重新平衡再平衡器：按需移动系统中的车辆和驾驶员最优路径
Rebalancing the Rebalancers: Optimally Routing Vehicles and Drivers in Mobility-on-Demand Systems
摘要：
在本文中，我们研究的再平衡策略的流动性按需城市交通系统混合客户驱动的车辆与出租车服务。在我们的系统中，客户到达许多指定车站之一，并通过自己驾驶或由雇用的司机驾驶被运送到任何其他指定车站。该系统允许单程旅行，因此客户不必返回其原籍。当一些起点和目的地比其他站更受欢迎时，车辆将变得不平衡，在一些站积累，在其他站耗尽。这个问题是通过采用再平衡驾驶员来将车辆从受欢迎的目的地驾驶到不受欢迎的目的地来解决的。然而，通过这种方法，再平衡驱动程序本身变得不平衡，我们需要通过让他们与客户一起返回热门目的地来“重新平衡再平衡器”。因此，在本文中，我们研究如何最佳路线的再平衡车辆和司机，使稳定性（在等待客户的数量的有界性方面）得到保证，同时最大限度地减少在网络中行驶的再平衡车辆的数量和所需的再平衡司机的数量;令人惊讶的是，这两个目标是一致的，可以找到最佳的再平衡策略，通过求解两个解耦的线性规划。利用我们的分析，我们确定了确保系统稳定性所需的最少驾驶员数量和最少车辆数量。有趣的是，我们的模拟表明，在欧几里得网络拓扑中，一个人需要的驾驶员数量是车辆的1/3到1/4。
英文摘要：
In this paper we study rebalancing strategies for a mobility-on-demand urban transportation system blending customer-driven vehicles with a taxi service. In our system, a customer arrives at one of many designated stations and is transported to any other designated station, either by driving themselves, or by being driven by an employed driver. The system allows for one-way trips, so that customers do not have to return to their origin. When some origins and destinations are more popular than others, vehicles will become unbalanced, accumulating at some stations and becoming depleted at others. This problem is addressed by employing rebalancing drivers to drive vehicles from the popular destinations to the unpopular destinations. However, with this approach the rebalancing drivers themselves become unbalanced, and we need to "rebalance the rebalancers" by letting them travel back to the popular destinations with a customer. Accordingly, in this paper we study how to optimally route the rebalancing vehicles and drivers so that stability (in terms of boundedness of the number of waiting customers) is ensured while minimizing the number of rebalancing vehicles traveling in the network and the number of rebalancing drivers needed; surprisingly, these two objectives are aligned, and one can find the optimal rebalancing strategy by solving two decoupled linear programs. Leveraging our analysis, we determine the minimum number of drivers and minimum number of vehicles needed to ensure stability in the system. Interestingly, our simulations suggest that, in Euclidean network topologies, one would need between 1/3 and 1/4 as many drivers as vehicles.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
