Designing van-based mobile battery swapping and rebalancing services for dockless ebike-sharing systems based on the dueling double deep Q-network

• We propose a van-based services for battery swapping and rebalancing in ebike-sharing systems. • We utilize the Markov decision process to depict the ebike-sharing system with a platform player and a van driver player. • We apply the dueling double deep Q-network method which is an advanced reinforcement learning approach. • We numerically show that the proposed strategy could help increase the platform's profit and van drivers' earnings. Ebike-sharing (electric bicycle-sharing) systems are gaining popularity as ebikes provide riders with transportation convenience when people have limited accessibility to other travel modes. Compared to traditional bike-sharing systems, ebike-sharing systems are more complicated as the platform needs to handle battery recharging issues as well as the imbalance between supply and demand. However, previous studies have not discussed how to address the two issues effectively. In this paper, we consider a dockless ebike-sharing system with removable ebike batteries and introduce vans to such a system to solve recharging and rebalancing problems simultaneously. In other words, during the operational horizon, van drivers can choose among rebalancing tasks, battery swapping tasks, and half-rebalancing-half-swapping tasks. This paper utilizes the Markov decision process to depict the highly dynamic ebike-sharing system with a platform player (agent) and a representative van driver player (agent). In such a system, van drivers choose their tasks to maximize their income, and the platform allocates spatiotemporal subsidies with predefined subsidy amounts to incentivize van drivers and optimize the profit. To efficiently solve the dynamic optimization problem with mixed agents, we apply the dueling double deep Q-network method which is an advanced reinforcement learning approach. We conduct numerical studies based on a real-world dataset in New York City, and evaluate the performance of the proposed operational services under different schemes. Our results show that the proposed van-based services for battery swapping and rebalancing could help increase the platform's profit and van drivers' earnings, and improve system performance. Additionally, it is also proved that the platform, van drivers, and the overall ebike-sharing system all benefit from spatiotemporal subsidies.