From Models to Mobility Systems: A Survey of Multimodal Dynamic Traffic Assignment

Abstract Multimodal dynamic traffic assignment models are widely used to estimate traffic flow patterns and support decision-making in transportation supply, infrastructure planning, and traffic management. As transportation systems shift toward multimodal, service-oriented, and data-driven approaches, existing models face growing challenges in realism and scalability. This survey reviews recent advances in multimodal dynamic traffic assignment, with a focus on modeling heterogeneous traffic, incorporating multiple transportation modes, and capturing interactions between different vehicle types. The evolution of dynamic traffic assignment from single- to multimodal frameworks is reviewed, alongside recent modeling and data-driven approaches, including deep reinforcement learning for adaptive, agent-based routing. Key research gaps are identified, and a future research agenda is outlined. Priority directions include improving scalability for large-scale networks, incorporating time-varying and data-driven demand representations, and integrating real-time traffic information to bridge the simulation-to-reality gap.