Energy-Saving Metro Train Timetable Rescheduling Model Considering ATO Profiles and Dynamic Passenger Flow

Hou, Zhuopu; Dong, Hairong; Gao, Shigen; Nicholson, Gemma; Chen, Lei; Roberts, Clive

doi:10.1109/tits.2019.2906483

Cited by 61 publications

(27 citation statements)

References 35 publications

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“…For large disruptions, adjustments to train schedules, rolling stock circulation plans and crew assignment plans are usually required [ 5 ]. To eliminate the impacts of disruptions, studies usually formulate exact mixed-integer linear programming (MILP) train rescheduling models, which can provide optimal solutions by reducing the difference between the upper and lower bounds of an objective function [ 6 ]. However, train rescheduling usually requires a high standard of computational efficiency for real-time operations [ 7 ].…”

Section: Literature Reviewmentioning

confidence: 99%

Decisions on train rescheduling and locomotive assignment during the COVID-19 outbreak: A case of the Beijing-Tianjin intercity railway

Kang

Xiao

Sun

et al. 2022

Decision Support Systems

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Travel restriction measures have been widely implemented to curb the continued spread of COVID-19 during the Chinese Lunar New Year celebrations. Many operation lines and train schedules of China's railway were either heavily adjusted or canceled. In this study, a mixed-integer linear programming model and a two-step solution algorithm were developed to handle such large-scale adjustments. The formulation considers a flexible time window for each operation line and locomotive traction operations, and minimizes the number of locomotives utilized with their total idle time for train rescheduling and locomotive assignment, respectively. The solution algorithm determines the minimum locomotive fleet size based on the optimal train rescheduling results; it then reduces the traction idle time of locomotives. In response to the uncertainty of COVID-19, two tailored approaches were also designed to recover and remove operation lines, which can insert and cut operation lines based on the results of locomotive assignment. Finally, we conducted a case study of the Beijing-Tianjin intercity railway from the start of the COVID-19 outbreak in December 2019 to the recovery of operations.

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Section: Literature Reviewmentioning

confidence: 99%

Decisions on train rescheduling and locomotive assignment during the COVID-19 outbreak: A case of the Beijing-Tianjin intercity railway

Kang

Xiao

Sun

et al. 2022

Decision Support Systems

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“…en, they present an integer programming model and design an allocation algorithm to obtain the optimal schedule. Hou et al [29] presented a mixed integer programming model to solve the metro train timetable rescheduling problem, which jointly optimizes the energy consumption and the total train delay.…”

Section: Literature Reviewmentioning

confidence: 99%

“…A er investigating the existing literature, most of them [20-22, 26, 28, 29] have contributed to energy-e cient driving using the timetable optimization method to obtain energy-saving timetables that are conducive to reducing energy consumption. e decision variables selected in the existing literature on train energy-saving driving optimization are usually running time [20][21][22] or dwelling time [26,28,29]. e decision variable chosen in this paper is the dwelling time.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Some studies also consider the impact of transport service quality [13,16,21]. Furthermore, Table 1 shows that the current solution methodologies to solve energy e ciency problems can be divided into three categories: commercial optimization so ware [20,29], heuristic algorithms [8,16,26], and simulation methods [17]. e optimization models applied in the studies listed in Table 1 are very complex.…”

Section: Literature Reviewmentioning

confidence: 99%

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Integrated Optimization on Energy Saving and Quality of Service of Urban Rail Transit System

Peng

Wen

et al. 2020

Journal of Advanced Transportation

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Optimizing to increase the utilization ratio of regenerative braking energy reduces energy consumption, and can be done without increasing the deviation of train running time in one circle. The latter entails that the train timetable is upheld, which guarantees that the demand for passenger transport services is met and the quality of services in the urban rail transit system is maintained. This study proposes a multi-objective optimization model for urban railways with timetable optimization to minimize the total energy consumption of trains while maximizing the quality of service. To this end, we apply the principles and ideas of calculus to reduce the power of the velocity in the train energy consumption model. This greatly simplifies the complexity of the optimization model. Then, considering the conflicting requirements of decision-makers, weight factors are added to the objective functions to reflect decision-makers’ preferences for energy-saving and the quality of service. We adopt the nondominated sorting genetic algorithm-II (NSGA-II) to solve the proposed model. A practical case study of the Yizhuang urban railway line in Beijing is conducted to verify the effectiveness of the proposed model and evaluate the advantages of the optimal energy saving timetable (OEST) in comparison to the optimal quality of service timetable (OQOST). The results showed that the OEST reduced total energy consumption by 8.72% but increased the deviation of trains running time in one circle by 728 s. The total energy consumption was reduced by 6.09%, but there was no increase in the deviation of train running time in one circle with the OQOST.

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“…Different from these two types of traditional methods, the TTR method studied in this paper aims to reschedule train timetable after disturbances occur to reduce traction energy consumption. Hou et al [23] developed a mixedinteger programming (MIP) model to solve a metro train timetable rescheduling problem, which aims to jointly optimize the total train delay, the number of stranded passengers, and the energy consumption of trains. Zhao et al [24] implemented three search methods, namely, enhanced brute force (EBF), ant colony optimization (ACO), and Genetic Algorithm (GA), to minimize energy consumption and delay after being disturbed.…”

Section: Introductionmentioning

confidence: 99%

A Real-Time Train Timetable Rescheduling Method Based on Deep Learning for Metro Systems Energy Optimization under Random Disturbances

Liao

Zhang

et al. 2020

Journal of Advanced Transportation

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Considering that uncertain dwell disturbances often occur at metro stations, researchers have proposed many methods for solving the train timetable rescheduling (TTR) problem. This paper proposes a Modified Genetic Algorithm-Gate Recurrent Unit (MGA-GRU) method, which is a real-time TTR method based on deep learning. The proposed method takes the Gate Recurrent Unit (GRU) network as the decision network and uses the results produced by the Modified Genetic Algorithm (MGA) as the training set of the decision network. A well-trained decision network can provide effective solutions in real time after random disturbances occur, in order to optimize the net traction energy consumption of trains in metro systems. Based on the Shanghai Metro Line One (SML1) pilot network, this paper establishes a comprehensive model of the metro system as a training and testing environment to verify the energy-saving effect and real-time performance of the proposed method in solving the TTR problem. The experimental results show that in the two-train metro system, the three-train metro system, and the five-train metro system, the MGA-GRU method can save an average of energy by 4.45%, 6.16%, and 7.19%, while the average decision time is only 0.15 s, 0.27 s, and 0.33 s, respectively.

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Energy-Saving Metro Train Timetable Rescheduling Model Considering ATO Profiles and Dynamic Passenger Flow

Cited by 61 publications

References 35 publications

Decisions on train rescheduling and locomotive assignment during the COVID-19 outbreak: A case of the Beijing-Tianjin intercity railway

Decisions on train rescheduling and locomotive assignment during the COVID-19 outbreak: A case of the Beijing-Tianjin intercity railway

Integrated Optimization on Energy Saving and Quality of Service of Urban Rail Transit System

A Real-Time Train Timetable Rescheduling Method Based on Deep Learning for Metro Systems Energy Optimization under Random Disturbances

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