A Subway Train Timetable Optimization Approach Based on Energy-Efficient Operation Strategy

Su, Shuai; Li, Xiang; Tang, Tao; Gao, Ziyou

doi:10.1109/tits.2013.2244885

Cited by 344 publications

(140 citation statements)

References 30 publications

Supporting

Mentioning

138

Contrasting

Unclassified

Order By: Relevance

“…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.

View full text Add to dashboard Cite

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.

show abstract

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.

View full text Add to dashboard Cite

show abstract

“…Moreover, as for the specific metro system such as SMLO, only four phases of acceleration, coasting, braking and dwelling are needed in normal operation conditions because the metro system always has a relatively short station spacing (normally 1 to 2 km) [3,42,46]. Note that this driving strategy modeling is a simplified model that is based on the real case of SMLO.…”

Section: Compensational Driving Strategy Algorithmmentioning

confidence: 99%

“…Li [41] combined the timetable optimization method and speed profile optimization method to achieve a better net energy consumption performance. Su [42] proposed a mathematical model to maximize the utilization of recovery energy. Then he also integrated the fleet size and cycle time decision, distribution of cycle time and driving strategy optimization to find a globally optimal schedule [43].…”

Section: Introductionmentioning

confidence: 99%

An Integrated Energy-Efficient Operation Methodology for Metro Systems Based on a Real Case of Shanghai Metro Line One

Gong

Zhang

et al. 2014

Energies

View full text Add to dashboard Cite

Metro systems are one of the most important transportation systems in people's lives. Due to the huge amount of energy it consumes every day, highly-efficient operation of a metro system will lead to significant energy savings. In this paper, a new integrated Energy-efficient Operation Methodology (EOM) for metro systems is proposed and validated. Compared with other energy saving methods, EOM does not incur additional cost. In addition, it provides solutions to the frequent disturbance problems in the metro systems. EOM can be divided into two parts: Timetable Optimization (TO) and Compensational Driving Strategy Algorithm (CDSA). First, to get a basic energy-saving effect, a genetic algorithm is used to modify the dwell time of each stop to obtain the most optimal energy-efficient timetable. Then, in order to save additional energy when disturbances happen, a novel CDSA algorithm is formulated and proposed based on the foregoing method. To validate the correctness and effectiveness of the energy-savings possible with EOM, a real case of Shanghai Metro Line One (SMLO) is studied, where EOM was applied. The result shows that a significant amount of energy can be saved by using EOM. OPEN ACCESSEnergies 2014, 7 7306

show abstract

“…Li et al after considering the energy consumption, train running time and other objectives, put forward a multi-objective train scheduling model, then developed a timetable for train energy-efficient [13]. In 2013, Shuai Su et al used the numerical algorithm to distribute the train running time, and then reduce the train energy consumption between the two stations by optimizing the speed profile [14].On the basis of multi-objective optimization method and theory, Jin Yu builds up the train running process optimization model with consideration of train punctuality, train parking accuracy and the train energy consumption. Finally, the improved particle swarm algorithm is used to solve the problem [15].…”

Section: Introductionmentioning

confidence: 99%

The Research of Train Energy-Efficient Operation Strategy Based on Multi-Objective Optimization

Yunzhen¹,

An²

2018

Proceedings of the 2017 2nd International Conference on Electrical, Control and Automation Engineering (ECAE 2017)

View full text Add to dashboard Cite

Abstract-In order to reducing the energy consumption of the train running between the stations, ensuring punctuality and the comfort of the passengers, this paper studies the train energyefficient operation strategy. After taking account of the slope and the speed limit of the line, the model of multi-objective optimization train energy-efficient is established based on train energy consumption, running time and passenger comfort. The improved multi-objective genetic algorithm (MOGA) is used to optimize the target speed sequence to obtain the operation strategy of the train. Different from previous multi-objective optimization, the energy-efficient driving optimization method is realized by considering automatic train operation's (ATO) double-level control structure, slope equivalent strategy, and Pareto optimization in this paper. Based on the actual line data and vehicle parameters of Yizhuang line in Beijing subway, the optimization method is verified by simulation. The simulation results show that, after using the improved multi-objective genetic algorithm, the energy consumption and running time of the train in Yizhuang train station are obviously decreased, and after the train comfort is measured, the rate of change in acceleration or deceleration meet the requirements of passenger experience needs. It can be seen that the proposed algorithm can effectively reduce the energy consumption of the train, ensure the accuracy of the running time and improve the comfort of the passengers.

show abstract

A Subway Train Timetable Optimization Approach Based on Energy-Efficient Operation Strategy

Cited by 344 publications

References 30 publications

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

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

An Integrated Energy-Efficient Operation Methodology for Metro Systems Based on a Real Case of Shanghai Metro Line One

The Research of Train Energy-Efficient Operation Strategy Based on Multi-Objective Optimization

Contact Info

Product

Resources

About