Towards Accessible Shared Autonomous Electric Mobility With Dynamic Deadlines

Shared autonomous electric mobility has attracted significant interest in recent years due to its potential to save energy consumption, enhance mobility accessibility, reduce air pollution, mitigate traffic congestion, etc. Although providing convenient, low-cost, and environmentally-friendly mobility, there are still some roadblocks to achieve efficient shared autonomous electric mobility, e.g., how to enable the accessibility of shared autonomous electric vehicles in time. To overcome these roadblocks, in this article, we design Safari, an efficient Shared Autonomous electric vehicle Fleet mAnagement system with joint Repositioning and chargIng based on dynamic deadlines to improve both user experience and operating profits. Our Safari considers not only the highly dynamic user demand for vehicle repositioning (i.e., where to relocate) but also many practical factors like the time-varying charging pricing for charging scheduling (i.e., where to charge). To perform the two tasks efficiently, in Safari, we design a dynamic deadline-based deep reinforcement learning algorithm, which generates dynamic deadlines via usage prediction combined with an error compensation mechanism to adaptively learn the optimal decisions for satisfying highly dynamic and unbalanced user demand in real time. More importantly, we implement and evaluate the Safari system with 10-month real-world shared electric vehicle data, and the extensive experimental results show that our Safari achieves 100% of accessibility and effectively reduces 26.2% of charging costs and reduces 31.8% of vehicle movements for energy saving with a small runtime overhead at the same time. Furthermore, the results also show Safari has a great potential to achieve efficient and accessible shared autonomous electric mobility during its long-term expansion and evolution process.