An analyzable agent-based framework for modeling day-to-day route choice

This paper proposes an analyzable agent-based route choice modeling framework with good theoretical properties. This modeling framework allows heterogeneous individual learning rules and learning rates. As long as travelers' route choice behaviors conform to the framework, even though their learning rules and learning rates are heterogeneous, the network flows can be proven to be with asymptotically stable fixed points. An approximation for network flow distribution is proposed from the perspective of the stochastic process. Some phenomena observed in laboratory experiments are well captured by the agent-based framework. Many existing network-level day-to-day dynamic models can be regarded as special cases of the framework by setting the concrete learning rules and learning rates of the agents. Numerical simulations are used to show model properties. This study can deepen our understanding of the behavioral mechanism of individual-level day-to-day route choice and network-level day-to-day traffic flow dynamics.