Institute of Transportation, MOTC

　　To satisfy different needs of passengers, intercity railway operators usually design various stopping patterns, such as all-stop, skip-stop, and express trains. The operators then assign adequate hourly service frequency for each stopping pattern. Since stopping patterns and service frequencies are typically determined by empirical rules or needs of some local citizens, the resulting arrangements may not be optimal and comprehensive, and most of them do not consider the time-space transition of trains and trips. Thus, this research aims to develop a decision support system to generate the optimal combination of stopping patterns for minimizing passenger in-vehicle time, and to obtain the best train service plans for maximizing operators’ profit. We refined the previous model for stopping pattern planning to resolve large-scale 0-1 integer programming problems. The optimal combination of stopping patterns obtained was then input to a train service plan, and the time-space transitions from the beginning to the end of trips were included, to determine the service frequencies of each stopping pattern in different periods through mixed integer programming. The proposed models were applied to the Taiwan High Speed Rail. The case analysis showed that the decision support system could precisely and efficiently find the optimal stopping patterns and service frequencies; the resulting passenger in-vehicle time and operators’ profit exhibited better results than current practices. The results demonstrate that the decision support system can not only meet passengers’ needs but also help operators to create more profits.