Train timetabling for a double-track urban rail transit line under dynamic passenger demand. (January 2022) - Bucak, Serkan; Demirel, Tufan
Highlights: We formulate a nonlinear programming model to devise demand-adapted timetables in a double-track urban rail transit line. The dynamic behavior of passenger demand is incorporated into the model via cumulative demand curves. The model takes into account left-behind passengers resulting from heavy congestion in trains. Train departure times, running times between stations, and dwell times are optimized simultaneously. The experimental results reveal that our approach is effective in reducing the average waiting time at stations. Abstract: Train timetable is of critical importance for an urban rail transit line, chiefly because it is the primary factor determining passenger perception of service quality. As it not only delivers efficient transit service to users but also significantly contributes to operator profitability, the train timetabling problem has been a widely studied subject in academic circles. Still, the existing models in the literature, for the most part, fail to sufficiently take into account train capacity, fleet size, and vehicle circulation. As a contribution to bridging this research gap, this study mainly focuses on the train timetabling problem in a congested urban rail corridor to adapt to dynamic behavior of passenger demand subject to operational and resource constraints. A nonlinear programming model is formulated to devise timetables with a view to minimizing the average waiting time per passenger. In the model, the congestion is represented by some passengers who may not be able to take the first incoming train due to limited train capacity. To evaluate the effectiveness of the proposed approach, a case study is performed on a metro line in Istanbul. According to study results, the optimized demand-oriented train timetable proved to be more advantageous when compared to its periodical counterpart prepared by traffic planners. Finally, a sensitivity analysis is conducted to attain the best trade-off between passenger satisfaction and operation cost.