We propose a methodology based on multiple automated data sources for evaluating the effects of station layout, arriving traveler flows, and platform and on-board crowding on the distribution of boarding passengers among individual cars of metro trains. The methodology is applied to a case study for a sequence of stations in the Stockholm metro network. The findings suggest that passengers opt for less crowded train cars in crowded situations, trading-off walking and in-vehicle crowding while waiting and riding. We find that the boarding car distribution is also affected by the locations of platform access points and the distribution of entering traveler flows. These insights may be used by transit planners and operators to increase the understanding of how passengers behave under varying crowding conditions, identify the factors that affect travelers' choice of metro car and eventually reduce experienced on-board crowding and increase the capacity utilization of the trains through investments in infrastructure or operational interventions.

中文翻译：

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通过自动化数据源揭示乘客选择地铁车辆的决定因素：斯德哥尔摩案例研究

我们提出了一种基于多个自动化数据源的方法，用于评估车站布局、到达旅客流量、站台和车上拥挤对地铁列车单节车厢中乘客分布的影响。该方法应用于斯德哥尔摩地铁网络中一系列车站的案例研究。研究结果表明，乘客在拥挤的情况下选择不那么拥挤的火车车厢，在等待和乘坐时权衡步行和车内拥挤。我们发现登机车分布还受到平台接入点位置和进入旅客流量分布的影响。交通规划者和运营商可以使用这些见解来增加对乘客在不同拥挤条件下的行为的理解，