Risk factors affecting the severity of disruptions in metro operation in shanghai, 2013-2016

Abstract

With the increase of network-scale and operating mileages, the number of metro disruptions is increasing, which had caused the widespread concern in the community. Thus, based on disruption data obtained from Shanghai metro, this paper establishes three ordered logit models to examine the impact of risk factors on disruption severity from the perspective of society, metro operator, and passengers. The total delay time (Model A), the number of stations affected (Model B) and the delay time reported by the metro operator (Model C) were used to represent the consequence and severity of disruptions, respectively. Results show that the hourly distribution of disruptions, location of disruptions, interchange stations, location of station, and disruption type are common significant variables in three models, indicating that whether it is the government, metro operators, or passengers should pay more attention to common risk factors affecting the metro disruptions. Besides, passenger flow and environment factors have few effect on the number of stations affected, while the line and station factors are more likely to affect the number of stations. Furthermore, compared with the results of Model B and Model C, the Model A could efficiently identify more risk factors affecting the severity of disruption.