Modeling Activity Selection and Scheduling Behavior of Population Cohorts within an Activity-Based Travel Demand Model System

Abstract

Understanding the time-use activity patterns of population cohorts in the region will contribute greatly to modeling spatio-temporal urban transportation demand models. The research detailed in this dissertation focuses on the development of the Scheduler for Activities, Locations, and Travel (SALT) disaggregated travel demand microsimulation model. The SALT modeling framework comprises a series of micro-behavioral modules that employ behaviorally realistic econometric, advanced machine learning, and data mining techniques to construct the 24-hour activity schedule and the corresponding travel linked with activities accomplished by individuals. A state-of-art three-dimensional, four-stage pattern recognition model is developed to identify population clusters with homogeneous time-use daily activity patterns, and to derive a representative set of activity patterns in each cluster. Each identified population cluster provides essential information related to temporal, spatial, and socio-demographic characteristics of individuals and activities, which are crucial for modeling the successive micro-behavioral modules of the SALT model. The representative behavior within each cluster is then used as an information guide for agent-based modeling. A new agent-based inference model is developed to predict various facets of the daily activity agenda, such as stop number, activity type, and activity sequential arrangement. In the next phase, temporal attributes of each activity in the agenda are predicted and the 24-hour activity schedule of all individuals is formed through a heuristic decision rule-based algorithm. Finally, a population synthesizer procedure is developed in order to implement the SALT system for the entire region. In addition, this study models the daily time-use activity patterns and estimated emission factors for university commuters, considered as a special trip generator in regional travel demand models. The data used for the analysis is from the large Halifax Space-Time Activity Research (STAR) household survey, which provides GPS-validated time-diary data for 2,778 person-days. Results show that the SALT scheduling model is able to assemble the traveler’s schedule with an average 82% accuracy in the 24-hour period. The proposed simulation modeling framework is useful for urban and transport modelers to advance transportation demand management for different segments of the urban population, as well as to analyze environmental mitigation and transport policy scenarios.