Dynamic pricing for ride-hailing services considering relocation and mode choice
Dynamic pricing for ride-hailing services considering relocation and mode choice
复制标题
考虑搬迁和模式选择的网约车服务动态定价
DOI：
10.1109/mt-its49943.2021.9529301
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发表时间：
2021
期刊：
影响因子：
--
通讯作者：
J.-D.
中科院分区：
文献类型：
--
作者：
Iacobucci;R. and Schmoecker;J.-D.
海陵服务在世界各地的城市越来越受欢迎，而共享自动驾驶汽车的引入可能会宣布这一趋势。这些服务的一个问题是，在没有客户的情况下重新安置需要额外的旅行，这是满足不对称需求所必需的。减少空驶量的一个解决方案是采用考虑搬迁成本的优化定价策略。在这项工作中，我们提出了一个动态的定价优化，集成了在线预测搬迁。我们模拟了在具有共享自动驾驶汽车的密集分布的需求点之间运营的服务和通用替代模式之间的选择。该策略在一天中的每个时段为每个始发地/目的地区域找到最佳票价，以使运营商的净利润最大化。我们的方法灵活、快速且可扩展。我们证明了我们提出的方法与一个大型数据集的出租车行程从纽约市。模拟的结果表明，我们的定价策略始终增加运营商的收入，降低搬迁成本相比，最佳的情况下，但恒定的，距离依赖票价。在乘客等待时间很长的情况下，我们的方法还可以显著减少平均等待时间，从而使乘客受益。
Ride hailing services are gaining popularity in cities around the world, and the introduction of shared autonomous vehicles is likely to pronounce this trend. A problem with these services is the extra travel to relocate without customers, necessary to serve an asymmetric demand. One solution to reduce the amount of empty driving is the adoption of an optimized pricing strategy that takes into account the cost of relocation. In this work, we present a dynamic pricing optimization that is integrated with online predictive relocation. We simulate the choice between a service that is operated between densely distributed demand points with shared autonomous vehicles and a generic alternative mode. The strategy finds optimal fares for each origin/destination area in each period of the day to maximize net profits for the operator. Our approach is flexible, fast, and scalable. We demonstrate our proposed methodology with a large dataset of taxi trips from New York City. The results of the simulations show that our pricing strategy consistently increases the operator’s revenues and decreases relocation costs when compared to the case with an optimal but constant, distance dependent fare. In cases where passengers’ waiting times are significant, our approach also significantly reduces average wait times, thus benefiting passengers too.