University of California Transportation Center

One of the major foci in transport research is the identification of the temporal-spatial decision making structure embedded in activity scheduling and its linkage to actual activity/travel execution. The latter part of the research in question has not been explored explicitly in real life situations due to the lack of effective data collection means. This research presented a real-time activity scheduling, activity/travel survey system that incorporates the extraction of activity scheduling and activity implementation information within one unified data collection framework, under the assumption that in reality activity scheduling and execution are an integral and dynamic process that continuously evolves over multiple time horizons. During a pilot study of 20 subjects, the system demonstrated its ability in successfully capturing the survey participants’ activity scheduling process and relevant activity execution into an organized dataset in the real-life, mobile environment. With the uniqueness of these empirical data in their full coverage of travel modes, site-to-site travel trace and concurrent tracing of activity scheduling and execution, they were used for explicitly exploring traveler’s routing choices, scheduling pattern and modeling the linkages (congruence and deviation relations) between the actual activity implementation and activity schedules with respect to the participants’ social-demographic characteristics and recorded schedule/activity/travel attributes. Using a binary logistic modeling approach, the research revealed that people’s routing behavior varies with gender, travel distance, different travel modes and activity categories. By exploratory statistics and missing value analysis, the research showed that activity scheduling behavior does not apply to activity categories in an equivalent way. Finally, the activity participation choice and start time decision making as revealed in the collected dataset were coalesced into a two-stage decision paradigm and modeled via nested logistic modeling and a multinomial logistic modeling approach. The influencing factors on the linkage between activity scheduling and execution were revealed. The multinomial modeling results showed the quantitative measures of the effects of factor changes on activity start time choices.