Modelling the impact of causal and non-causal factors on disruption duration for Toronto’s subway system: An exploratory investigation using hazard modelling
place - north america, place - urban, mode - subway/metro, planning - safety/accidents
Public transit, Subway, Service delay, Service disruption, Survival analysis
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.
Permission to publish the abstract has been given by Elsevier, copyright remains with them.
Louie, J., Shalaby, A., & Habib, K.N. (2017). Modelling the impact of causal and non-causal factors on disruption duration for Toronto’s subway system: An exploratory investigation using hazard modelling. Accident Analysis & Prevention, Vol. 98, pp.232-240.