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%.
Highlights
Recent years have witnessed a tremendous increase in the urban railway transit ridership
Passenger’s access walking time and egress walking time follow the truncated normal distribution (Equation 4), where μand σare respectively the mean and the standard deviation of the normal distribution. a and b are respectively the lower bound and the upper bound of the truncated normal distribution. ψ() is the probability density function (PDF) of the truncated normal distribution. φ() is the PDF of the normal distribution
Based on the Automatic Fare Collection (AFC) data and the current timetable parameters, the probabilities of passengers matching different trains would be calculated by using the Markov Chain Monte Carlo (MCMC) technique
Summary
Recent years have witnessed a tremendous increase in the urban railway transit ridership. Concerning the microcosmic statistical indicator, it consists of passengers’ various behaviours [12, 13] (Sun et al 2012, Zhou et al 2015), including transfer behaviour, train matching behaviour, walking behaviour, etc How to infer these implicit macroscopic and microcosmic indicators from the AFC data remains a major challenge. A model to minimise passengers’ total waiting time considering each passenger train matching is designed in the context of the peak hour. The main contribution of this paper is developing a two-stage model to deal with the passenger-and-trains matching problem and timetable optimisation. The final chapter concludes with a summary of the findings
Sign-in/Register to view highlights & summary 
Published Version (
Free)
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
