An agent-based travel demand model is developed in which travel demand emerges 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, Illinois, sketch transportation network with nearly 1,000 trip generators and sinks, and possible calibration procedures are discussed. 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 studies on individual travel behaviors. Future research directions are identified, as is the relationship between the agent-based and activity-based approaches for travel forecasting.