AGENT-BASED ROUTE CHOICE MODEL WITH LEARNING AND EXCHANGE OF INFORMATION

Route choice is one of the most important choice dimensions in travel demand modeling. Research emerging from fields such as road pricing and advanced traveler Information systems (ATIS) requires travel demand models that are able to consider travelers with distinct attributes (value of time (VOT), willingness to pay, travel budgets, etc.) and behavioral preferences (e.g. willingness to switch routes with potential savings) in a differentiated market. Traditional trip-based models have difficulty in dealing with the aforementioned heterogeneity. Moreover, the role of spatial information has not been fully addressed in existing models. This paper proposes to explicitly model the formation and spreading of spatial knowledge among travelers, following cognitive map theory. An Agent-based Route Choice (ARC) model was developed to track choices of each individual decision-maker in a road network over time and map individual choices into macroscopic flow pattern.