Stochastic optimization of an urban rail timetable under time‐dependent and uncertain demand

Shakibayifar, Masoud; Hassannayebi, Erfan; Jafary, Hossein; Sajedinejad, Arman

doi:10.1002/asmb.2268

Cited by 35 publications

(7 citation statements)

References 63 publications

(83 reference statements)

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“…On this basis, with the goal of minimizing the total waiting time of passengers, Barrena et al [21] and Canca et al [22] established a nonlinear integer programming model to optimize train departure and arrival time. Based on the volatility of passenger arrival rate, Shakibayifar et al [23] established a two-stage stochastic train timetable optimization model. Considering the total traveling time of passengers, Zhou and Zhong [24] established a multiobjective 0-1 mixed-integer programming model to minimize the running time and waiting time of passengers.…”

Section: Introductionmentioning

confidence: 99%

Optimizing Train Timetable Based on Departure Time Preference of Passengers for High-Speed Rails

Huang

Niu

Gao

et al. 2021

Journal of Advanced Transportation

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Passengers would like to choose the most suitable train based on their travel preferences, expenses, and train timetable in the high-speed railway corridor. Meanwhile, the railway department will constantly adjust the train timetable according to the distribution of passenger flows during a day to achieve the optimal operation cost and energy consumption saving plan. The question is how to meet the differential travel needs of passengers and achieve sustainable goals of service providers. Therefore, it is necessary to design a demand-oriented and environment-friendly high-speed railway timetable. This paper formulates the optimization of train timetable for a given high-speed railway corridor, which is based on the interests of both passengers and transportation department. In particular, a traveling time-space network with virtual departure arc is constructed to analyze generalized travel costs of passengers of each origin-destination (OD), and bilevel programming model is used to optimize the problem. The upper integer programming model regards the minimization of the operating cost, which is simplified to the minimum traveling time of total trains, as the goal. The lower level is a user equilibrium model which arranges each OD passenger flow to different trains. A general advanced metaheuristic algorithm embedded with the Frank–Wolfe method is designed to implement the bilevel programming model. Finally, a real-world numerical experiment is conducted to verify the effectiveness of both the model and the algorithm.

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Section: Introductionmentioning

confidence: 99%

Optimizing Train Timetable Based on Departure Time Preference of Passengers for High-Speed Rails

Huang

Niu

Gao

et al. 2021

Journal of Advanced Transportation

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“…Selection process is so crucial that a wide range of data‐driven operational problems cannot be solved without it. In stochastic optimization, one has to decide which hypothesis best characterizes the distribution of the random variables 1 . In variance reduction procedure, particularly in high‐dimensional context, one must tune hyper‐parameters to determine the strength of penalty and select which set of features to leverage for estimation 2 .…”

Section: Introductionmentioning

confidence: 99%

Cross‐estimation for decision selection

2020

Appl Stoch Models Bus & Ind

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We propose a data-driven procedure, cross-estimation for decision selection (CrEDS), to choose from an abundance of off-the-shelf statistical models or computer algorithms at a decision-maker's disposal. CrEDS combines the ideas of cross-validation (CV) and local smoothing, a nonparametric statistical technique. We demonstrate the power of CrEDS with five numerical experiments in inventory and revenue management problems, ranging from low to high dimensional and from exogenous to endogenous. We also conduct a case study using an auto-lending data. CrEDS performs favorably compared to other existing selection criteria and provides a practical framework for a broad range of optimal decision selection problems.

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“…Principally, the satisfaction of passengers is directly affected by their travel and waiting times [8]. One of the primary sources of passenger dissatisfaction is unpredicted variations in passenger flows [9,10]. The process of timetable adjustment supplying the passengers' demand provides solutions with high sensitivity to the stochastic variations and requires an effective and robust solution to be applied [11].…”

Section: Introductionmentioning

confidence: 99%

A Multi-objective Optimization Model for Robust Skip-Stop Scheduling with Earliness and Tardiness Penalties

2019

Self Cite

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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 twostage 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.

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Stochastic optimization of an urban rail timetable under time‐dependent and uncertain demand

Cited by 35 publications

References 63 publications

Optimizing Train Timetable Based on Departure Time Preference of Passengers for High-Speed Rails

Optimizing Train Timetable Based on Departure Time Preference of Passengers for High-Speed Rails

Cross‐estimation for decision selection

A Multi-objective Optimization Model for Robust Skip-Stop Scheduling with Earliness and Tardiness Penalties

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