Constrained Distance-Based Congestion Pricing in Multi-Modal Urban Networks

Abstract

While previous studies have examined the fairness and equity implications of alternative pricing schemes, designing an equitable congestion pricing methodology for complex multi-modal traffic systems that explicitly accounts for modal interactions and traveller heterogeneity remains a significant challenge. This study proposes a constrained, data-driven congestion pricing framework based on reinforcement learning (RL) to maximise network outflow while maintaining equity. A trip-based three-dimensional Macroscopic Fundamental Diagram (3D-MFD) simulation model is developed to serve as the RL environment, capturing multi-modal interactions among cars, buses, and walking. Traveller heterogeneity is incorporated through variations in origin–destination pairs, trip lengths, departure times, and individual values of time (VoT). Results indicate that the RL agent significantly mitigate congestion and reduce total travel time by up to 50%, while distributing cost burdens more equitably among travellers.