An Agent-Based Approach to Travel Demand Modeling: An Exploratory Analysis

Abstract

The paper develops an agent-based travel demand model. In this model, travel demands emerge from the interactions of three types of agents in the transportation system: node, arc and traveler. Simple local rules of agent behaviors are shown to be capable of efficiently solving complicated transportation problems such as trip distribution and traffic assignment. A unique feature of the agent-based model is that it explicitly models the goal, knowledge, searching behavior, and learning ability of related agents. The proposed model distributes trips from origins to destinations in a disaggregate manner and does not require path enumeration or any standard shortest-path algorithm to assign traffic to the links. A sample 10-by-10 grid network is used to facilitate the presentation. The model is also applied to the Chicago sketch transportation network with nearly 1000 trip generators and sinks, followed by a discussion of possible calibration procedures. The agent-based modeling techniques provide a flexible travel forecasting framework that facilitates the prediction of important macroscopic travel patterns from microscopic agent behaviors, and hence encourages the studies on individual travel behaviors. Future research directions are identified, as are the relationship between the agent-based and activity-based approaches for travel forecasting.