Dynamic passenger demand oriented metro train scheduling with energy-efficiency and waiting time minimization: Mixed-integer linear programming approaches

Yin, Jianqin; Yang, Lixing; Tang, Tao; Gao, Ziyou; Ran, Bin

doi:10.1016/j.trb.2017.01.001

Cited by 257 publications

(93 citation statements)

References 60 publications

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“…Stanojević et al [5] developed an integer programming approach for determining an optimal timetable for a single, one-way track linking two major stations. Yin et al [6] established linear mixed-integer programming models for timetable optimization to decrease the energy consumption and simultaneously to shorten the passengers' waiting time. Gao et al [7] investigated a rescheduling problem on a metro line in an overcrowded situation after disruptions.…”

Section: Literature Reviewmentioning

confidence: 99%

“…In constraint (6), represents the difference between the departure time of the connecting train and the arrival time of transfer passengers from the feeder train at the platform of train . It is determined by the departure time of the connecting train , , the arrival time of the feeder train , , and the passengers' walking time from platform of to the one of , walk .…”

Section: Assumptionsmentioning

confidence: 99%

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Last-Train Timetabling under Transfer Demand Uncertainty: Mean-Variance Model and Heuristic Solution

Yang

Gao

et al. 2017

Journal of Advanced Transportation

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Traditional models of timetable generation for last trains do not account for the fact that decision-maker (DM) often incorporates transfer demand variability within his/her decision-making process. This study aims to develop such a model with particular consideration of the decision-makers' risk preferences in subway systems under uncertainty. First, we formulate an optimization model for last-train timetabling based on mean-variance (MV) theory that explicitly considers two significant factors including the number of successful transfer passengers and the running time of last trains. Then, we add the mean-variance risk measure into the model to generate timetables by adjusting the last trains' departure times and running times for each line. Furthermore, we normalize two heterogeneous terms of the risk measure to provide assistance in getting reasonable results. Due to the complexity of MV model, we design a tabu search (TS) algorithm with specifically designed operators to solve the proposed timetabling problem. Through computational experiments involving the Beijing subway system, we demonstrate the computational efficiency of the proposed MV model and the heuristic approach.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Assumptionsmentioning

confidence: 99%

Last-Train Timetabling under Transfer Demand Uncertainty: Mean-Variance Model and Heuristic Solution

Yang

Gao

et al. 2017

Journal of Advanced Transportation

Self Cite

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“…The metro line layouts and the basic problem descriptions are the same as in [5]. Notation used throughout this paper is listed in Table 1.…”

Section: Model Formulationmentioning

confidence: 99%

“…The tradeoffs between energy consumption and train travel time are observed in many studies [5,6]. Generally, longer segment travel time requires less energy consumption, because more coasting and less traction controls are adopted.…”

Section: Introductionmentioning

confidence: 99%

A Numerical Optimization Technique for Scheduling Non-uniform Metro Trains under Time-varying Passenger Demands

Huang¹,

Li²

2017

dtcse

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Urban rail transit is costly and often overloaded. Thus, the motivation of this paper is to develop specific operating strategies to reduce cost and improve service level. This paper proposed a numerical optimization technique to optimize non-uniform metro train timetables with time-varying passenger demands, where the total monetary cost of time and energy is minimized. The problem was formulated with a constrained, non-smooth, nonlinear programming model, and transformed into an unconstrained one by the proposed Augmented Lagrangian (AL) algorithm. Then, the Pattern Search (PS) algorithm was designed to search for an optimal solution in each iteration step of AL. The case study shows that the proposed technique is effective to obtain non-uniform timetables that significantly outperform uniform ones reducing cost at most 34.92%, and provides decision-making support.

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“…Meanwhile, since many metro systems need government subsidies to cover operating expenses, the planners focus more on operating cost than on passenger factors when designing a timetable [2]. This motivates some recent studies to combine these two conflicting objectives in the train timetabling problem [3,4].…”

Section: Introductionmentioning

confidence: 99%

Metro Timetabling for Time-Varying Passenger Demand and Congestion at Stations

Huang

Schonfeld

2018

Journal of Advanced Transportation

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For the train timetabling problem (TTP) in a metro system, the operator-oriented and passenger-oriented objectives are both important but partly conflicting. This paper aims to minimize both objectives by considering frequency (in the line planning stage) and train cost (in the vehicle scheduling stage). Time-varying passenger demand and train capacity are considered in a nonsmooth, nonconvex programming model, which is transformed into a mixed integer programming model with a discrete time-space graph (DTSG). A novel dwell time determining process considering congestion at stations is proposed, which turns the dwell times into dependent variables. In the solution approach, we decompose the TTP into a subproblem for optimizing segment travel times (OST) and a subproblem for optimizing departure headways from the shunting yard (OH). Branch-and-bound and frequency determining algorithms are designed to solve OST. A novel rolling optimization algorithm is designed to solve OH. The numerical experiments include case studies on a short metro line and Beijing Metro Line 4, as well as sensitivity analyses. The results demonstrate the predictive ability of the model, verify the effectiveness and efficiency of the proposed approach, and provide practical insights for different scenarios, which can be used for decision-making support in daily operations.

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Dynamic passenger demand oriented metro train scheduling with energy-efficiency and waiting time minimization: Mixed-integer linear programming approaches

Cited by 257 publications

References 60 publications

Last-Train Timetabling under Transfer Demand Uncertainty: Mean-Variance Model and Heuristic Solution

Last-Train Timetabling under Transfer Demand Uncertainty: Mean-Variance Model and Heuristic Solution

A Numerical Optimization Technique for Scheduling Non-uniform Metro Trains under Time-varying Passenger Demands

Metro Timetabling for Time-Varying Passenger Demand and Congestion at Stations

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