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14 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 K to 12, and preschool is made in the lower-level models explanatory value of the models. That information, cou- on the basis of the age and enrollment type of the partic- pled with the fact that most survey respondents are reluc- ular person in the sample. tant to provide much detail about their in-home activities, explains why none of the other models distin- guishes between types of in-home activities. Some of the NUMBER OF IN-HOME ACTIVITY PURPOSES models predict which people work primarily at home: that provides some substitution between in-home and In the Portland models, in-home activities are distin- out-of-home work. It does not, however, handle the phe- guished on three purposes (workschool, maintenance, nomenon of part-time telecommuting, which is the focus and discretionary), but this distinction is made only for of some transportation demand management policies. the primary activity of the day and is predicted only As a result, there is some interest in predicting work at when the person has no out-of-home activities. This dis- home as a separate activity type in the Bay Area model if tinction did not appear to add substantially to the the data will support it. Brief Description of CEMDAP Comprehensive Econometric Microsimulator for Daily Activity-Travel Patterns Chandra R. Bhat, University of Texas at Austin T he synthetic population generator (SPG) in Compre- hensive Econometric Microsimulator for Daily Activity-Travel Patterns (CEMDAP) uses census lated on the basis of a suite of models estimated by using PUMS and other sources of data. tractblock groupblock level summary tables as con- trol totals for synthesizing households and individuals SCHOOL AND WORK LOCATIONS from the 2000 5% Public Use Microdata Samples (PUMS) data. Some of the summary tables contain the The "usual" school and work locations are modeled distribution of a single variable, while other tables at the "top" level. Every work location zone is con- describe the joint distribution of multiple variables. sidered as an alternative in the choice set (i.e., to avoid These tables are used to construct a full multiway dis- large prediction bias, the work location model is not tribution by using a recursive merge procedure and the applied to just a sample of zones). However, home iterative proportional-fitting procedure. The SPG location zone, adjacent location zone, and central allows the user to specify the choice of control vari- business district zones are given higher preference in ables from a wide range of census variables at run time. the utility functions. In addition to modeling the Currently, for the DallasFort Worth (DFW) appli- "fixed" school and work locations at the top level, cation, four household-level variables and three work-related activity (business meeting, etc.) destina- individual-level variables, are used as controls. The tion choice models are implemented at the activity household-level variables are household type (six cat- stop level. egories), household size (seven categories), presence of children (two categories), and age of householder (two categories). The individual-level variables are gender OUT-OF-HOME AND IN-HOME ACTIVITIES (two categories), race (seven categories), and age (10 categories). All other variables in the PUMS data that CEMDAP application for the DFW area includes 11 are required for the activity travel pattern simulator, out-of-home activity types for adults (work, school, but not controlled during the population synthesis, work-related, drop-off at school, pickup from school, are not directly used. Instead, their values are simu- joint discretionary activity with children, grocery