城市近容量地铁的最优拥塞控制策略：通过基本图进行通知干预
Optimal congestion control strategies for near-capacity urban metros: informing intervention via fundamental diagrams
摘要：
拥堵;由于地铁拥挤和列车排队的恶性循环而导致的运行延迟;对于地铁系统来说是一个不断升级的问题，因为它具有从乘客不适到最终模式转换的负面后果。由于大量乘客在瓶颈车站上落，这可能会导致车站停车时间增加，从而导致上游列车排队，进一步减少线路吞吐量，并意味着车站的乘客积聚更大。缓解拥挤需要控制策略，例如调节进入瓶颈车站的乘客流入。日常运营中的大规模列车和训练移动数据的可用性促进了模型的开发，这些模型可以以数据驱动的方式为此类战略提供信息。在本文中，我们建议通过经验的乘客上下车和训练流的关系，以下，基本图（FD）模型站级的列车拥挤。我们强调，估计FD使用站级数据是具有挑战性的经验，由于混淆的偏见所产生的相互依赖的操作在不同的车站，这掩盖了真正的来源，在网络中的拥塞。因此，我们采用因果统计建模方法来产生对混淆具有鲁棒性的FD，并因此适合适当地告知控制策略。与该模型最接近的先行者是道路交通网络的FD，它通过定位最佳运行点等方式通知交通管理策略。我们对香港地下铁路数据的分析表明，在已确定的瓶颈车站存在凹形FD，以及相关的临界上下车水平，如果不进行干预，拥堵就会开始。
英文摘要：
Congestion; operational delays due to a vicious circle of passenger-congestion and train-queuing; is an escalating problem for metro systems because it has negative consequences from passenger discomfort to eventual mode-shifts. Congestion arises due to large volumes of passenger boardings and alightings at bottleneck stations, which may lead to increased stopping times at stations and consequent queuing of trains upstream, further reducing line throughput and implying an even greater accumulation of passengers at stations. Alleviating congestion requires control strategies such as regulating the inflow of passengers entering bottleneck stations. The availability of large-scale smartcard and train movement data from day-to-day operations facilitates the development of models that can inform such strategies in a data-driven way. In this paper, we propose to model station-level passenger-congestion via empirical passenger boarding-alightings and train flow relationships, henceforth, fundamental diagrams (FDs). We emphasise that estimating FDs using station-level data is empirically challenging due to confounding biases arising from the interdependence of operations at different stations, which obscures the true sources of congestion in the network. We thus adopt a causal statistical modelling approach to produce FDs that are robust to confounding and as such suitable to properly inform control strategies. The closest antecedent to the proposed model is the FD for road traffic networks, which informs traffic management strategies, for instance, via locating the optimum operation point. Our analysis of data from the Mass Transit Railway, Hong Kong indicates the existence of concave FDs at identified bottleneck stations, and an associated critical level of boarding-alightings above which congestion sets-in unless there is an intervention.
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