Panel Regression Analysis of Disruption Frequency of Urban Rail Transit Systems

Abstract

High-frequency short delays in the urban rail transit system result in a cumulative delay effect within the network, which in turn affects the daily operational organization. Most studies focus more on long-term disruption, but there is less research on high-frequency short delays, lacking detailed classification and definition. Based on the 13-week operation data of Nanjing Metro and the detailed division of short-delay frequency, this study constructed four panel-regression models and compared and explained the influencing factors from multiple perspectives. The results show that the negative binomial fixed-effects model has the best goodness of fit. The two-way fixed-effects model increased complexity but does not improve fitting performance compared with the fixed-effects model. There are no significant time fixed effects in the three types of frequency data. New routes, remote stations with low passenger volume, stations with more train trips, and stations with surrounding land use of commercial service facilities have a higher frequency of minor delays, highlighting the need to deploy adequate facilities at these stations.