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首页> 外文期刊>Transportation >Understanding urban mobility patterns from a spatiotemporal perspective: daily ridership profiles of metro stations
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Understanding urban mobility patterns from a spatiotemporal perspective: daily ridership profiles of metro stations

机译：从时空角度了解城市出行方式：地铁站的每日乘客量概况

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摘要

Smart card data derived from automatic fare collection (AFC) systems of public transit enable us to study resident movement from a macro perspective. The rhythms of traffic generated by different land uses differ, reflecting differences in human activity patterns. Thus, an understanding of daily ridership and mobility patterns requires an understanding of the relationship between daily ridership patterns and characteristics of stations and their direct environment. Unfortunately, few studies have investigated this relationship. This study aims to propose a framework of identifying urban mobility patterns and urban dynamics from a spatiotemporal perspective and pointing out the linkages between mobility and land cover/land use (LCLU). Relying on 1 month's transactions data from the AFC system of Nanjing metro, the 110 metro stations are classified into 7 clusters named as employment-oriented stations, residential-oriented stations, spatial mismatched stations, etc., each characterized by a distinct ridership pattern (combining boarding and alighting). A comparison of the peak hourly ridership of the seven clusters is conducted to verify whether the clustering results are reasonable or not. Finally, a multinomial logit model is used to estimate the relationship between characteristics of the local environment and cluster membership. Results show that the classification based on ridership patterns leads to meaningful interpretable clusters and that significant associations exist between local LCLU characteristics, distance to the city center and cluster membership. The analytical framework and findings may be beneficial for improving service efficiency of public transportation and urban planning.
机译：从公共交通自动收费系统（AFC）获得的智能卡数据使我们能够从宏观角度研究居民的出行情况。不同土地利用产生的交通节奏不同，反映出人类活动模式的差异。因此，要了解日常乘车方式和出行方式，就需要了解日常乘车方式与车站特性及其直接环境之间的关系。不幸的是，很少有研究调查这种关系。这项研究旨在提出一个框架，从时空的角度识别城市的交通方式和动态，并指出交通与土地覆盖/土地利用之间的联系。根据南京地铁AFC系统1个月的交易数据，将110个地铁站分为以就业为导向，以居民为导向，以空间错配为代表的7个集群，每个集群的骑行模式都不同（结合上下车）。比较了七个聚类的高峰小时乘车量，以验证聚类结果是否合理。最后，使用多项式logit模型来估计局部环境特征与集群成员之间的关系。结果表明，基于出行方式的分类导致有意义的可解释集群，并且当地LCLU特征，距市中心的距离和集群成员之间存在显着关联。分析框架和结果可能有助于提高公共交通和城市规划的服务效率。

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  • 来源
    《Transportation》 |2020年第1期|315-336|共22页
  • 作者

    Gan Zuoxian; Yang Min; Feng Tao; Timmermans Harry;

  • 作者单位

    Southeast Univ Sch Transportat 2 Southeast Univ Rd Nanjing 211189 Peoples R China|Southeast Univ Jiangsu Prov Collaborat Innovat Ctr Modern Urban 2 Southeast Univ Rd Nanjing 211189 Peoples R China;

    Eindhoven Univ Technol Dept Urban Sci & Syst Urban Planning Groups POB 513 NL-5600 Eindhoven Netherlands;

    Eindhoven Univ Technol Dept Urban Sci & Syst Urban Planning Groups POB 513 NL-5600 Eindhoven Netherlands|Nanjing Univ Aeronaut & Astronaut Coll Civil Aviat Nanjing 211106 Peoples R China;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Urban mobility; Ridership patterns; Smart card data; Station clustering; LCLU;

    机译：城市交通;乘车模式;智能卡数据;车站集群;加州大学;

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