拼车系统时空分层自适应调度
Spatio-Temporal Hierarchical Adaptive Dispatching for Ridesharing Systems
摘要：
如今，拼车已成为在线乘车平台提供的最受欢迎的服务之一（例如，Uber和滴滴出行）。现有的拼车平台采用的策略是以统一的时间间隔在整个城市调度订单。然而，在现实世界的拼车系统中不均匀的时空顺序分布表明，这种方法在实践中是次优的。因此，在本文中，我们采用自适应的调度间隔，以提高平台的利润，在保证最大的乘客等待时间。具体来说，我们提出了一个层次化的方法，它产生集群的地理区域适合共享相同的调度间隔，然后在每个空间集群内选择适当的时间实例进行订单调度的在线决策。从技术上讲，我们证明了不可能设计恒定的竞争比算法的在线自适应区间问题，并提出在线算法下的部分甚至零未来的顺序知识，显着提高平台的利润超过现有的方法。我们对一个大规模的拼车订单数据集进行了广泛的实验，该数据集包含了滴滴出行在2018年10月1日至10月31日期间在中国北京收到的所有超过350万个拼车订单。实验结果表明，我们提出的算法优于现有的方法。
英文摘要：
Nowadays, ridesharing has become one of the most popular services offered by online ride-hailing platforms (e.g., Uber and Didi Chuxing). Existing ridesharing platforms adopt the strategy that dispatches orders over the entire city at a uniform time interval. However, the uneven spatio-temporal order distributions in real-world ridesharing systems indicate that such an approach is suboptimal in practice. Thus, in this paper, we exploit adaptive dispatching intervals to boost the platform's profit under a guarantee of the maximum passenger waiting time. Specifically, we propose a hierarchical approach, which generates clusters of geographical areas suitable to share the same dispatching intervals, and then makes online decisions of selecting the appropriate time instances for order dispatch within each spatial cluster. Technically, we prove the impossibility of designing constant-competitive-ratio algorithms for the online adaptive interval problem, and propose online algorithms under partial or even zero future order knowledge that significantly improve the platform's profit over existing approaches. We conduct extensive experiments with a large-scale ridesharing order dataset, which contains all of the over 3.5 million ridesharing orders in Beijing, China, received by Didi Chuxing from October 1st to October 31st, 2018. The experimental results demonstrate that our proposed algorithms outperform existing approaches.
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。
