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181 Hardware Requirements and Running Time for the Mid- Ohio Regional Planning Commission Travel Forecasting Model Rebekah S. Anderson, Ohio Department of Transportation Zhuojun Jiang, Mid- Ohio Regional Planning Commission Chandra Parasa, Mid- Ohio Regional Planning Commission In October 2001, the Mid- Ohio Regional PlanningCommission (MORPC) contracted with PB Consult todevelop a set of regional travel forecasting models. The new model is a disaggregate tour- based model applied with the microsimulation of each individual household, person, or tour. The new modeling system was completed in late 2004 and refined throughout 2005. The new model is being used by MORPC for conformity analysis, transit alterna- tive analysis, and for highway Major Investment Study projects in the Columbus region. The model area is divided into 1,805 internal and 72 external zones and includes Franklin, Delaware, and Licking counties, and parts of Fairfield, Pickaway, Madi- son, and Union counties. The primary inputs to the model are transportation networks and zonal data, where each zone has the standard socioeconomic char- acteristics that one would normally find in a four- step model. The main differences from the prior four- step model are that the new model accounts for travel at the tour level, as opposed to the trip level, and for each indi- vidual household and person, as opposed to zonal and market segment aggregates. MODEL FORMULATION The forecasting model consists of nine separate linked models and other network processing steps. The nine models are 1. Population synthesis: A synthesized list of all households and population for the entire area is gener- ated, consistent with the household and workforce vari- ables in the zonal data. The output from the population synthesis model is a file with a record for every person in the area containing various attributes attributed to that synthesized person. 2. Auto ownership: The number of vehicles available for each household is simulated. 3. Daily activity pattern: The daily activity pattern for each person and the number of mandatory tours each person with a mandatory activity pattern makes during the day are simulated. 4. Joint tour generation: Generation of tours under- taken by members of the same household. 5. Individual nonmandatory tour generation; 6. Tour destination choice: Logit- based choice model (applied with Models 7 and 8). 7. Time- of- day choice: Logit- based choice model (applied with Models 6 and 8). 8. Tour mode choice: Logit- based choice model (applied with Models 6 and 7). 9. Stops and trip mode choice: This model determines if any stops are made on either the outbound (from home) or inbound leg of the tour and the location of those stops. The core choice models (1 through 9 as described above) are applied in a disaggregate manner. Instead of applying aggregate fractional probabilities to estimate the number of trips, the new model is applied with the microsimulation of each individual household, person, or tour, mostly using Monte Carlo realization of each possibility estimated by the models, with use of a ran-