Options
Development of the Timetable Performance Evaluation System for Rail Transportation
Date Issued
2012
Date
2012
Author(s)
Chen, Kuan-Ting
Abstract
Reliable railway operation is a result of a well-designed timetable. A robust and stable timetable should incorporate an appropriate level of slacks in order to recover the system from the unexpected disruption to the normal state. However, due to the high cost of railway infrastructure, a surplus slack can incur an unexpected expense and waste. Consequently, the evaluation of timetable stability and efficiency is important, since there is a trade-off between the railway capacity, capacity utilization and stability.
Most of the previous studies evaluated timetable stability with delay index, while Li (2010) considered this index may either over or underestimate the stability and thus proposed to use recovery time from the aspect of railway capacity. Recovery time is the amount of time to clear out the disrupted scheduled trains and return to the normal state. Li calculated the expected recovery time of timetable as the stability index. However, due to the uncertainty of disturbance, the inherently randomness of recovery time should be further studied in order to provide a flexible evaluation result. In this research, a timetable performance evaluation system is developed with four indices, including efficiency, expected recovery time, standard deviation of recovery time and failure probability. And the Monte Carlo simulation accounted for the uncertainty of recovery time is also developed.
A case study of Taiwan Railway Administration (TRA) before and after the timetable revision on September 28th, 2012 was applied. The evaluation results showed that the bottlenecks of the stability are on peak periods and Xizhi to Qidu section. The analysis also showed that after the revision, the efficiency of capacity utilization increased. This led to the decrease of stability of northbound timetable, but not all the stability indices of southbound timetable indicated a worse result. With the evaluation of these four indices, accurate information can be provided to the railway agency in the timetable planning process so as to provide reliable and robust services to their customers, and return on shareholders’ investment.
Most of the previous studies evaluated timetable stability with delay index, while Li (2010) considered this index may either over or underestimate the stability and thus proposed to use recovery time from the aspect of railway capacity. Recovery time is the amount of time to clear out the disrupted scheduled trains and return to the normal state. Li calculated the expected recovery time of timetable as the stability index. However, due to the uncertainty of disturbance, the inherently randomness of recovery time should be further studied in order to provide a flexible evaluation result. In this research, a timetable performance evaluation system is developed with four indices, including efficiency, expected recovery time, standard deviation of recovery time and failure probability. And the Monte Carlo simulation accounted for the uncertainty of recovery time is also developed.
A case study of Taiwan Railway Administration (TRA) before and after the timetable revision on September 28th, 2012 was applied. The evaluation results showed that the bottlenecks of the stability are on peak periods and Xizhi to Qidu section. The analysis also showed that after the revision, the efficiency of capacity utilization increased. This led to the decrease of stability of northbound timetable, but not all the stability indices of southbound timetable indicated a worse result. With the evaluation of these four indices, accurate information can be provided to the railway agency in the timetable planning process so as to provide reliable and robust services to their customers, and return on shareholders’ investment.
Subjects
Timetable stability
Railway capacity
Recovery time
Monte Carlo simulation
SDGs
Type
thesis
File(s)
No Thumbnail Available
Name
index.html
Size
23.27 KB
Format
HTML
Checksum
(MD5):a46299d590eeb5a266e77915a6181122