Meiling Su, Ling Liu, Xiyang Chen, Rongxian Long, Chenhui Liu
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Finally, the mixed geographically weighted regression\n models are constructed to quantitatively identify the effects of these built\n environment factors on the AM and PM peak ridership, respectively. The\n estimation results indicate that population density and employment density have\n significant but opposite influences on the AM and PM peak ridership, which can\n be attributed to the opposite travel directions of commuters in the AM and PM\n peak. The recreational facility density, road density, and 10-min walking\n accessibility could significantly positively affect the PM peak ridership, and\n their influences vary greatly over space. Besides, the operating time of\n stations significantly positively affects both the AM and PM peak ridership,\n transfer stations have significantly larger PM peak ridership and terminal\n stations have significantly larger AM peak ridership. The findings are expected\n to provide new insights for agencies to formulate appropriate measures to\n improve the ridership of urban rail transit.","PeriodicalId":181105,"journal":{"name":"SAE International Journal of Sustainable Transportation, Energy, Environment, & Policy","volume":"3 7","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Exploration of the Impact of Built Environment Factors on Morning and\\n Evening Peak Ridership at Urban Rail Transit Stations: A Case Study of Changsha,\\n China\",\"authors\":\"Meiling Su, Ling Liu, Xiyang Chen, Rongxian Long, Chenhui Liu\",\"doi\":\"10.4271/13-06-02-0009\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"To identify the influences of various built environment factors on ridership at\\n urban rail transit stations, a case study was conducted on the Changsha Metro.\\n First, spatial and temporal distributions of the station-level AM peak and PM\\n peak boarding ridership are analyzed. The Moran’s I test indicates that both of\\n them show significant spatial correlations. Then, the pedestrian catchment area\\n of each metro station is delineated using the Thiessen polygon method with an\\n 800-m radius. The built environment factors within each pedestrian catchment\\n area, involving population and employment, land use, accessibility, and station\\n attributes, are collected. Finally, the mixed geographically weighted regression\\n models are constructed to quantitatively identify the effects of these built\\n environment factors on the AM and PM peak ridership, respectively. The\\n estimation results indicate that population density and employment density have\\n significant but opposite influences on the AM and PM peak ridership, which can\\n be attributed to the opposite travel directions of commuters in the AM and PM\\n peak. The recreational facility density, road density, and 10-min walking\\n accessibility could significantly positively affect the PM peak ridership, and\\n their influences vary greatly over space. Besides, the operating time of\\n stations significantly positively affects both the AM and PM peak ridership,\\n transfer stations have significantly larger PM peak ridership and terminal\\n stations have significantly larger AM peak ridership. 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引用次数: 0
摘要
为了确定各种建筑环境因素对城市轨道交通车站乘客量的影响,我们对长沙地铁进行了案例研究。首先,分析了车站早高峰和晚高峰乘车人次的时空分布。Moran's I 检验表明,两者在空间上存在显著的相关性。然后,采用 Thiessen 多边形方法,以 800 米为半径,划定了每个地铁站的行人集聚区。收集每个行人集聚区内的建筑环境因素,包括人口和就业、土地利用、可达性和车站属性。最后,建立混合地理加权回归模型,分别定量确定这些建筑环境因素对上午和下午高峰乘客量的影响。估计结果表明,人口密度和就业密度对早高峰和晚高峰乘客量的影响显著但相反,这可能是由于早高峰和晚高峰乘客的出行方向相反。娱乐设施密度、道路密度和 10 分钟步行可达性对下午高峰乘客量有显著的正向影响,且影响程度随空间变化较大。此外,车站运营时间对早高峰和晚高峰乘客量均有明显的正向影响,换乘站的晚高峰乘客量明显更大,终点站的早高峰乘客量明显更大。这些研究结果有望为相关机构提供新的启示,以制定适当的措施来提高城市轨道交通的乘客率。
Exploration of the Impact of Built Environment Factors on Morning and
Evening Peak Ridership at Urban Rail Transit Stations: A Case Study of Changsha,
China
To identify the influences of various built environment factors on ridership at
urban rail transit stations, a case study was conducted on the Changsha Metro.
First, spatial and temporal distributions of the station-level AM peak and PM
peak boarding ridership are analyzed. The Moran’s I test indicates that both of
them show significant spatial correlations. Then, the pedestrian catchment area
of each metro station is delineated using the Thiessen polygon method with an
800-m radius. The built environment factors within each pedestrian catchment
area, involving population and employment, land use, accessibility, and station
attributes, are collected. Finally, the mixed geographically weighted regression
models are constructed to quantitatively identify the effects of these built
environment factors on the AM and PM peak ridership, respectively. The
estimation results indicate that population density and employment density have
significant but opposite influences on the AM and PM peak ridership, which can
be attributed to the opposite travel directions of commuters in the AM and PM
peak. The recreational facility density, road density, and 10-min walking
accessibility could significantly positively affect the PM peak ridership, and
their influences vary greatly over space. Besides, the operating time of
stations significantly positively affects both the AM and PM peak ridership,
transfer stations have significantly larger PM peak ridership and terminal
stations have significantly larger AM peak ridership. The findings are expected
to provide new insights for agencies to formulate appropriate measures to
improve the ridership of urban rail transit.