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40 Travel time to nearest city center, particularly their emphasis on forecasting the spatial distri- Travel time to nearest airport, and bution of economic activity and land use. The Oregon Travel time to nearest major arterial. statewide model was recently described in the draft report for NCHRP Project 8-43. This section will emphasize the The regression model was calibrated by comparing the re- economic activity portions of Ohio's model, how the eco- gression of year 2000 population against 1990 socioeco- nomic activity portions integrate with other components, and nomic data with actual 2000 data. Then the model was used the microsimulation of activity-based trip patterns. Both the to produce population changes from year 2000 to 2030 in Ohio and Oregon models have the philosophy that travel is a terms of changes in zonal shares of county totals. "Only half consequence of human and economic activities; therefore, the modeled shift in zonal share of county population pre- the spatial organization of the state's economy is first mod- dicted by the regression model was applied to bias the final eled comprehensively and aggregately. Activities result in allocation towards the existing distribution of population trip making, which is then modeled in a disaggregated fash- given the inherent uncertainty in land use forecasting." ion, both in space and in time. The same approach for forecasting population was used to The scope of the Ohio model was decided on after a study forecast zonal employment. The independent variables in- of stakeholder needs. The model was designed to address cluded in the accessibility regression model were: three principal issues: economic development, congestion mitigation, and truck flows. Total population, Total households, Ohio deliberately staged its model development by first Population density, creating an "interim model," which is currently operational. Of Aggregate personal income, greater interest here is the "advanced" model, which is sched- Presence of airport, uled to be operational soon. The overall structure of Ohio's Presence of hospital, model may be seen in Figure 18 as being made up of several University enrollment, submodels. The submodels that seem most unusual in a Travel time to nearest city center, statewide context are the Land Development submodel and the Travel time to nearest major arterial, Activity Allocation submodel. These submodels are similar to Travel time to nearest freeway, aggregate land use models that have been implemented in Accessibility to intermodal freight facilities, some metropolitan areas. Because these submodels deal with Accessibility to households, both household and industry location simultaneously, there is Accessibility to population, an intrinsic linkage between the passenger and freight compo- Accessibility to university enrollment, and nents. The other submodels, some nontraditional, replace sim- Accessibility to wealth (by place of residence). ilar functions of a four-step model or are post-processors. "Only one-third of the modeled shift in zonal share of county Ohio Statewide Model Summary employment predicted by the regression model was applied State population: 11.4 million to bias the final allocation towards the existing distribution State area: 44,828 square miles of employment given the inherent uncertainty in land use Gross state product: $403 billion forecasting and the r-squared for the regression model." No. of zones: 5,103 External zone structure: Halo, states ISTDM has been used for statewide system planning, cor- Internal zone structure: TAZs, grid cells ridor planning, bypass studies, economic development stud- No. of highway links: 250,000 ies, air quality analysis, project prioritization, inputs to eco- Freight modes: Truck nomic modeling, and long-term investment studies. No. of commodities: 28 categories No. of industries: 15 categories Sources for this case study were: Bernardin, Lochmueller Household composition: Microsimulation & Associates, Inc., and Cambridge Systematics, Inc. (2004), Tour formation: Microsimulation Indiana response to Peer Exchange questionnaire, Longboat Passenger mode split: Microsimulation Key, Florida (Sep. 2004), and Indiana response to Synthesis Truck vehicle split: Microsimulation questionnaire (Feb. 2005). Assignment: Static equilibrium, multiclass Delay estimation: BPR curves CASE STUDY 3: OHIO COMBINED PASSENGER Major data: Household and business surveys, AND FREIGHT COMPONENTS TRANSEARCH, CTPP, ES-202, County Business Patterns, assessor land values Both Ohio and Oregon have statewide models that differ sig- Time frame: Eight years of development time nificantly from the typical four-step UTP model seen else- Computation time: Not determined where. These two statewide models share many similarities, In-house staff: 1 FTE

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41 Interregional Aggregate Economic Model Demographic Model Land Activity Development Allocation Model Model Employment Disaggregate Spatial Household Disaggregation Synthesis Model Model Times & Costs Commercial Personal Travel Travel Tour Tour Models Models Travel Demand Models Network Assignment Models Sub-Area Traffic Air Quality & Micro-Simulation Accident Models Model FIGURE 18 Overall structure of Ohio's statewide travel forecasting model. (Source: Hunt et al. 2004a.) To keep computations reasonable, Ohio adopted three nested-zone structures. The economic activity portions of the model use approximately 700 "activity model zones," which are each made up of whole TAZs. The 5,103 TAZs are com- posed of many grid cells for (1) maintenance of land use and demographic data and (2) disaggregation of traffic assign- ment. Ohio's grid cells are also used for providing locations of origins and destinations for those steps that microsimulate freight and person travel. Small TAZs cover all of Ohio and a halo of approximately 50 mi into surrounding states. Larger zones extend to the rest of the 48 contiguous states. Ohio's TAZs are shown in Figure 19 and the network within Ohio is shown in Figure 20. An extensive data collection effort was needed to support the goals of the model. The major data sources were: Household travel surveys, Household long distance travel survey, GPS-based travel survey, Business establishment survey, National Transport Networks, Ohio DOT Roadway Information Database, U.S. Census, ES-202, FIGURE 19 Ohio's traffic analysis zone structure. (Source: TRANSEARCH, Ohio's response to the synthesis questionnaire 2005.)

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42 ducers and consumers within zones in such a manner as to cre- ate a supply/demand equilibrium throughout the state. The supply/demand equilibrium is maintained by adjusting prices of commodities, services, labor, and land (or floor space). The allocations of goods, services, and labor are undertaken using logit and nested-logit expressions, where utility functions con- tain (1) the cost of travel or transport, (2) the size of zone, and (3) the price of the commodity. The allocations depend on what is already present or has been allocated in a previous time period. Industry is organized into the following categories: Agriculture, forestry, and fishing Primary metals Light industry Heavy industry Transportation equipment Wholesale Retail Hotel and accommodation Construction Health care Transportation handling Other services FIGURE 20 Ohio's network within state. (Source: Ohio's K-12 education response to the Peer Exchange questionnaire 2004.) Higher education Government and other. Department of Natural Resources land use data, County assessor land value data, Households are divided into six categories by income. The model is stepped through a sequence of 5-year time pe- Ohio DOT traffic counts, riods until the planning year has been reached. The Land De- IMPLAN (IO model), velopment submodel determines how categories of land are Roadside surveys, developed using a series of logit expressions. Land uses are: Travel time studies, CTPP outside Ohio, Residential, County Business Patterns, Commercial, BEA Regional Economic Information System program, Light industrial, College and university enrollments, and Heavy industrial, County auditor data. Grade school, Post-secondary institutional, The household travel surveys were composed of new sur- Health institutional, veys in small and medium MPOs, in addition to existing sur- Agricultural, veys in larger MPOs. These household surveys combined to Forest and protected resource, and yield approximately 25,000 responses. A GPS survey was si- Vacant. multaneously conducted to monitor underreporting of trips. The household long distance survey elicited information about Ohio is also using the capability of PECAS to separate land trips of greater than 50 mi from 2,000 households. Roadside uses in serviced and unserviced categories. surveys were taken at approximately 700 locations. Approxi- mately 800 business establishments were surveyed to provide The traditional generation, distribution, and mode split information about services and commodities that are not in- steps for personal travel are replaced by microsimulation of cluded in the TRANSEARCH database. NHTS was not used. household travel decisions. Separate submodels are provided for household synthesis; short-distance, home-based person The activity allocation and land use submodels were based tours; long distance, home-based person tours; commercial, on PECAS (Production Exchange and Consumption Alloca- work-based person tours; and visitor person tours. tion System) (Hunt and Abraham 2003), a land use model de- veloped at the University of Calgary. In a manner similar to a Household synthesis--This submodel uses a Monte compact IO table, PECAS tracks the flow of goods and ser- Carlo process to create a list of households by TAZ. vices between industries and final demand (households), but Each household has attributes that are required by other does so spatially as well as monetarily. The model locates pro- submodels. The Monte Carlo probabilities are based on