Louie Jacob, Shalaby Amer, Habib Khandker Nurul
University of Toronto, Canada.
Department of Civil Engineering, University of Toronto, Canada.
Accid Anal Prev. 2017 Jan;98:232-240. doi: 10.1016/j.aap.2016.10.008. Epub 2016 Oct 19.
Most investigations of incident-related delay duration in the transportation context are restricted to highway traffic, with little attention given to delays due to transit service disruptions. Studies of transit-based delay duration are also considerably less comprehensive than their highway counterparts with respect to examining the effects of non-causal variables on the delay duration. However, delays due to incidents in public transit service can have serious consequences on the overall urban transportation system due to the pivotal and vital role of public transit. The ability to predict the durations of various types of transit system incidents is indispensable for better management and mitigation of service disruptions. This paper presents a detailed investigation on incident delay durations in Toronto's subway system over the year 2013, focusing on the effects of the incidents' location and time, the train-type involved, and the non-adherence to proper recovery procedures. Accelerated Failure Time (AFT) hazard models are estimated to investigate the relationship between these factors and the resulting delay duration. The empirical investigation reveals that incident types that impact both safety and operations simultaneously generally have longer expected delays than incident types that impact either safety or operations alone. Incidents at interchange stations are cleared faster than incidents at non-interchange stations. Incidents during peak periods have nearly the same delay durations as off-peak incidents. The estimated models are believed to be useful tools in predicting the relative magnitude of incident delay duration for better management of subway operations.
大多数关于交通领域中与事故相关的延误时长的调查都局限于公路交通,很少关注公共交通服务中断造成的延误。在研究非因果变量对延误时长的影响方面,基于公共交通的延误时长研究也远不如公路交通研究全面。然而,由于公共交通的关键和重要作用,公共交通服务中的事故造成的延误可能会对整个城市交通系统产生严重后果。预测各类公共交通系统事故的时长对于更好地管理和减轻服务中断至关重要。本文详细调查了2013年多伦多地铁系统中的事故延误时长,重点关注事故发生的地点和时间、所涉及的列车类型以及未遵守适当恢复程序的影响。通过估计加速失效时间(AFT)风险模型来研究这些因素与由此产生的延误时长之间的关系。实证研究表明,同时影响安全和运营的事故类型通常比仅影响安全或运营的事故类型预期延误更长。换乘站的事故比非换乘站的事故清理得更快。高峰期的事故延误时长与非高峰期的事故几乎相同。估计的模型被认为是预测事故延误时长相对大小的有用工具,有助于更好地管理地铁运营。