Mining train delay propagation pattern from train operation records in a high-speed system

Abstract

This study aims to investigate delays, delay increases, and delay recovery characteristics, by using statistical methods to clarify delay propagation patterns according to historical records of the Wuhan-Guangzhou high-speed railway (HSR) in China in 2014 and 2015. Specifically, we examined arrival and departure delay duration distributions and used heatmaps to demonstrate the spatiotemporal frequency distribution of delays, delay increases, and delay recovery, and the heatmaps clearly show hot spots (coordinates with high frequencies) in a timetable. Then, we separated delays as discrete intervals according to their severity, and analyzed the delay increasing frequency and the delay increasing severity within each interval, so as to clarify the relationships of delay increasing probability and delay increasing severity with delay extents. Next, we investigated the observed delay recoveries and prescheduled buffer times at (in) station (section), which demonstrate the recovery ability of each station and section. Finally, to understand the key influencing factor of delay propagation, we analyzed the relationship between capacity utilization and delays, delay increases, and delay recoveries, by examining their Pearson correlation coefficients. These indicate that delay frequencies and delay increasing frequencies with Pearson correlation coefficients as high as 0.9 are highly dependent on capacity utilization. The uncovered delay propagation patterns can enrich dispatchers’ experience, and improve their decision-making ability during real-time dispatching in HSR.