Zou Qingru, Xia Yue, Ran Xinchen, Guo Xueli, Feng Jiaxiao
Department of Chongqing Key Laboratory of Intelligent Integrated and Multidimensional Transportation System, Chongqing Jiaotong University, Chongqing, China.
Department of School of Traffic and Transportation, Chongqing Jiaotong University, Chongqing, China.
PLoS One. 2025 May 27;20(5):e0323937. doi: 10.1371/journal.pone.0323937. eCollection 2025.
Mountainous urban rail transit stations exhibit distinct characteristics. To investigate how these features affect passenger flow variations at rail stations, we analyze geographic-environmental data surrounding the stations and integrate road network topology, automatic fare collection data, and point-of-interest (POI) data. We propose a method to classify rail transit stations by considering the mountainous features and establish a multiscale geographically weighted regression (MGWR) model to assess the classification results. This study focuses on 189 rail stations in Chongqing, identifying six station categories: comprehensive mountainous, comprehensive non-mountainous, employment mountainous, employment non-mountainous, residential mountainous, and residential non-mountainous. The MGWR results show that road growth coefficients, average longitudinal slopes, and road lengths significantly influence station performance. For instance, the average longitudinal slope substantially affects employment in mountainous stations, particularly during the morning peak. The analysis reveals that the average longitudinal slope exerts a stronger negative effect on morning peak inbound passenger flow at employment mountainous stations (-0.949), indicating that commuters are more sensitive to travel time during the morning peak. In contrast, the evening peak inbound passenger flow is less impacted (-0.409), suggesting that evening commuters face fewer time constraints. These findings offer strategic insights for zoning transit stations to support transit-oriented development(TOD).
山地城市轨道交通车站具有鲜明的特点。为了研究这些特征如何影响轨道交通车站的客流变化，我们分析了车站周边的地理环境数据，并整合了道路网络拓扑、自动售检票数据和兴趣点（POI）数据。我们提出了一种考虑山地特征对轨道交通车站进行分类的方法，并建立了多尺度地理加权回归（MGWR）模型来评估分类结果。本研究聚焦于重庆的189个轨道交通车站，识别出六种车站类型：综合山地型、综合非山地型、就业山地型、就业非山地型、居住山地型和居住非山地型。MGWR结果表明，道路增长系数、平均纵坡和道路长度对车站性能有显著影响。例如，平均纵坡对山地车站的就业情况有显著影响，尤其是在早高峰期间。分析表明，平均纵坡对就业山地型车站早高峰进站客流的负面影响更强（-0.949），这表明通勤者在早高峰期间对出行时间更为敏感。相比之下，晚高峰进站客流受到的影响较小（-0.409），这表明晚高峰通勤者面临的时间限制较少。这些发现为划分交通站点区域以支持以公交为导向的发展（TOD）提供了战略见解。
