Q-learning approach to coordinated optimization of passenger inflow control with train skip-stopping on a urban rail transit line. (January 2019)

• Record Type: Journal Article
• Authors: Jiang, Zhibin; Gu, Jinjing; Fan, Wei; Liu, Wei; Zhu, Bingqin
• Is Part Of: Computers & industrial engineering. Volume 127(2019)
• Page Start: 1131
• Page End: 1142
• Publication Date: 2019-01
• Subjects: Coordinated optimization scheme -- Passenger inflow control -- Skip-stopping strategy -- Q-learning approach -- Urban rail transit line; Engineering -- Data processing -- Periodicals; Industrial engineering -- Periodicals; 620.00285
• DOI: 10.1016/j.cie.2018.05.050
• Languages: English
• ISSNs: 0360-8352

Abstract

Graphical abstract: Highlights: A coordinated scheme is proposed in an over-crowded URT line to ensure the safety of passengers. The optimization scheme combines the coordinated passenger inflow control with train rescheduling. The proposed model is to minimize the penalty value of passengers being stranded along the whole line. A novel Q-learning based approach to this combinatorial optimization problem is developed. A real-world URT line in Shanghai is used to demonstrate the performance of coordinated scheme. Abstract: In the case of an over-crowded urban rail transit (URT) line, a large number of passengers may be left stranded and daily timetable may become infeasible. This paper proposes a coordinated optimization scheme for a URT line, which combines both the coordinated passenger inflow control with train rescheduling strategies. With the aim of minimizing the penalty value of passengers being stranded along the whole line, the coordinated passenger inflow control helps relieve demand pressure and ensure safety at over-crowded URT stations while the train rescheduling of skip-stopping helps to balance the utilization of train capacity. A novel Q-learning based approach to this combination optimization problem is developed. Simulation experiments are carried out on a real-world URT line in Shanghai. Basic principles of Q-learning are presented, which consist of the environment and its states, learning agents and their respective actions, and rewards. The results show that the coordinated optimization scheme solved by the Q-learning approach is effective in relieving the passenger congestion on the URT line. The Q-learning approach can offer accurate scheme to deal with the problem of passenger congestion and train operation on a URT line.