Abstract
This paper describes a method for efficiently storing, modeling, and processing an individual’s travel history collected with a global positioning system (GPS) enabled device. The technique uses a general framework called Hybrid Dynamic Mixed Networks (HDMNs), which are Hybrid Dynamic Bayesian Networks that allow representation of discrete deterministic information in the form of constraints. The paper uses this framework to model a person’s travel activity over time and to infer likely destinations and routes given information about the current trip. The paper also presents a preliminary empirical evaluation demonstrating the effectiveness of the modeling framework and algorithms using three variants of the activity model. This research will improve the quality of activity surveys based on electronic data collection methods, as well as improve the usefulness and effectiveness of in-vehicle and hand-held devices for daily activity planning and rescheduling.