Intelligent Train Operation Algorithms for Subway by Expert System and Reinforcement Learning

Yin, Jianqin; Chen, Dewang; Li, Lingxi

doi:10.1109/tits.2014.2320757

Cited by 153 publications

(54 citation statements)

References 36 publications

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“…Lu [11] performed a comparative analysis among GA, DP (dynamic programming), and ACO (ant colony optimization) and suggested that the robustness could be improved by combining different algorithms. Yin et al [27] presented two intelligent train operation algorithms based on expert systems and reinforcement learning without precise train model information and offline optimized speed profiles. Domínguez et al [28] used multi-objective particle swarm optimization to collaboratively optimize the running time, train speed, and energy consumption.…”

Section: Mathematical Methods and Optimal Theorymentioning

confidence: 99%

Energy-Efficient Speed Profile Approximation: An Optimal Switching Region-Based Approach with Adaptive Resolution

Yang

Jia

et al. 2016

Energies

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Speed profile optimization plays an important role in optimal train control. Considering the characteristics of an electrical locomotive with regenerative braking, this paper proposes a new algorithm for target speed profile approximation. This paper makes the following three contributions: First, it proves that under a certain calculation precision, there is an optimal coast-brake switching region-not just a point where the train should be switched from coasting mode to braking mode. This is very useful in engineering applications. Second, the paper analyzes the influence of regenerative braking on the optimal coast-brake switching region and proposes an approximate linear relationship between the optimal switching region and the regeneration efficiency. Third, the paper presents an average speed equivalent algorithm for local speed profile optimization in steep sections. In addition, this paper simplifies the proof of the optimality of smooth running on a non-steep track in two steps. The effects on energy consumption from two important factors (optimal coast and running time) are systematically analyzed. Extensive simulations verify these points of view and demonstrate that the proposed approximation approach is computationally efficient and achieves sufficient engineering accuracy.

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Section: Mathematical Methods and Optimal Theorymentioning

confidence: 99%

Energy-Efficient Speed Profile Approximation: An Optimal Switching Region-Based Approach with Adaptive Resolution

Yang

Jia

et al. 2016

Energies

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“…From [5] and [9], the subway train operation needs to meet multiple objectives. In detail, it should maintain the safety of the train such that the speed of the train cannot exceed certain limits, reduce the energy consumption, and keep high punctuality and good riding comfort for passengers.…”

Section: A Definition Of Symbolsmentioning

confidence: 99%

“…To address the train operation problems, with one train in a fixed segment, Yin et al [9] emphasized the importance of intelligent control for urban metro systems and developed two train control algorithms based on an expert system and reinforcement learning. Nevertheless, the timetable is assumed to be constant in this study, which means that the driving process is not affected by any uncertain factor.…”

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confidence: 99%

Efficient Real-Time Train Operation Algorithms With Uncertain Passenger Demands

Yin

Chen

Yang

et al. 2016

IEEE Trans. Intell. Transport. Syst.

Self Cite

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The majority of existing studies in subway train operations focus on timetable optimization and vehicle tracking methods, which may be infeasible with disturbances in actual operations. To deal with uncertain passenger demands and realize real-time train operations (RTOs) satisfying multiobjectives, including overspeed protection, punctuality, riding comfort, and energy consumption, this paper proposes two RTO algorithms via expert knowledge and an online learning approach. The first RTO algorithm is developed by a knowledge-based system to ensure the multiple objectives with a constant timetable. Then, by considering uncertain passenger demand at each station and random running time errors, we convert the train operation problem into a Markov decision process with nondeterministic state transition probabilities in which the aim is to minimize the reward for both the total time delay and energy consumption in a subway line. After designing policy, reward, and transition probability, we develop an integrated train operation (ITO) algorithm based on Q-learning to realize RTOs with online adjusting the timetable. Finally, we present some numerical examples to test the proposed algorithms with real detected data in the Yizhuang Line of Beijing Subway. The results indicate that, taking the multiple objectives into account, the RTO algorithm outperforms both manual driving and automatic train operations. In addition, the ITO algorithm is capable of dealing with uncertain disturbances, keeping the total time delay within 2 s and reducing the energy consumption.

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“…For this reason, the use of expert systems is increasing and also being used in a number of sectors of our social and technological life. In fact, in those the use of expert systems is becoming quite a significant aspect during problem solving and decision support [9][10][11][12][13][14][15][16][17][18].…”

Section: Introductionmentioning

confidence: 99%

Designing a Rule Based Expert Systems for Contact Lenses Patients

Aydilek¹,

Gümüşçü²

2018

IJITCS

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Abstract-Expert systems that bring facts and valuable experiences together and make some deductions possible. Expression of relevant knowledge and experience in these structures may be in a set of rules. Learning problems are valid with expert systems. Therefore, they cannot add new rules and information automatically by themselves. Rules are created by human experts on the way and added upon the system. Classification datasets are collections of data commonly used in machine learning that contain and classify the previously obtained experiences. In this study, rules were obtained by using Part, NNge, Prism rule classifier algorithms, and a knowledge base of expert systems was systematically created to achieve enrichment. Enrichment and rule deduction process needs careful and sensitive attention. A combined methodology and study was revealed during this sensitive process. In this context, studies were conducted on five widely used datasets. It was aimed to reduce the redundant, conflicting, subsumed and circular rules in order to create a consistent and complete knowledge base. In this way, a methodology was developed to establish more powerful and richer contents of knowledge base that have higher quality.

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Intelligent Train Operation Algorithms for Subway by Expert System and Reinforcement Learning

Cited by 153 publications

References 36 publications

Energy-Efficient Speed Profile Approximation: An Optimal Switching Region-Based Approach with Adaptive Resolution

Energy-Efficient Speed Profile Approximation: An Optimal Switching Region-Based Approach with Adaptive Resolution

Efficient Real-Time Train Operation Algorithms With Uncertain Passenger Demands

Designing a Rule Based Expert Systems for Contact Lenses Patients

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