A Cooperative Scheduling Model for Timetable Optimization in Subway Systems

Yang, Xin; Li, Xiang; Gao, Ziyou; Wang, Hongwei; Tang, Tao

doi:10.1109/tits.2012.2219620

Cited by 279 publications

(191 citation statements)

References 37 publications

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“…Relevant studies employ the overlapping time of accelerating trains and braking trains to access the utilization rate of regenerative energy [20]. Yang et al [21] and Zhao et al [22] considered the waiting time and the total passenger time in the optimization of train timetable to increase the utilization of regenerative energy. These papers aim to maximize the utilization of regenerative energy, but ignore the electrical safety of the system in the management of regenerative energy.…”

Section: Groundmentioning

confidence: 99%

Maximum Safety Regenerative Power Tracking for DC Traction Power Systems

Zhang

et al. 2017

Energies

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Direct current (DC) traction power systems are widely used in metro transport systems, with running rails usually being used as return conductors. When traction current flows through the running rails, a potential voltage known as "rail potential" is generated between the rails and ground. Currently, abnormal rises of rail potential exist in many railway lines during the operation of railway systems. Excessively high rail potentials pose a threat to human life and to devices connected to the rails. In this paper, the effect of regenerative power distribution on rail potential is analyzed. Maximum safety regenerative power tracking is proposed for the control of maximum absolute rail potential and energy consumption during the operation of DC traction power systems. The dwell time of multiple trains at each station and the trigger voltage of the regenerative energy absorbing device (READ) are optimized based on an improved particle swarm optimization (PSO) algorithm to manage the distribution of regenerative power. In this way, the maximum absolute rail potential and energy consumption of DC traction power systems can be reduced. The operation data of Guangzhou Metro Line 2 are used in the simulations, and the results show that the scheme can reduce the maximum absolute rail potential and energy consumption effectively and guarantee the safety in energy saving of DC traction power systems.

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Section: Groundmentioning

confidence: 99%

Maximum Safety Regenerative Power Tracking for DC Traction Power Systems

Zhang

et al. 2017

Energies

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“…In a number of studies, schedule optimization of trains was performed so that it reduces energy consumption or energy loss. They include proposing an algorithm which distributes travel times of the trains for the most efficient energy consumption (Su et al, 2013), developing a cooperative scheduling model to increase simultaneous accelerates and brakes of the consecutive trains (Nasri et al, 2010;Yang et al, 2013) and applying the genetic algorithm to decrease the simultaneous acceleration of trains in order to avoid maximum traction power of the system (Chen et al, 2005).…”

Section: Related Workmentioning

confidence: 99%

Modelling Temporal Schedule of Urban Trains Using Agent-Based Simulation and Nsga2-Based Multiobjective Optimization Approaches

Sahelgozin

Alimohammadi

2015

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Increasing distances between locations of residence and services leads to a large number of daily commutes in urban areas. Developing subway systems has been taken into consideration of transportation managers as a response to this huge amount of travel demands. In developments of subway infrastructures, representing a temporal schedule for trains is an important task; because an appropriately designed timetable decreases Total passenger travel times, Total Operation Costs and Energy Consumption of trains. Since these variables are not positively correlated, subway scheduling is considered as a multi-criteria optimization problem. Therefore, proposing a proper solution for subway scheduling has been always a controversial issue. On the other hand, research on a phenomenon requires a summarized representation of the real world that is known as Model. In this study, it is attempted to model temporal schedule of urban trains that can be applied in Multi-Criteria Subway Schedule Optimization (MCSSO) problems. At first, a conceptual framework is represented for MCSSO. Then, an agent-based simulation environment is implemented to perform Sensitivity Analysis (SA) that is used to extract the interrelations between the framework components. These interrelations is then taken into account in order to construct the proposed model. In order to evaluate performance of the model in MCSSO problems, Tehran subway line no. 1 is considered as the case study. Results of the study show that the model was able to generate an acceptable distribution of Pareto-optimal solutions which are applicable in the real situations while solving a MCSSO is the goal. Also, the accuracy of the model in representing the operation of subway systems was significant.

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“…There are two main levels of train energy-efficient operation approaches. A recent emerging research interest is in the field of regenerative energy utilization [1][2][3][4][5][6], which focuses on developing a timetable including the dwelling time at stations and running time at sections (between two adjacent stations) in order to improve the utilization of regenerative energy by synchronizing the operations of accelerating and braking trains [7]. Compared with the upper level of timetable optimization, the lower level of energy-efficient control strategy design at sections has long attracted widespread attention [8][9][10][11][12][13][14][15][16][17][18][19][20][21] to calculate the speed profile with minimum tractive energy consumption under the timetable constraints [22].…”

Section: Introductionmentioning

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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A Cooperative Scheduling Model for Timetable Optimization in Subway Systems

Cited by 279 publications

References 37 publications

Maximum Safety Regenerative Power Tracking for DC Traction Power Systems

Maximum Safety Regenerative Power Tracking for DC Traction Power Systems

Modelling Temporal Schedule of Urban Trains Using Agent-Based Simulation and Nsga2-Based Multiobjective Optimization Approaches

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

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