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首页> 外文会议>IEEE International Smart Cities Conference >Balancing Taxi Distribution in A City-Scale Dynamic Ridesharing Service: A Hybrid Solution Based on Demand Learning
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Balancing Taxi Distribution in A City-Scale Dynamic Ridesharing Service: A Hybrid Solution Based on Demand Learning

机译：在城市规模的动态拼车服务中平衡出租车分配：基于需求学习的混合解决方案

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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.
机译：在本文中，我们研究了在动态乘车服务中如何平衡城市中出租车的分配这一具有挑战性的问题。首先，我们介绍了动态乘车共享系统的体系结构，并正式定义了指示系统效率的性能指标。然后，我们提出一种包含一系列算法的混合解决方案：“关联池”收集相关的骑手请求;基于“需求学习”的“邻接乘车”将出租车分配给骑手，并在本地平衡出租车的分配;“贪婪怠速运动”旨在引导没有出租车的出租车当前分配给需要服务的骑手的区域。在实验中，我们应用了来自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阈值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其他多种方法进行了比较。我们的实验结果表明，我们的混合动力解决方案在不增加出租车运营数量的情况下提高了客户服务率，使驾驶员和司机在每次旅行中都可以赚取更多的钱，并且可以为他们节省更多，而所有这些仅需要增加通话时间和额外的旅行时间。

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