Enhancing Joint Behavior Modeling with Route Choice Using Adversarial Inverse Reinforcement Learning

Route choice modeling is an important issue in transportation planning. In recent years, the importance of comfortable urban spaces not only for vehicles but also for pedestrians has been increasing toward low-carbon society. Accordingly, efforts have been made to model the pedestrians' characteristic behaviors. However, most of the existing studies about route choice modeling are limited to the representation within a single traffic mode. In a typical urban street space, each traffic mode is interacting with each other in a complex manner. Describing multimodal interaction is necessary to efficiently allocate the limited road space and to maximize the value of the road space as a result of the network effect. However, there are two challenges to describing the interaction of multiple transportation modes in a route choice model: the computational cost of model estimation and the limited expressive power of the model. In this study, we construct a route choice model based on the AIRL method which can rapidly estimate the user equilibrium under multi-agent Markov game and can take into account the interaction among multiple traffic modes explicitly. The use of highly expressive machine learning models is also expected to lead the high reproducibility of traffic behavior.