Joint Train Timetable Optimization for Short-Turning Strategy and Flexible Train Decomposition

In order to adapt to the passenger demand with uneven spatial and temporal distribution, this paper studies the train timetable optimization problem and proposes a timetable optimization method that combines short-turning strategy and flexible train decomposition, i.e., rolling stock is able to determine whether it is required to be uncoupled and whether it should perform a short-turn strategy according to passenger demand. A mixed integer nonlinear programming(MINLP) model has been constructed to minimize the number of stranded passengers at all stations on the line. Numerical experiments are designed to evaluate the validity of the proposed method based on real data of Beijing Metro Yizhuang Line during morning peak hours. The results show that the introduction of flexible train decomposition mode can better decrease the amount of stranded passengers without increasing the amount of rolling stocks compared with the conventional short-turning operation strategy.