How to Optimize Train Stops under Diverse Passenger Demand: a New Line Planning Method for Large-Scale High-Speed Rail Networks

Abstract Optimizing stop plans of train lines greatly contributes to improving the quality of rail passenger service. Traditionally, stop plans are pre-specified according to the classification of stations in the line planning process. However, with the expansion of railway networks and the great changes of travel demand among different origin–destination pairs (ODs), it becomes more difficult to generate stops using this simple method. To meet the new challenges of line planning in railway management, a linear integer programming model based on a new definition of lines is presented in this paper. In the proposed model, proper lines with reasonable train ODs and frequencies can be selected from a series of potential lines in the given line pool. The stops of selected lines can be chosen from candidate stop sets that are generated based on both station classification and types of trains. More practical considerations of stop planning are integrally considered, i.e., the balance of the stop distribution and restrictions on the stop numbers. In experiments involving small-scale cases, the stop numbers decrease under different types of demand scenarios when using the proposed method compared to the traditional methods. We also conduct a series of sensitivity analyses using large-scale random cases to reveal the impact of different stopping rules on the quality of passenger services. Finally, two real-world case studies based on the Beijing-Shanghai high-speed rail network are utilized to demonstrate the applicability of the proposed method.