Intermodal Journey Planning to Transportation Hubs in a Microscopic Environment: A Multi-Objective Multi-Agent Reinforcement Learning Optimization Framework

Intermodal journey planning remains a challenge in intelligent transportation systems, particularly when account- ing for heterogeneous passenger preferences and the integration into smart cities. Traditional planning approaches often fail to capture dynamic traffic conditions and the passenger-centric view required for future transportation systems. This study proposes a Multi-Objective Multi-Agent Reinforcement Learn- ing (MOMARL) framework for individual intermodal journey planning across multiple modes. Two microscopic traffic models were developed in Simulation of Urban Mobility (SUMO), cre- ating simulation environments in which passengers plan their journeys to arrive on time at transportation hubs. One simpler model for the verification of the framework and a calibrated model reflecting the dynamics of a real city. The transportation networks were modeled as multilayered graphs. Since each passenger has different preferences and access to transport modes, their individual cost-minimal paths are formulated as a multi-objective optimization (MOO) problem. From this, the scalarized reward signals used in the MOMARL framework are derived. Simulation results show that the proposed approach enables agents to generate feasible intermodal routes in a microscopic traffic environment, demonstrating the use of MOMARL for passenger-centric coordination in multimodal transport systems. Application to the calibrated model of Ingolstadt posed challenges regarding simulation complexity, highlighting the need to expand research in methods that allow the systematic reduction of model fidelity and granularity while retaining realistic dynamics.