A bi-level model for single-line rail timetable design with consideration of demand and capacity
place - urban, mode - rail, operations - scheduling, operations - capacity, operations - crowding, ridership - demand
Urban rail line, Headway, Timetable, Schedule-based, Capacity, User equilibrium
This paper proposes a bi-level model to solve the timetable design problem for an urban rail line. The upper level model aims at determining the headways between trains to minimize total passenger cost, which includes not only the usual perceived travel time cost, but also penalties during travel. With the headways given by the upper level model, passengers’ arrival times at their origin stops are determined by the lower level model, in which the cost-minimizing behavior of each passenger is taken into account. To make the model more realistic, explicit capacity constraints of individual trains are considered. With these constraints, passengers cannot board a full train, but wait in queues for the next coming train. A two-stage genetic algorithm incorporating the method of successive averages is introduced to solve the bi-level model. Two hypothetical examples and a real world case are employed to evaluate the effectiveness of the proposed bi-level model and algorithm. Results show that the bi-level model performs well in reducing total passenger cost, especially in reducing waiting time cost and penalties. And the section loading-rates of trains in the optimized timetable are more balanced than the even-headway timetable. The sensitivity analyses show that passenger’s desired arrival time interval at destination and crowding penalty factor have a high influence on the optimal solution. And with the dispersing of passengers' desired arrival time intervals or the increase of crowding penalty factor, the section loading-rates of trains become more balanced.
Permission to publish the abstract has been given by Elsevier, copyright remains with them.
Zhu, Y., Mao, B., Bai, Y., & Chen, S. (2017). A bi-level model for single-line rail timetable design with consideration of demand and capacity. Transportation Research Part C: Emerging Technologies, Vol. 85, pp. 211-233.