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16 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 of causality. A similar model is planned for the Bay Area and participate in the same activity while at that location. system. In the lower-level models, such as mode and destination choice, it is best to model such cases as a single joint deci- sion rather than as independent decisions made by differ- JOINT ACTIVITIES LINKED EXPLICITLY ACROSS ent people. The Columbus and Atlanta model systems HOUSEHOLD MEMBERS include models of household joint-activity generation and participation. The application of the Columbus model Joint activities are those in which two or more household has shown that predicting joint travel can have significant members travel together to and from an activity location implications for mode choice, so this type of model has start and end times is currently implemented at 30-min BRIEF DESCRIPTION OF CEMDAP periods, but it can be implemented at any finer time (continued from page 15) intervals. The school start and end times are predicted in continuous time by using hazard-based duration mod- els. The departure time for all other activities is also scheduled in continuous time. Available time windows TIME OF DAY, MODE, AND DESTINATION are used in both the worker and nonworker scheduling CHOICE MODELING SEQUENCE models at the subpattern, tour, and stop levels. The work and school locations are predicted at the top level, while the work start and end times and work ACCESSIBILITY MEASURES commute mode choice are modeled in sequence at the travel day level. The school start and end times are Measures of accessibility from the home zone are used also predicted before the to- and from-school mode in activity generation models. The accessibility of a choice models. For all other activities, the tour mode zone to another zone is calculated as the ratio of an is predicted at the tour level, followed by predicting attraction measure in the other zone relative to an the time of day and then the destination choice at the impedance measure between the two zones (which is a activity stop level. The departure time is derived from function of travel times and costs). The parameters of the predicted home staywork stay duration before these attraction and impedance functions were prede- each tour, activity duration at--and travel time to-- termined from a destination choice model. The overall each stop. accessibility of a zone is then calculated as the average of the zone-to-zone accessibility measures. NETWORK FEATURES, LEVEL OF SERVICE VARIABLES, AND MODELED TIME PERIODS REFERENCES CEMDAP can be used with any level of spatial reso- Bhat, C. R., J. Y. Guo, S. Srinivasan, and A. Sivakumar. lution of zones and any number of time periods for Comprehensive Econometric Microsimulator for Daily level-of-service (LOS) variables. The DFW application Activity-Travel Patterns. In Transportation Research uses a system of 4,784 traffic analysis zones for spatial Record: Journal of the Transportation Research Board, representation and five time-of-day periods (a.m. off No. 1894, Transportation Research Board of the peak, a.m. peak, midday off peak, p.m. peak, and p.m. National Academies, Washington, D.C., 2004, pp. off peak) for LOS characteristics. No finer spatial 5766. units are used for land use variables. Guo, J. Y., S. Srinivasan, N. Eluru, A. Pinjari, R. Copper- The effect of time-varying LOS characteristics is man, and C. R. Bhat. Activity-Based Travel-Demand considered directly in work scheduling and indirectly Analysis for Metropolitan Areas in Texas: CEMSELTS in activity generation models through accessibility Model Estimations and Prediction Procedures, 4,874 measures. The LOS attributes are also used in models Zone System CEMDAP Model Estimations and Proce- of commute mode choice and nonwork-activity stop- dures, and the SPG Software Details. Report 4080-7. location choice. Texas Department of Transportation, Oct. 2005. Any time-of-day feature in CEMDAP is predicted in www.ce.utexas.edu/prof/ bhat/REPORTS/4080_7.pdf. continuous time. The simultaneous prediction of work