to Virginia, Wisconsin identified Southeastern Wisconsin Regional Planning Commission. consuming industries and final demand for a given commod- Network attributes for links within Wisconsin come from ei- ity group by using a national IO table. Regression analysis was ther the Wisconsin Information System for Local Roads or then performed to ascertain the relationships between con- the Wisconsin DOT's State Trunk Network inventory. The sumption totals in the TRANSEARCH database and zonal em- network outside of Wisconsin was obtained from FAF, ployment and population. The independent variables used in NHPN, and TIGER line files. the regression are shown in Table 9. Employment data were TABLE 9 INDEPENDENT VARIABLES FOR TONNAGE GENERATION FOR SELECTED COMMODITY GROUPS Commodity Production Consumption Farm and Fish SIC01 + SIC02 + SIC07 + SIC20 + SIC54 SIC09 Nonmetallic Minerals SIC14 + SIC15 + SIC16 + SIC14 + SIC15 + SIC16 + SIC17 SIC17 Food SIC20 Population Lumber SIC24 SIC24 + SIC25 + SIC50 Pulp, Paper, Allied Products SIC26 SIC26 + SIC27 Chemicals SIC28 Total employment Clay, Concrete, Glass, and Stone SIC32 Population Primary Metal Products SIC33 SIC33 + SIC34 Fabricated Metal Products SIC34 Population Transportation Equipment SIC37 SIC42 Secondary Warehousing SIC42 Population Furniture or Fixtures SIC25 Population Printed Matter SIC27 Total employment Other Nondurable Manufacturing Products SIC21 + SIC22 + SIC23 Population Other Durable Manufacturing Products SIC30 + SIC31 + SIC35 + SIC50 SIC36 + SIC38 + SIC39

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50 obtained from Wisconsin's Department of Workforce Devel- TABLE 10 opment. Employment and demographic forecasts came from WISCONSIN PAYLOAD FACTORS BY TWO-DIGIT COMMODITY CODES Woods & Poole growth rates applied to the Department of Workforce Development base data. STCC Description Tons per Truck 1 Farm products 24 8 Forest products 13 Production or consumption of certain commodity groups 9 Fresh fish or other marine products 6 did not correlate well with demographic variables. These com- 10 Metallic ores 24 modity groups were handled by factoring base year production 11 Coal 24 and consumption data from the TRANSEARCH database. 13 Crude petroleum, natural gas, or gasoline 14 14 Nonmetallic minerals, excluding fuels 19 Wisconsin has 27 special generators for freight, which 19 Ordnance or accessories 24 20 Food or kindred products 18 were county and commodity combinations. These special 21 Tobacco products 5 generators consist of retail distribution centers, truckrail in- 22 Textile mill products 5 termodal terminals, ports, airports, and obvious outliers from 23 Apparel or other finished textile products 3 the trip generation calibration, such as a highly automated 24 Lumber or wood products 15 General Motors assembly plant. The only primary data col- 25 Furniture or fixtures 3 lection specifically for the freight component was a pilot 26 Pulp, paper, or allied products 16 truck survey at the Union Pacific intermodal terminal in 27 Printed matter 9 28 Chemicals 22 Rochelle, Illinois. Another survey at this location is planned. 29 Petroleum or coal products 19 30 Rubber or miscellaneous plastics products 4 When forecasting the relationship between employment and 31 Leather or leather products 3 commodity production it is important to account for changes in 32 Clay, concrete, glass, or stone products 23 worker productivity. Wisconsin obtained worker productivity 33 Primary metal products 19 factors for future years from a regional economic model. 34 Fabricated metal products 24 35 Machinery--Other than electrical 9 36 Electrical machinery, equipment, or supplies 8 Trip distribution is handled by a gravity expression, where 37 Transportation equipment 12 the friction factor for each commodity has been calibrated 38 Instruments--Photographic or optical goods 5 such that the model replicates average trip lengths from the 39 Miscellaneous manufacturing products 2 TRANSEARCH data applied to the FAF highway network. 40 Waste or scrap materials 16 The metric for spatial separation was distance in miles, dij. 41 Miscellaneous freight shipments 23 Therefore, friction factors were determined by this formula 42 Shipping devices returned empty 4 43 Mail and express traffic 3 f (dij) = exp(dij /) 44 Freight forwarder traffic 4 45 Shipper association or similar traffic 3 where is a constant that varies by commodity group. Val- 46 Miscellaneous mixed shipments 7 ues of range from approximately 100 to 2,800, depending 47 Small packaged freight shipments 4 on the commodity. 48 Hazardous waste 16 49 Hazardous materials 18 Wisconsin's freight component has four principal modes: 99 Unknown 12 truck, air cargo, railroad, and water shipping (both deep and Note: STCC = Standard Transportation Community Codes. inland). The model also explicitly considers three intermodal combinations (truckair, truckrail, and truckwater) by assumed 306 trucking days per year. Table 10 was derived including drayage links on the highway network between principally from Wisconsin records within VIUS. Wisconsin counties and major intermodal terminals, some of which are located in Illinois and Minnesota. Mode split The only validation for the freight component that was was accomplished by fixed shares as derived from the distinct from passenger traffic was a comparison of com- TRANSEARCH database. Air, rail, and water modes are not modity tonnages between the model and TRANSEARCH. assigned to a network. Assigned trucks were also compared with truck counts at ap- proximately 300 stations for reasonableness--a direct com- Wisconsin's highway traffic assignment is 24-h, multi- parison is not possible because the model forecasts com- class, and user-optimal equilibrium. Trucks are loaded to the modity carrying trucks only, not total trucks. Total truck network at the same time as passenger cars; therefore, the VMT was checked against available data sources. route choice of trucks is influenced by congestion. Trucks re- ceive a constant passenger car equivalent factor of 1.9. De- Outputs from the freight component aid other planning ef- lay was estimated with BPR curves. forts. An important feature of Wisconsin's model is its interface with MPO models in the state. Internal truck travel in the MPO Annual tonnages of commodities were converted to daily models is handled with procedures taken from the QRFM, but trucks by using the payload factors from Table 10 and an external traffic patterns come from the statewide model. In ad-