Urban Railway Transit Timetable Optimisation Based on Passenger-and-Trains Matching – A Case Study of Beijing Metro Line

Abstract

Due to the congested scenarios of the urban railway system during peak hours, passengers are often left behind on the platform. This paper firstly brings a proposal to capture passengers matching different trains. Secondly, to reduce passengers’ total waiting time, timetable optimisation is put forward based on passengers matching different trains. This is a two-stage model. In the first stage, the aim is to obtain a match between passengers and different trains from the Automatic Fare Collection (AFC) data as well as timetable parameters. In the second stage, the objective is to reduce passengers’ total waiting time, whereby the decision variables are headway and dwelling time. Due to the complexity of our proposed model, an MCMC-GASA (Markov Chain Monte Carlo-Genetic Algorithm Simulated Annealing) hybrid method is designed to solve it. A real-world case of Line 1 in Beijing metro is employed to verify the proposed two-stage model and algorithms. The results show that several improvements have been brought by the newly designed timetable. The number of unique matching passengers increased by 37.7%, and passengers’ total waiting time decreased by 15.5%.

Keywords

• urban railway system
• train matching
• timetable optimisation
• afc data
• machine learning