Crowding in urban rail transit is an inevitable issue for most of the high-density cities across the world, especially during peak time. For commuters who have considerably fixed destination arrival times, departure time choice is an important tool to adjust their trips. The ignorance of crowding impact on commuters’ departure time choice in urban rail transit may cause errors in forecasting dynamic passenger flow during peak time in urban rail transit. The paper develops a mixed logit model to identify how crowding impacts the departure time choice of commuters and their taste variation. Arrival time value was firstly measured in a submodel by applying the reference point approach and then integrated to the main model. Considering the characteristics of human perception, we divided crowding into five grades with distinct circumstances. All parameter distributions were assumed based on their empirical distributions revealed through resampling. The data from Shanghai Metro used for estimation were collected by a specifically designed survey, which combines revealed preference questions and stated preference experiments to investigate the willingness and extent of changing departure time choice of passengers who experienced various grades and duration of crowding in the most crowded part. The result shows that an asymmetric valuation model with preferred arrival time as the only reference point best captured commuters’ responses to arrival time. The departure time choice model clearly identified that only crowding ranging from Grades 3 to 5 had an impact on commuters’ departure time choice. The parameters of crowding costs can be assumed to follow transformed lognormal distributions. It is found that the higher the grade of crowding is, the bigger the impact each unit of crowding cost has on commuters’ departure time choice, while commuters’ tastes get more concentrated when crowded situation upgrades. The model in this paper can help policymakers better understand the interaction between commuters’ departure time choice and crowding alleviation.

中文翻译：

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识别拥挤对城市轨道交通通勤者出发时间选择的影响

对于世界上大多数高密度城市而言，城市轨道交通的拥挤是不可避免的问题，尤其是在高峰时段。对于目的地到达时间有相当固定的通勤者来说，出发时间的选择是调整行程的重要工具。城市轨道交通拥挤对通勤者出发时间选择的影响的无知，可能会导致在城市轨道交通高峰时段预测动态客流时产生误差。本文开发了一种混合logit模型，以识别拥挤如何影响通勤者的出发时间选择及其口味变化。首先通过使用参考点方法在子模型中测量到达时间值，然后将其集成到主模型中。考虑到人类感知的特征，我们将拥挤分为具有不同情况的五个等级。假设所有参数分布均基于通过重采样显示的经验分布。上海地铁用于估算的数据是通过专门设计的调查收集的，该调查结合了揭示的偏好问题和陈述的偏好实验，以研究经历了不同等级和拥挤持续时间的最拥挤乘客的改变出发时间选择的意愿和程度。部分。结果表明，以到达时间为唯一参考点的非对称估值模型可以最好地反映出通勤者对到达时间的反应。出发时间选择模型清楚地表明，只有3至5年级的人群对通勤者的出发时间选择有影响。可以假定拥挤成本的参数服从变换后的对数正态分布。研究发现，拥挤等级越高，每个拥挤成本对通勤者出发时间选择的影响越大，而当拥挤状况升级时通勤者的品味会变得更加集中。本文中的模型可以帮助决策者更好地了解通勤者出发时间选择与拥挤缓解之间的相互作用。