This paper proposes a general unplanned incident analysis framework for public transit systems from the supply and demand sides using automated fare collection (AFC) and automated vehicle location (AVL) data. Specifically, on the supply side, we propose an incident-based network redundancy index to analyze the network’s ability to provide alternative services under a specific rail disruption. The impacts on operations are analyzed through the headway changes. On the demand side, the analysis takes place at two levels: aggregate flows and individual responses. We calculate the demand changes of different rail lines, rail stations, bus routes, and bus stops to better understand the passenger flow redistribution under incidents. Individual behavior is analyzed using a binary logit model based on inferred passengers’ mode choices and socio-demographics using AFC data. The public transit system of the Chicago Transit Authority is used as a case study. Two rail disruption cases are analyzed, one with high network redundancy around the impacted stations and the other with low. Results show that the service frequency of the incident line was largely reduced (by around 30%~70%) during the incident time. Nearby rail lines with substitutional functions were also slightly affected. Passengers showed different behavioral responses in the two incident scenarios. In the low redundancy case, most of the passengers chose to use nearby buses to travel further, either to their destinations or to the nearby rail lines. In the high redundancy case, most of the passengers transferred directly to nearby bus or rail lines. The results of the individual analysis show that the increase in network redundancy can increase the probability of using transit during disruptions. This effect is more prominent for low-income passengers. Corresponding policy implications and operating suggestions are discussed.

中文翻译：

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计划外的长期服务中断对城市公共交通系统的影响


本文利用自动售检票（AFC）和自动车辆定位（AVL）数据，从供需双方角度提出了公共交通系统的通用意外事件分析框架。具体来说，在供应方面，我们提出了基于事件的网络冗余指数，以分析网络在特定铁路中断情况下提供替代服务的能力。通过车头时距变化来分析对运营的影响。在需求方面，分析在两个层面进行：总体流量和个体响应。我们计算不同铁路线、火车站、公交线路、公交站点的需求变化，以更好地了解事件下的客流重新分配。使用二元 Logit 模型分析个人行为，该模型基于使用 AFC 数据推断乘客的模式选择和社会人口统计数据。以芝加哥交通管理局的公共交通系统作为案例研究。分析了两种铁路中断案例，一种是受影响车站周围的网络冗余度较高，另一种是较低的网络冗余度。结果表明，事故发生期间，事故线路的服务频率大幅下降（约下降30%~70%）。附近具有替代功能的铁路线也受到轻微影响。乘客在这两种事件场景中表现出不同的行为反应。在冗余度较低的情况下，大多数乘客选择乘坐附近的公交车前往更远的地方，要么前往目的地，要么前往附近的铁路线。在高冗余情况下，大多数乘客直接转乘附近的公交车或铁路线。单独分析的结果表明，网络冗余的增加可以增加在中断期间使用交通的可能性。 这种效应对于低收入乘客来说更为突出。讨论了相应的政策含义和操作建议。