首页> 外文期刊>Transportation research, Part A. Policy and practice >I can board, but I'd rather wait: Active boarding delay choice behaviour analysis using smart card data in metro systems
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I can board, but I'd rather wait: Active boarding delay choice behaviour analysis using smart card data in metro systems

机译:我可以登机,但我宁愿等待:在地铁系统中使用智能卡数据进行主动登机延迟选择行为分析

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In a crowded metro network, it is not unusual to observe that passengers actively choose not to board but wait for the next train for a seat, even if there is vacant standing room on the arriving train. We analyse such behaviour using a logit-based choice model based on revealed preference data collected from the smart card records and the operational timetables. The choice model considers waiting time, fluctuating crowding levels, and passengers' expected seat availability at each station on their trip. The revealed preference data are collected based on an existing time component framework, which can estimate passengers' itineraries by dividing passengers' travel time into time components (i.e. access, egress, boarding delay, and transfer-walking times) and analysing their uncertainty. We improve the time component framework by developing methods for estimating distributions corresponding to each time component. Using Chengdu Metro as a case, we find that the extra waiting time resulting from active boarding delay and standing time is valued 50.5 more positively and 25.3 more negatively than the in-vehicle sitting time, respectively. By comparing our findings with studies focused on passive boarding delays caused by fully loaded trains, we suggest that extra waiting time due to active and passive boarding delays should be explicitly distinguished in practice. The estimation results of the distributions for the time components indicate that access and egress walking times follow different distributions at given stations, as opposed to the assumption in most prior studies.
机译:在拥挤的地铁网络中,观察到乘客主动选择不上车而是等待下一班火车的座位并不罕见,即使到达的列车上有空置的站立空间。我们使用基于logit的选择模型来分析这种行为,该模型基于从智能卡记录和操作时间表中收集的显示偏好数据。选择模型考虑了等待时间、波动的拥挤程度以及乘客在旅途中每个车站的预期座位可用性。所揭示的偏好数据是基于现有的时间分量框架收集的,该框架可以通过将乘客的旅行时间划分为时间分量(即出入、出口、登机延误和转机步行时间)并分析其不确定性来估计乘客的行程。我们通过开发估计与每个时间分量相对应的分布的方法改进了时间分量框架。以成都地铁为例,我们发现,主动上车延误和站立时间导致的额外等待时间比车内坐时间的正值和负值分别高出50.5%和25.3%。通过将我们的研究结果与针对满载列车导致的被动登车延误的研究进行比较,我们建议在实践中应明确区分由于主动和被动登车延误而导致的额外等待时间。时间分量分布的估计结果表明,在给定站点上,通道和出口步行时间遵循不同的分布,这与大多数先前研究中的假设相反。

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