Abstract
Inefficient transport systems impose extra travel time for travelers, cause dissatisfaction and reduce service levels. In this study, the demand-oriented train scheduling problem is addressed using a robust skip-stop method under uncertain arrival rates during peak hours. This paper presents alternative mathematical models, including a two-stage scenario-based stochastic programming model and two robust optimization models, to minimize the total travel time of passengers and their waiting time at stations. The modeling framework accounts for the design and implementation of robust skip-stop schedules with earliness and tardiness penalties. As a case study, each of the developed models is implemented on line No. 5 of the Tehran metro, and the results are compared. To validate the skip-stop schedules, the values of the stochastic solution and the expected value of perfect information are calculated. In addition, a sensitivity analysis is conducted to test the performance of the model under different scenarios. According to the obtained results, having perfect information can reduce up to 16% of the value of the weighted objective function. The proposed skip-stop method has been shown to save about 5% in total travel time and 49% in weighted objective function, which is a summation of travel times and waiting times as against regular all-stop service. The value of stochastic solutions is about 21% of the value of the weighted objective function, which shows that the stochastic model demonstrates better performance than the deterministic model.
Highlights
Providing affordable and efficient transportation services to users is a vital role in rail transportation systems [1]
The proposed optimization models were solved by CPLEX 12.9 solver, which uses several state-of-the-art MILP techniques, e.g., branch and bound (B&B) and branch and cut (B&C) algorithms
This study addressed the joint train scheduling and train stop planning problem by considering skip-stop services during peak hours in rapid rail transit systems
Summary
Providing affordable and efficient transportation services to users is a vital role in rail transportation systems [1]. Skip-stop service is one of the best-known strategies for both increasing the operational speed and decreasing the passenger waiting cost It is commonly adopted in rapid rail transit lines, which allow trains to skip some low demand stations to reduce the train traveling time [14]. Even though extensive literature exists on timetabling in the rail transportation system, there have not been enough studies directed toward the optimization of robust skip-stop strategies with earliness and tardiness criteria under the stochastic demand. The present research focuses on multiobjective optimization of train timetables for minimum passenger cost in terms of travel time and schedule deviation. This study develops alternative stochastic programming models for generating robust skip-stop schedules with earliness and tardiness criteria for rapid rail transit systems, with an illustration based on the Tehran metro system. The concluding remarks are provided, and suggestions for future research are presented
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