Abstract
Rescheduling is often needed when trains stay in segments or stations longer than specified in the timetable due to disturbances. Under crowded situations, it is more challenging to return to normal with heavy passenger flow. Considering making a trade-off between passenger loss and operating costs, we present a train regulation combined with a passenger control model by analyzing the interactive relationship between passenger behaviors and train operation. In this paper, we convert the problem into a Markov decision process and then propose the management strategy of regulating the running time and controlling the number of boarding passengers. Owing to the high dimensions of the large-scale problem, we applied the Approximate Dynamic Programming (ADP) approach, which approximates the value function with state features to improve computational efficiency. Finally, we designed three experimental scenarios to verify the effectiveness of our proposed model and approach. The results show that both the proposed model and the approach have a good performance in the cases with different passenger flows and different disturbances.
Highlights
When operation suffers a disturbance, prompt rescheduling measures must be taken to maintain the robustness of the metro system
Considering minimizing the total delay of passengers and service quality, as well as adjustment costs under dynamic passenger flow, we propose train regulation combined with a passenger control model under discrete the Markov decision process framework
This paper studies the train adjustment problem under dynamic passenger flow and establishes a model combined train regulation with passenger control
Summary
When operation suffers a disturbance, prompt rescheduling measures must be taken to maintain the robustness of the metro system. In the past few years, train rescheduling has caused great concerns among many researchers, and many different approaches have been developed with different model formulations. The train rescheduling problem is converted into the problem of mathematical programming, aiming to make the operation return to normal as soon as possible by altering the running time and dwelling time. It is most important to maintain service as much as possible for the customers [1]. Classical optimization methods were used for rail transit train regulation to describe passenger perception of service quality [2]. A mixed integer programming model was established in Ref. [4]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
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 call
