Optimizing urban rail timetable under time-dependent demand and oversaturated conditions

Niu, Huimin; Zhou, Xuesong

doi:10.1016/j.trc.2013.08.016

Cited by 428 publications

(209 citation statements)

References 21 publications

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“…The passenger arriving and alighting at the metro station for a given period 0 ,   final tt can be modelled by a time-dependent origin-destination table [44][45][46].…”

Section: Passenger Characteristicsmentioning

confidence: 99%

Optimizing the Energy-Efficient Metro Train Timetable and Control Strategy in Off-Peak Hours with Uncertain Passenger Demands

Feng

Liu

et al. 2017

Energies

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How to reduce the energy consumption of metro trains by optimizing both the timetable and control strategy is a major focus. Due to the complexity and difficulty of the combinatorial operation problem, the commonly-used method to optimize the train operation problem is based on an unchanged dwelling time for all trains at a specific station. Here, we develop a simulation-based method to design an energy-efficient train control strategy under the optimized timetable constraints, which assign the dwelling time margin to the running time. This time margin is caused by dynamically uncertain passenger demands in off-peak hours. Firstly, we formulate a dwelling time calculation model to minimize the passenger boarding and alighting time. Secondly, we design an optimal train control strategy with fixed time and develop a time-based model to describe mass-belt train movement. Finally, based on this simulation module, we present numerical examples based on the real-world operation data from the Beijing metro Line 2, in which the energy consumption of one train can be reduced by 21.9%. These results support the usefulness of the proposed approach.

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“…The passenger arriving and alighting at the metro station for a given period 0 ,   final tt can be modelled by a time-dependent origin-destination table [44][45][46].…”

Section: Passenger Characteristicsmentioning

confidence: 99%

Optimizing the Energy-Efficient Metro Train Timetable and Control Strategy in Off-Peak Hours with Uncertain Passenger Demands

Feng

Liu

et al. 2017

Energies

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“…With reference to the existing studies, 13,14,16,17 a bidirectional urban rail line with only one rail yard located at one end is considered in our studies, whose layout is shown in Figure 1. The station on the end where the yard is located is called the start terminal, while the station on the other end is called return terminal.…”

Section: Problem Statementmentioning

confidence: 99%

Pareto optimal train scheduling for urban rail transit using generalized particle swarm optimization

Chu

Zhang

Chen

et al. 2016

Advances in Mechanical Engineering

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In urban rail transport, train timetable plays a crucial role, whose quality determines the whole system's performance to a large extent. In practical urban rail operation, two contradictive aspects-service quality and operation cost-should be considered during train scheduling. A good train timetable should achieve considerable service quality with as little operation cost as possible. Previously, many studies have been conducted specific to urban rail train scheduling, although most of them do not put enough emphasis on its multi-objective nature. In this article, therefore, Pareto optimal urban rail train scheduling which can give more instruction to practical operation is studied. First, referring to some existing studies, the problem is reasonably defined, which takes time-dependent origin-destination demand as the input and aims at minimizing the passengers' total travel time and the number of used train stocks. Then, an efficient iteration algorithm and a valid train stock assignment procedure are designed to calculate the passengers' total travel time and required train stock number, respectively. On that basis, the studied problem is reasonably formulated as a bi-objective optimization model and a Pareto-based particle swarm optimization procedure is designed to solve it. Finally, with two different scaled urban rail lines, the whole methodology is illustrated and the algorithm is tested.

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“…Researchers have devoted themselves to optimize the train schedule to assist help dispatchers make better decisions meeting passengers and operators expectations. The objective functions are most concentrated on enhancing passengers satisfaction in terms of degree of crowdedness [1][2][3], passengers waiting time [2,4,5] and passengers travel time [6,7] or reducing operation costs in terms of energy consumption [6,[8][9][10], trains [11,12], train travel time [13], etc. Not only does the urban rail transit systems expand fast, but also the passenger demand rises significantly.…”

Section: Introductionmentioning

confidence: 99%

A Short Turning Strategy for Train Scheduling Optimization in an Urban Rail Transit Line: The Case of Beijing Subway Line 4

Zhang

Wang

et al. 2018

Journal of Advanced Transportation

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In urban rail transit systems, train scheduling plays an important role in improving the transport capacity to alleviate the urban traffic pressure of huge passenger demand and reducing the operation costs for operators. This paper considers the train scheduling with short turning strategy for an urban rail transit line with multiple depots. In addition, the utilization of trains is also taken into consideration. First, we develop a mixed integer nonlinear programming (MINLP) model for the train scheduling, where short turning train services and full-length train services are optimized based on the predefined headway obtained by the passenger demand analysis. The MINLP model is then transformed into a mixed integer linear programming (MILP) model according to several transformation properties. The resulting MILP problem can be solved efficiently by existing solvers, e.g., CPLEX. Two case studies with different scales are constructed to assess the performance of train schedules with the short turning strategy based on the data of Beijing Subway line 4. The simulation results show that the reduction of the utilization of trains is about 20.69%.

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Optimizing urban rail timetable under time-dependent demand and oversaturated conditions

Cited by 428 publications

References 21 publications

Optimizing the Energy-Efficient Metro Train Timetable and Control Strategy in Off-Peak Hours with Uncertain Passenger Demands

Optimizing the Energy-Efficient Metro Train Timetable and Control Strategy in Off-Peak Hours with Uncertain Passenger Demands

Pareto optimal train scheduling for urban rail transit using generalized particle swarm optimization

A Short Turning Strategy for Train Scheduling Optimization in an Urban Rail Transit Line: The Case of Beijing Subway Line 4

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