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26 I N N O VAT I O N S I N T R AV E L D E M A N D M O D E L I N G , V O L U M E 2 research project on time-of-day models that included a MODEL VALIDATION case study of a new time-of-day model (including peak spreading) for SF-CHAMP. Plans call for this new time- Travel behavior was validated by comparing travel data of-day model to be incorporated into the model. in a household travel survey to related travel data in the · The approach to trip assignment included a tradi- travel demand forecasting model. For the validation of tional aggregate assignment because there were too few the current 1998 SFCTA regional travel demand fore- resources in the project to implement a microsimulation casting model, the trip data in the 1990 Census and the assignment methodology. This approach has been used 1990 MTC household survey data were compared with in all other tour-based model applications in the United the same data in the model (2). States to date (except Transims). Nonetheless, it intro- The model components were calibrated individually duces aggregation bias and fails to take advantage of the by using various observed data sources. This effort disaggregate information on each traveler during route involved calibrating each model separately and then choice. reviewing highway and transit assignment results for · SF-CHAMP combines trip tables from the MTC each of the five periods to make additional adjustments regional trip-based model with trips generated from the in the model components. The adjustments were all San Francisco tour-based model. As a result, only San made to constants within the models; there were no Francisco residents are represented by the tour-based adjustments to model coefficients. Highlights of results model and its advantages. of the calibration are summarized below for each model component. These limitations were known at the outset and accepted as lesser priorities than the core objective of · Vehicle Availability: The vehicle availability model building a tour-based model. In some cases, these limita- was calibrated primarily on two key variables--number tions are already undergoing change in the update of the of workers per household and superdistrict--by using SF-CHAMP model. the 1990 Census as the primary source of observed data. There was one additional innovative aspect of the A second validation test was used to evaluate the total mode choice model: the inclusion of reliability and number of vehicles estimated by the vehicle availability crowding as explicit variables in the transit utility func- model compared with Department of Motor Vehicles tions; this aspect was tested and then not included in the estimates of auto registrations. These data were different final models. These variables were included in a stated- by 5%. Unfortunately, the 1990 MTC survey, which was preference telephone survey of 407 transit users in San used to estimate the model, contained different results Francisco. Logit analysis was used to estimate trade-offs for vehicle availability than the 1990 Census. Because between in-vehicle time, frequency of service, reliability the 1990 Census has a much larger sample size, these (defined as the percentage of days that the vehicle data were used to calibrate the vehicle availability model. arrives five or more minutes late), and crowding (low The results, therefore, have indirect effects on the market plenty of seats available; medium few seats available, segmentation of automobiles and workers that was car- but plenty of room to stand; high no seats available ried out in the mode split model. and standing room is crowded). It was estimated that · Full-Day Pattern Tour Models: The full-day pattern improving the percentage of vehicles arriving on time by tour models were calibrated by converting tours to trips 10% (e.g., once every 2 weeks) is equivalent to reducing and comparing these to the 1996 MTC survey expanded the typical wait time (half the headway) by 4 min for to match the 1998 population. The 1996 MTC survey commuters or 3 min for noncommuters. It was also esti- was used because the number of trips within San Fran- mated that improving the level of crowding from high cisco County was very low in the 1990 MTC survey due to low is equivalent to reducing the typical wait time by to underreporting of trips. The underreporting of trips is 5 min for commuters and 9 min for noncommuters. not consistent across time periods or across trip purposes, Thus, relative to commuters, noncommuters are, on conditions which may have influenced model estimation average, less sensitive to delay but more sensitive to that was based on the 1990 MTC survey. The differences crowding. In application, the reliability and crowding between trips by period were confirmed with initial was coded in the transit network by means of observed assignments by periods with the uncalibrated San Fran- system data collected by SFCTA. The trade-offs esti- cisco model revealing that the off-peak time periods were mated between these variables and wait time were significantly underestimated compared with traffic applied in performing transit assignment and found not counts. The vast majority of underreporting of trips in to be coincident with the observed boardings. As a the 1990 MTC survey was in other tours. result, these variables were not used in model · Destination (Primary-and-Intermediate Stop) application. Choice Models: The destination choice models were cal-