Assessing how ride-hailing rebalancing strategies improve the resilience of multi-modal transportation systems

The global ride-hailing (RH) industry plays an essential role in multi-modal transportation systems by improving user mobility, particularly as first- and last-mile solutions. However, the flexibility of on-demand mobility services can lead to local supply–demand imbalances. While many RH rebalancing studies focus on nominal scenarios with regular demand patterns, it is crucial to consider disruptions – such as train line interruptions – that negatively impact operational efficiency, resulting in longer travel times, higher costs, increased transfers, and service delays. This study examines how RH rebalancing strategies can strengthen the resilience of multi-modal transportation systems against such disruptions. We incorporate RH services into systems where users choose and transfer transportation modes based on their preferences, accounting for uncertainties in demand predictions that reflect discrepancies between forecasts and actual conditions. To address the stochastic supply–demand dynamics in large-scale networks, we propose a multi-agent reinforcement learning (MARL) strategy, specifically utilizing a multi-agent deep deterministic policy gradient (MADDPG) approach. The proposed framework is particularly well-suited for this problem due to its ability to handle continuous action spaces, which are prevalent in real-world transportation systems, and its capacity to enable effective coordination among multiple agents operating in dynamic and decentralized environments. Through a 900 km 2 multi-modal traffic simulation, we evaluate the proposed model’s performance against four existing RH rebalancing strategies, focusing on its ability to enhance system resilience. The results demonstrate significant improvements in key performance indicators, including user waiting time, resilience metrics, total travel time, and travel distance. • The resilience of transportation systems is assessed under supply-side disruptions on train lines. • The systems include trains, buses, metro, ride-hailing (RH) vehicles, and background traffic. • A reinforcement learning-based RH rebalancing strategy is proposed to manage disruptions. • The results demonstrate improvements in key performance indicators related to resilience metrics.