Deep Reinforcement Learning of Simulated Students Multimodal Mobility Behavior: Application to the City of Toulouse

This study presents a Deep Reinforcement Learning (DRL) approach to address the multimodal mobility behavior of daily commuters, focusing specifically on students’ home-university multimodal trips. The proposed mesoscopic model addresses key limitations of recent macro and microscopic models by balancing individual mobility preferences with significant group-level student factors. At its core, the model employs a Proximal Policy Optimization (PPO)-based agent that learns to match student navigation behavior in a multimodal transportation network, considering his group mobility factors such as vehicle ownership and origin-destination regions. Experiments conducted on a SUMO (Simulation of Urban MObility) simulated dataset of a university students’ trips in the Toulouse metropolitan area demonstrate the model’s performance in both unimodal and multimodal transportation scenarios. The resulting policy offers potential applications in predicting future multimodal mobility behavior, optimizing resource allocation for communities with regular travel needs, and developing more efficient and environment friendly urban transportation systems.