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Spatiotemporal evolution of ridesourcing markets under the new restriction policy: A case study in Shanghai

机译:新限制政策下的拼车市场时空演变:以上海为例

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摘要

Identifying and understanding factors that influence the demand of ridesourcing market is essential for online hailing systems to improve the quality of service. This paper proposes a two-level growth model (GM) to identify the potential multi-level factors that may affect online ride-hailing service demand. By using the massive datasets from Didi Chuxing, Inc., including both Didi Express and Didi Taxi services, the order number fluctuations at different urban circle zones after the implementation of restrictions on ridesourcing in Shanghai, 2016 were analyzed, to assess the competition and mutual complementarities between Express and Taxi, the two major services provided by Didi Chuxing. The relative market share of Express was estimated to reveal the possible related spatial and temporal factors, which further demonstrates significant positive associations between ridesourcing demand and built environment factors, such as commercial/residential land use, public transport accessibility, as well as weather conditions. Metro service availability and rainy weather were found correlated with a relatively higher market share of Express service. Additionally, compared to the regular road transit service, the metro system was found to have a stronger correlation with the ridesourcing demand. Findings of this study may provide guidelines for urban planning and traffic operations, which in turn assists to achieve high-quality ridesourcing service for travellers.
机译:识别和理解影响拼车市场需求的因素对于在线征服系统提高服务质量至关重要。本文提出了一个两级增长模型(GM),以识别可能影响在线乘车服务需求的潜在多级因素。通过使用滴滴出行公司的海量数据集,包括滴滴快车和滴滴出租车服务,分析了2016年上海实施出行限制之后,不同城市圈区域的订单数量波动,以评估竞争和相互滴滴出行提供的两项主要服务是快递和出租车之间的互补。据估计,Express的相对市场份额揭示了可能的相关时空因素,这进一步表明了乘车出行需求与建筑环境因素之间的显着正相关关系,例如商业/住宅用地,公共交通的可达性以及天气状况。发现地铁服务的可用性和阴雨天气与快速服务的相对较高的市场份额有关。另外,与常规的公路运输服务相比,发现地铁系统与出行需求具有更强的相关性。这项研究的结果可能会为城市规划和交通运营提供指导,从而有助于为旅行者提供高质量的骑行服务。

著录项

  • 来源
    《Transportation Research》 |2019年第12期|227-239|共13页
  • 作者

  • 作者单位

    Shanghai Jiao Tong Univ China Inst Urban Governance 1954 Hua Shan Rd Shanghai 2002030 Peoples R China|Shanghai Jiao Tong Univ Smart City & Intelligent Transportat Res Ctr Sch Naval Architecture Ocean & Civil Engn 800 Dongchuan Rd Shanghai 200240 Peoples R China;

    Shenzhen Urban Transportat Planning Ctr Shenzhen 518057 Peoples R China;

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

    Car-hailing service; Growth model (GM); Multi-level factors; Demand and market share change; New restrictions on ridesourcing;

    机译:租车服务;增长模型(GM);多层次因素;需求和市场份额变化;乘车租赁的新限制;

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