Energy-efficient metro train rescheduling with uncertain time-variant passenger demands: An approximate dynamic programming approach
mode - subway/metro, economics - operating costs, operations - scheduling, planning - travel demand management, ridership - demand
Metro train rescheduling, Passenger delay, Energy efficiency, Approximate dynamic programming, Stochastic programming
In a heavily congested metro line, unexpected disturbances often occur to cause the delay of the traveling passengers, infeasibility of the current timetable and reduction of the operational efficiency. Due to the uncertain and dynamic characteristics of passenger demands, the commonly used method to recover from disturbances in practice is to change the timetable and rolling stock manually based on the experiences and professional judgements. In this paper, we develop a stochastic programming model for metro train rescheduling problem in order to jointly reduce the time delay of affected passengers, their total traveling time and operational costs of trains. To capture the complexity of passenger traveling characteristics, the arriving ratio of passengers at each station is modeled as a non-homogeneous poisson distribution, in which the intensity function is treated as time-varying origin-to-destination passenger demand matrices. By considering the number of on-board passengers, the total energy usage is modeled as the difference between the tractive energy consumption and the regenerative energy. Then, we design an approximate dynamic programming based algorithm to solve the proposed model, which can obtain a high-quality solution in a short time. Finally, numerical examples with real-world data sets are implemented to verify the effectiveness and robustness of the proposed approaches.
Permission to publish the abstract has been given by Elsevier, copyright remains with them.
Yin, J., Tang, T., Yang, L., Gao, Z., & Ran, B. (2016). Energy-efficient metro train rescheduling with uncertain time-variant passenger demands: An approximate dynamic programming approach. Transportation Research Part B: Methodological, Vol. 91, pp. 178–210.