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43 the existing composition of the zone and the quantities derived from Reebie's TRANSEARCH database and from of newly developed land. systematic adjustments based on truck counts. Person tours--The four tour submodels are conceptually similar. They use microsimulation to create a list of tours The TRANSEARCH data for Virginia gave commodity and then a list of trips within tours. Selection probabili- flows in tons from, to, and within Virginia. Data were orga- ties come from logit expressions. Trips have attributes of nized geographically by state, BEA region, and Virginia origin zone, destination zone, start time, and mode. county. Separate tables were given for each two-digit commodity group from STCC for truck, railroad, water, and Transport of large commodities is handled somewhat tra- air. Trucks were further divided into truck-load, less-than- ditionally, once the flows of goods have been established by truckload, and private. Eventually the model was organized the economic activity modules. Flows between activity into 28 commodity groups, as listed in Table 8. The model zones are converted to flows between TAZs by ap- TRANSEARCH database omits many agricultural products portioning flows according to employment levels. OD flows and local service and delivery trucks, which particularly of goods are converted to a whole number of vehicles affect estimates of truck movements within the state. grouped by vehicle types and departure times, using a Monte Carlo process. The list of vehicle trips, so obtained, can be The freight component uses the same highway network as post-processed in a traffic microsimulation or aggregated for the passenger component. This network has nearly 247,000 a traditional traffic assignment. The 28 commodity cate- links and almost 1,600 TAZs. The network is illustrated in Fig- gories are consistent with two-digit STCC. ures 21 and 22, although it is difficult to get a sense of the highly detailed network within Virginia from these figures. The zone Service and delivery commercial tours are created with system is illustrated in Figures 23 and 24. It can readily be seen microsimulation. As with person tours, logit expressions are that the network and zone system span the full contiguous 48 used to obtain selection probabilities. The overall number of states, but is sharply focused on Virginia. A moderately de- tours relates to the amount and types of employment in the tailed network and set of zones extend well into adjacent states activity model zone. The attributes of each trip are deter- and beyond. Virginia implements subzoning for traffic assign- mined in the following order: stop purpose, stop TAZ, de- ment that helps eliminate lumpy vehicle loadings to links. parture time (accounting for earlier stops on the tour), stop subzone, and vehicle type (light, medium, and heavy). This Virginia Statewide Freight Component Summary method is described in an article about Calgary's urban State population: 7.1 million model (Hunt et al. 2004b). This method has these processes: State area: 42,769 square miles tour generation, tour stop time, tour purpose and vehicle Gross state product: $304 billion type, next stop purpose, next stop location, and stop duration. No. of zones: 1,584 The last three processes are performed iteratively with ear- External zone structure: Halo, aggregations of states lier stops in the tour influencing the nature of later stops. Internal zone structure: Micro/macro No. of links: 246,935 Traffic assignment is stochastic, multiclass, and user- Freight modes: Truck optimal equilibrium. Capacities are coded for 24-h. Delay for No. of commodity categories: 28 the equilibrium assignment is calculated with BPR curves. Production: Employment by industry group Transit assignment is also done. Consumption: IO, employment by industry group, population Post-processors have been provided for air pollution emis- Distribution: Fratar factoring freight flow database, OD sions and accident calculations and for traffic microsimula- table estimation to truck ground counts tion of small portions of the network. Mode split: Fixed shares Truck-type split: Fixed shares Sources for this case study were: Hunt and Abraham Assignment: Static equilibrium, multiclass (2003), Hunt et al. (2004a), HBA Specto Incorporated and Delay estimation: BPR curves Parsons Brinckerhoff Ohio (2005), Ohio's response to the Major data: TRANSEARCH, IO tables Peer Exchange questionnaire (2004), and Ohio's response to Time frame: Three years of development time the Synthesis questionnaire (2005). Computation time: 2.5 h In-house staff: 1 FTE CASE STUDY 4: VIRGINIA FREIGHT COMPONENT Virginia's freight component concept is illustrated in Fig- The Virginia freight component is designed to properly ac- ure 25. OD tonnages by trucks from the TRANSEARCH data- count for trucks on highways when loading passenger auto- base are converted to truck loads by the payload factors listed mobiles. The model combines trucks and automobiles within in Table 8, adopted from Texas. Daily tonnage was taken to be an equilibrium multiclass traffic assignment step that preloads 1/365th of yearly tonnage. An initial traffic assignment was trucks using all-or-nothing assignment. Truck OD tables are made. The truck OD table from the TRANSEARCH database

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44 TABLE 8 VIRGINIA PAYLOAD FACTORS FOR COMMODITIES Movement Type STCC Commodity Type Intrastate Interstate Through 1 Farm products 16.1 16.1 16.1 9 Fresh fish or marine products 12.6 12.6 12.6 10 Metallic ores 11.5 11.5 11.5 11 Coals 16.1 16.1 16.1 14 Nonmetallic ores 16.1 16.1 16.1 19 Ordinance or accessories 3.1 3.1 3.1 20 Food products 17.9 17.9 17.9 21 Tobacco products 9.7 16.4 16.8 22 Textile mill products 15.2 16.1 16.5 23 Apparel or related products 12.4 12.4 12.5 24 Lumber or wood products 21.1 21.0 21.1 25 Furniture or fixtures 11.3 11.3 11.4 26 Pulp, paper, allied products 18.6 18.5 18.6 27 Printed matter 13.8 13.6 13.9 28 Chemicals or allied products 16.9 16.9 16.9 29 Petroleum or coal products 21.6 21.6 21.6 30 Rubber or miscellaneous plastics 9.1 9.2 9.3 31 Leather or leather products 10.8 11.0 11.3 32 Clay, concrete, glass, or stone 14.4 14.3 14.4 33 Primary metal products 19.9 19.9 20.0 34 Fabricated metal products 14.3 14.3 14.3 35 Machinery 10.8 10.8 10.9 36 Electrical equipment 12.7 12.8 12.9 37 Transportation equipment 11.3 11.3 11.3 38 Instruments, photo, optical equip. 9.4 9.4 9.7 39 Misc. manufacturing products 14.2 14.4 14.8 40 Waste or scrap metals 16.0 16.0 16.0 50 Secondary traffic 16.1 16.1 16.1 Note: STCC = Standard Transportation Commodity Code. was found to substantially underestimate truck volumes be- total employment by zone was taken to be the measures of both cause of the missing commodities. Instead of attempting to trip productions and trip attractions. The TRANSEARCH model these missing commodities directly, Virginia adopted a commodities were assigned to the network and the differences method of correcting the TRANSEARCH data by comparing from ground counts were found. These differences were as- the assigned volumes to ground counts. sumed to consist of trucks carrying the missing commodities in the TRANSEARCH database. The resulting OD table form Virginia used a maximum likelihood method of OD table of the gravity expression was scaled so that, on average, the to- estimation from ground counts that was contained within their tal number of trucks was correct when assigned to the network. travel forecasting software package. This method required a This scaled table was adjusted to the difference between the "seed" OD table, as well as numerous truck ground counts. assignment and the counts. The seed OD table was created by a gravity expression, where Each commodity was forecasted individually by Fratar factoring its OD table. Each of the 28 commodity groups has been matched to a similar industry group for calculating changes in commodity production. Changes in production are directly proportional to changes in industrial employ- ment. For commodity consumption, a weighted combination of industry employment and final demand is used. The weights are derived from analysis of sales from the National InputOutput Tables, Direct Requirements Table. Final demand was forecasted in proportion to a weighted combi- nation of population and employment. Forecasts in employ- ment were provided for counties by Woods & Poole and modified by national productivity coefficients. County-level data were apportioned to TAZs according to employment totals. There were no special generators. FIGURE 21 Virginia's zone system, full extent. (text continues on page 47)

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45 FIGURE 22 Virginia's zone system, in and near state. FIGURE 23 Virginia's highway network, full extent.

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46 FIGURE 24 Virginia's highway network within state. Reebie2001 Data Rail Truck Air Water Commodity Commodity Commodity Commodity Flows Flows Flows Flows Truck Loading Factors by Commodity Type Truck OD Seeds (short distance) Reebie Truck Trips Initial Truck Network Local Truck Matrix Assignment Estimation Local Truck Trips Overall Truck Trips Network Assignment FIGURE 25 Major steps in Virginia's truck model.