Paper Info
Title
Train Scheduling for Minimizing the Total Travel Time With a Skip-Stop Operation in Urban Rail Transit.
Abstract
The skip-stop operating mode for urban rail transit systems defines three types of stations: A, B, and AB stations. In this strategy, all the trains stop at AB stations, and A (B) trains stop at only A (B) stations. Skipping some stops can increase the operating speed, which leads to direct benefits by reducing the in-vehicle time for passengers and improving the operational efficiency for operators. With the skip-stop mode, passengers have to transfer at an AB station when the trains they take do not stop at their destination, and the extra transfer waiting time depends on the AB station distribution and the corresponding headways. In this paper, we develop a train scheduling optimization model to minimize the total passenger travel time so that passengers can get to their destinations faster without experiencing excessive transfer waiting times. A two-phase approach based on a genetic algorithm is developed to solve this mixed-integer nonlinear problem. Finally, the proposed train scheduling optimization model is applied to an idealized corridor and a real-world scenario based on Line 6 of the Beijing URT network. An analysis of the type of scenarios in which skip-stop operations are applicable and the efficiency of different types of trips is presented. The results show that a short headway (below 5-6 min) is favorable for a skip-stop operation and there needs to be at least one AB station for everyfive stations. The average travel time per passenger can be reduced by more than 1 min for the Beijing scenario under the optimized skip-stop operation.
Year
DOI
Venue
2019
10.1109/ACCESS.2019.2923231
IEEE ACCESS
Keywords
Field
DocType
Urban rail transit (URT),skip-stop,schedule optimization,travel time,genetic algorithm (GA)
Headway,Scheduling (computing),Computer science,Operating speed,Computer network,Real-time computing,Schedule,Urban rail transit,Train,Genetic algorithm,Beijing
Journal
Volume
ISSN
Citations 
7
2169-3536
0
PageRank 
References 
Authors
0.34
0
4
Name
Order
Citations
PageRank
Anan Yang100.34
Jianling Huang200.34
Bo Wang35718.01
Yanyan Chen4163.59