Methods and Tools to Strengthen Transit System Resilience

根据世界各地大多数乘客满意度调查的结果，公交乘客在旅途中最不喜欢的就是公交车辆的长时间延误和过度拥挤。造成运输延误的因素很多，从运行环境中的小干扰一直到紧急情况或系统故障造成的重大事故。在多伦多，仅地铁系统每年就发生12000多起事故，其中约1,000起被认为是“重大”事故。交通延误往往会加剧过度拥挤，相反的情况也是如此。用户和运营商的成本可能是巨大的。最近的一项研究估计，纽约地铁事故造成的用户延误造成的经济损失每年超过3.89亿美元。这项研究计划旨在解决大型多式联运系统中的延误和过度拥挤问题。它采用了广泛的、多方面的方法，将产生需求管理、供应管理和流量管理的解决方案，所有这些都旨在一致和一致地缓解交通延误和过度拥挤，特别是在地铁系统中。为了管理需求，我们的研究将开发基于激励的创新计划，为从过度拥挤的地点和时间换乘的公交乘客提供奖励积分。基于激励和忠诚度的计划已经有效地应用于各种其他服务和行业，虽然它们正在慢慢地被引入公共交通，但它们需要辅助的实证工具来最大化其有效性。这些工具将在本研究中开发。为了管理供应，特别是在地铁服务发生重大中断的情况下，我们将开发一套模型和工具系统，帮助交通机构部署有效的穿梭巴士服务，以填补地铁服务的缺口。更具体地说，我们将开发分析方法来实时预测任何给定地铁事件的预期持续时间，并确定最佳公交车数量以及这些公交车应该从哪些路线调度到中断的地铁路段，从而使总延误降至最低。这项研究的第三条主要路线是管理进入地铁线路所有车站的客流，以避免因过度拥挤而造成的运力损失。这将通过创造性地调度与地铁线路连接的公共汽车和有轨电车支线，以及避免在任何给定车站过饱和的实时控制策略来实现。拟议的解决方案将利用目前大量可用的关于运输业务和需求的丰富和多样化的数据来制定。建议的模型建立在健全的理论基础上，将使用严格的和新的建模方法。拟议的研究可能会对过境复原力政策和技术产生变革性的影响。

Transit riders dislike nothing in their journeys more than long delays and overcrowding of transit vehicles, according to the results of most rider satisfaction surveys around the world. Many factors can cause transit delays, ranging from minor perturbations in the operating environment all the way to major incidents due to emergencies or system failure. In Toronto, the subway system alone is afflicted by more than 12000 incidents/year, approximately 1000 of which are considered “major”. Transit delays often exacerbate overcrowding, and the opposite is also true. The cost to users and the operator can be enormous. One recent study estimated the economic cost of user delays due to subway incidents in New York to be upward of 389 million USD annually. This research program is aimed directly at tackling the problems of delays and overcrowding in large multi-modal transit systems. It adopts a broad, multi-faceted approach which will produce solutions for demand management, supply management and flow management, all aimed consistently and coherently at alleviating transit delays and overcrowding, particularly in subway systems. To manage demand, our research will develop innovative incentive-based programs which offer reward points to transit riders who switch away from overcrowded spots and times. Incentive-based and loyalty programs have been applied effectively to a variety of other services and industries, and while they are slowly being introduced to public transit they require supporting empirical tools to maximize their effectiveness. These tools will be developed in this research. To manage supply, specifically in the event of a major disruption to subway service, we will develop a system of models and tools to help transit agencies deploy effective shuttle bus services to fill the gap in subway service. In more specific terms, we will develop analytics to predict in real-time the expected duration of any given subway incident, and we will determine the optimal number of buses and which routes these buses should be pulled from for dispatching to the disrupted subway section such that total delays are minimized. The third major track of this research is concerned with managing the flows of passengers into all stations of a subway line to avoid capacity loss due to overcrowding. This will be achieved through creative scheduling of the bus and streetcar feeder lines connecting with the subway line, along with a real-time control strategy to avoid oversaturation at any given station. The proposed solutions will be developed using the rich and diverse data on transit operations and demand which are now available abundantly. Founded in sound theory, the proposed models will be built using rigorous and new modelling approaches. The proposed research is likely to have a transformative effect on transit resilience policy and technologies.