ABSTRACT
Combining the social factors, passenger transport factors, and train factors of the stations themselves, this paper uses the information entropy method and the systematic clustering method to classify the railway stations. On this basis, a multi-objective model is established with the service frequency of passenger stations, the number of single train stops, station capacity and train transport capacity as constraints, and the minimum railway operation cost and the minimum total travel time of passengers as the objectives. The optimization model is solved by a genetic algorithm. The study shows that compared with the actual scheme, the average service frequency of stations of different levels in the optimization scheme is reduced by 2.05 trains, the average OD service frequency of stations of different levels is reduced by 2.4 trains, the total number of stops is reduced by 36, the total travel time of passengers is reduced by 2.27%, and the transportation cost of the intercity railway is reduced by 9.21%.
