论文标题
在城市规模的动态乘车服务服务中平衡出租车分配：基于需求学习的混合解决方案
Balancing Taxi Distribution in A City-Scale Dynamic Ridesharing Service: A Hybrid Solution Based on Demand Learning
论文作者
论文摘要
在本文中，我们研究了如何在动态乘车服务中平衡整个城市的出租车分配的具有挑战性的问题。首先，我们介绍了动态乘车系统的体系结构，并正式定义了指示系统效率的性能指标。然后，我们提出了一个涉及一系列算法的混合解决方案：相关的合并收集相关的骑手请求，基于需求的邻接乘车匹配，根据需求学习将出租车分配给骑手和在本地的出租车分配，贪婪的闲置运动旨在直接直接出租车，而无需当前与骑手的区域分配到需要服务的骑手。在实验中，我们应用了芝加哥市的城市规模数据集，并完成了一个案例研究，分析了相关骑手请求的门槛和每种算法的平均在线运行时间。我们还将混合解决方案与多种其他方法进行比较。我们的实验结果表明，我们的混合动力解决方案提高了客户的服务率，而不会增加运营中的出租车数量，这使得驾驶员都可以赚取更多和骑手以节省更多的每次旅行，并且呼叫和额外的旅行时间的增加。
In this paper, we study the challenging problem of how to balance taxi distribution across a city in a dynamic ridesharing service. First, we introduce the architecture of the dynamic ridesharing system and formally define the performance metrics indicating the efficiency of the system. Then, we propose a hybrid solution involving a series of algorithms: the Correlated Pooling collects correlated rider requests, the Adjacency Ride-Matching based on Demand Learning assigns taxis to riders and balances taxi distribution locally, the Greedy Idle Movement aims to direct taxis without a current assignment to the areas with riders in need of service. In the experiment, we apply city-scale data sets from the city of Chicago and complete a case study analyzing the threshold of correlated rider requests and the average online running time of each algorithm. We also compare our hybrid solution with multiple other methods. The results of our experiment show that our hybrid solution improves customer serving rate without increasing the number of taxis in operation, allows both drivers to earn more and riders to save more per trip, and all with a small increase in calling and extra trip time.
