An intercity passenger rail is built to connect several major cities. To provide satisfactory services to passengers, railway operators plan different stop schedules, such as all-stop, skip-stop, and express services. However, stopping patterns determined by empirical rules or political arguments are generally not optimal. This paper aims at developing a decision support system to generate the optimal combination of stopping patterns for minimizing total passenger in-vehicle time. This problem was first formulated by using mixed integer programming, but this method is intractable when dealing with large-scale problems because of the complexity of model structure and the nature of the problem. A genetic algorithm was then developed to search for the optimal or near-optimal solution efficiently within a reasonable computation time. The proposed algorithm was successfully implemented on Taiwan High Speed Rail. The resulting solution is better than the current practice, and the proposed algorithm is capable of finding the optimal solution in seconds. The present case study demonstrates that the decision support tool can tackle large-scale problems and can help operators efficiently and effectively design an optimal combination of stopping patterns.

Intercity railway system operation on national holidays can be challenging because of possible surging demand. This study proposes an analysis framework to investigate railway system ridership data on national holidays, seeking to attain better understanding of relevant intercity trip patterns, so as to enable enhanced preparation and response before and during national holidays. The ridership data are analyzed in the form of Origin–Destination (O-D) tables and regarded as pictures of N ×  N pixels, where N is the number of the considered stations/cities in a railway system. The framework primarily adopts a deep auto-encoder to process these pictures to reduce data dimensions and abstracting key features within these pictorial data. Based on the abstracted features, k-means clustering is then conducted to categorize the O-D tables with similar trip patterns into the same group. Further, a discrete outcome model based on logistic regression is developed on the clustering results to enhance the interpretation of the trip pattern in each group and identify the significant holiday-related characteristics and external factors that can affect the trip pattern generation. The ridership data of Taiwan Railways Administration associated with 38 national holidays from January 2014 to August 2018 are analyzed. The analysis results highlight insightful interpretation in relation to clustered trip patterns and relevant trip characterization relative to various national holidays. The proposed framework and developed discrete outcome model are also validated, showing 85% correct assignments of O-D tables to the groups of relevant trip patterns.