Efficient Real-Time Train Operation Algorithms With Uncertain Passenger Demands

Yin, Jianqin; Chen, Dewang; Yang, Lixing; Tang, Tao; Ran, Bin

doi:10.1109/tits.2015.2478403

Cited by 33 publications

(17 citation statements)

References 44 publications

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“…Yin et al [15] developed a stochastic programming model for metro train rescheduling problem in order to jointly reduce the time delay of affected passengers, their total traveling time, and operational costs of trains. Yin et al [16] converted the train operation problem into a Markov decision process with nondeterministic state transition probabilities to minimize the cost for both the total time delay and energy consumption in a subway line. The second stream of literature related to this paper is on risk analysis and management.…”

Section: Literature Reviewmentioning

confidence: 99%

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%

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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“…The agent learns and optimizes the driving strategies to maximize the cumulative reward from the environment [5] . In [6,7], a model-free algorithm based on Q-learning was proposed to adjust the speed trajectory in real time, which coped with the train delays caused by the uncertain passenger demands of the subway system. [8] formulated the traction/braking force of the subway train as the continuous action space and developed two intelligent operation algorithms using the Deep Deterministic Policy Gradient (DDPG) and the Normalized Advantage Function (NAF) without relying on the accurate train model.…”

Section: Introductionmentioning

confidence: 99%

Q-learning for the speed trajectory optimization of the freight train

Lin

Liang

Yan

et al. 2022

International Conference on Frontiers of Traffic and Transportation Engineering (FTTE 2022)

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The train speed trajectory optimization (TSTO) aims at finding the optimal speed trajectory considering the safety, energy efficiency, punctuality and stopping accuracy. From the perspective of mitigating the greenhouse effect, it’s quite significant to study the TSTO problem. This paper proposed an optimization algorithm based on Reinforcement Learning (RL). Firstly, a global optimization model using RL was established. In the model, the control sequence including the control regimes and their switching points was taken as the state. The optimization objectives were taken as the reward function. The adjustment of the position of the switching points in the control sequence was taken as the decision space of the agent. Secondly, an adjustment method of the control sequence based on the deep Q-learning and embedding matrix was proposed. The training data was sampled using the experience replay. The optimal control sequence was obtained through the iterative training of the neural network. Finally, the optimization algorithm based on RL was compared with the driving strategies based on the Pontryagin’s Maximum Principle (PMP) and the field test data. The results show that the energy consumption of the proposed algorithm is reduced by 0.16% in comparison with that of the PMP, which proves that the proposed method can be applied to the multi-objective optimization of the train operation. Comparing with the field test data, the energy consumption of the optimization algorithm is reduced by 4.89%, which demonstrates that the proposed method can be used to guide the drivers to drive the freight train energy-efficiently.

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“…Although researchers have conducted extensive research on train timetable in the past few decades, trains in metro systems are still often subject to unexpected disturbances, such as a sudden increase in passenger flow, unexpected accidents, and unplanned parking [13,14]. To solve this kind of problem, researchers have proposed many train timetable rescheduling (TTR) methods [15][16][17][18][19].…”

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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Efficient Real-Time Train Operation Algorithms With Uncertain Passenger Demands

Cited by 33 publications

References 44 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

Q-learning for the speed trajectory optimization of the freight train

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

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