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Optimizing matching time interval and matching radius in on-demand ride-sourcing markets

机译:在按需骑行采购市场中优化匹配时间间隔和匹配半径

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摘要

With the availability of the location information of drivers and passengers, ride-sourcing platforms can now provide increasingly efficient online matching compared with physical searching and meeting performed in the traditional taxi market. The matching time interval (the time interval over which waiting passengers and idle drivers are accumulated and then subjected to peer-to-peer matching) and matching radius (or maximum allowable pick-up distance, within which waiting passengers and idle drivers can be matched or paired) are two key control variables that a platform can employ to optimize system performance in an online matching system. By appropriately extending the matching time interval, the platform can accumulate large numbers of waiting (or unserved) passengers and idle drivers and thus match the two pools with a reduced expected pick-up distance. However, if the matching time interval is excessively long, certain passengers may become impatient and even abandon their requests. Meanwhile, a short matching radius can reduce the expected pick-up distance but may decrease the matching rate as well. Therefore, the matching time interval and matching radius should be optimized to enhance system efficiency in terms of passenger waiting time, vehicle utilization, and matching rate. This study proposes a model that delineates the online matching process in ride-sourcing markets. The model is then used to examine the impact of the matching time interval and matching radius on system performance and to jointly optimize the two variables under different levels of supply and demand. Numerical experiments are conducted to demonstrate how the proposed modeling and optimization approaches can improve the real-time matching of ride-sourcing platforms. (C) 2019 Elsevier Ltd. All rights reserved.
机译:有了驾驶员和乘客的位置信息,与传统出租车市场上进行的实物搜索和会议相比,乘车采购平台现在可以提供越来越有效的在线匹配。匹配时间间隔(累积等待的乘客和空闲驾驶员的时间间隔,然后进行对等匹配)和匹配半径(或最大允许上车距离),在此范围内可以匹配等待的乘客和空闲驾驶员或成对)是平台可以用来优化在线匹配系统中系统性能的两个关键控制变量。通过适当延长匹配时间间隔,平台可以积累大量等待(或未服务)的乘客和空转驾驶员,从而以减少的预期接送距离来匹配两个池。但是,如果匹配时间间隔过长,某些乘客可能会变得不耐烦,甚至放弃他们的要求。同时,较短的匹配半径可以减少预期的拾取距离,但也可能降低匹配率。因此,应该优化匹配时间间隔和匹配半径,以在乘客等待时间,车辆利用率和匹配率方面提高系统效率。这项研究提出了一个模型,该模型描述了骑行采购市场中的在线匹配过程。然后,该模型用于检查匹配时间间隔和匹配半径对系统性能的影响,并在不同供需水平下共同优化两个变量。进行了数值实验,以证明所提出的建模和优化方法如何改善乘车来源平台的实时匹配。 (C)2019 Elsevier Ltd.保留所有权利。

著录项

  • 来源
    《Transportation research》 |2020年第1期|84-105|共22页
  • 作者

  • 作者单位

    Hong Kong Univ Sci & Technol Dept Civil & Environm Engn Kowloon Clear Water Bay Hong Kong Peoples R China;

    Univ Michigan Dept Computat Med & Bioinformat Ann Arbor MI 48109 USA|AI Labs Beijing Peoples R China;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Ride-sourcing; Online matching; Matching time interval; Matching radius;

    机译:拼车;在线匹配;匹配时间间隔;匹配半径;

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