Earthquake resilience assessment and improving method of high-speed railway based on train timetable rescheduling. (November 2022)

• Record Type: Journal Article
• Authors: Li, Shuang; Tang, Yumeng; Zhai, Changhai
• Is Part Of: International journal of disaster risk reduction. Volume 82(2022)
• Publication Date: 2022-11
• Subjects: Resilience -- High-speed railway -- Rescheduling -- Disaster relief -- Earthquake; Emergency management -- Periodicals; Risk management -- Periodicals; Disaster relief -- Periodicals; Hazard mitigation -- Periodicals; 363.34
• DOI: 10.1016/j.ijdrr.2022.103361
• Languages: English
• ISSNs: 2212-4209

Abstract

Abstract: Owning the characteristics of fast transportation speed and large transportation volume, high-speed railway (HSR) transportation is one of the most critical ways for passenger and freight transportation between cities. On the HSR, trains operate according to the planned timetable normally. However, error and conflict will occur when facing unexpected disruptions for the inability of timely and correct rearrangement by human beings also due to those characteristics. Most literature contributions fail in considering the change in traffic demand after the disruption caused by specific disasters. To this end, a mixed integer linear programming (MILP) model considering the main track outage, track recovery, station recovery, and dynamic traffic demand to enhance seismic resilience is proposed to solve the timetable rescheduling problem (TRP) after earthquakes automatically and in timely. Moreover, the use of the original timetable as a guidance solution reduces the computation time significantly. The proposed method is performed on a realistic simulation of the Dalian-Shenyang section of the Harbin-Dalian HSR of the China HSR network against an earthquake. The outcomes provide the optimal timetable from the recovery period to resume to the original timetable that leads to the best resilience. A computational test of the proposed model on a heavily used part of the China HSR reveals that the method can find optimal solutions with the highest resilience in short computation times.