Analysis of Metro ridership at station level and station-to-station level in Nanjing: an approach based on direct demand models
Analysis of Metro ridership at station level and station-to-station level in Nanjing: an approach based on direct demand models
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DOI：
10.1007/s11116-013-9492-3
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发表时间：
2014
期刊：
影响因子：
4.3
通讯作者：
Jinbao Zhao;W. Deng;Yan Song;Yueran Zhu
中科院分区：
文献类型：
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作者：
Jinbao Zhao;W. Deng;Yan Song;Yueran Zhu
近年来，越来越多的研究采用直接需求模型来揭示公交客流量与影响因素之间的关系。以南京市轨道交通建设为例，分别采用多元回归模型和乘法模型，从车站层面和站站间层面研究轨道交通客流量的影响因素。自变量包括测量土地利用组合，多式联运连接，车站背景和旅行阻抗的因素。多元回归模型证明，11个变量与地铁站层面的乘客量显著相关：人口，就业，商业/办公楼面积，CBD虚拟变量，主要教育场所，娱乐场所和购物中心的数量，道路长度，接驳公交线路，自行车停车场和乘坐（P&R）空间，和转移虚拟变量。乘法模型的结果表明，影响地铁站客流量的因素也可能影响地铁站到站客流量，不同的行程结束（起点/目的地）和一天中的时间。与以往的案例研究相比，CBD虚拟变量和自行车P&R在解释南京地铁客流量方面具有统计学意义。此外，地铁出行阻抗变量对站间客流量有重要影响，反映了出行分布的基本时间衰减关系。模型结果的潜在影响包括通过考虑重要变量来估计车站级和车站到车站级的地铁乘客量，认识到建立合作的多式联运系统的必要性，并确定以公交为导向的发展机会。
A growing base of research adopts direct demand models to reveal associations between transit ridership and influence factors in recent years. This study is designed to investigate the factors affecting rail transit ridership at both station level and station-to-station level by adopting multiple regression model and multiplicative model respectively, specifically using an implemented Metro system in Nanjing, China, where Metro implementation is on the rise. Independent variables include factors measuring land-use mix, intermodal connection, station context, and travel impedance. Multiple regression model proves 11 variables are significantly associated with Metro ridership at station level: population, employment, business/office floor area, CBD dummy variable, number of major educational sites, entertainment venues and shopping centers, road length, feeder bus lines, bicycle park-and-ride (P&R) spaces, and transfer dummy variable. Results from multiplicative model indicate that factors influencing Metro station ridership may also influence Metro station-to-station ridership, varied by both trip ends (origin/destination) and time of day. In comparison with previous case studies, CBD dummy variable and bicycle P&R are statistically significant to explain Metro ridership in Nanjing. In addition, Metro travel impedance variables have significant influence on station-to-station ridership, representing the basic time-decay relationship in travel distribution. Potential implications of the model results include estimating Metro ridership at station level and station-to-station level by considering the significant variables, recognizing the necessity to establish a cooperative multi-modal transit system, and identifying opportunities for transit-oriented development.