LAUSR

Abstract The comprehensive optimization of the timetables of urban rail transit systems under more realistic conditions is essential for their practical operation. Currently, most time-dependent timetabling models do not adequately consider train capacity and variable operation parameters. To bridge this gap, this study mainly investigates the timetable design problem of the urban rail transit system so as to adapt to time-dependent passenger demand under congested conditions by considering the variable number of trains, train running time, and train dwell time. Two nonlinear non-convex programming models are formulated to design timetables with the objective of minimizing the total passenger travel time (TTT) under the constraints of train operations, and passenger boarding and alighting processes. The difference between the two models is that one is a train-capacity unconstrained model and the other is a train-capacity constrained model. The proposed models are examined through real-world cases solved by the adaptive large neighborhood search algorithm. The results show that the first model can minimize passenger TTT under dynamic passenger demand, whereas the second can comprehensively optimize passenger TTT and meantime keep the train load factor within a reasonable level. Accordingly, it is concluded that the proposed models are more realistic.