Developing Train Station Parking Algorithms: New Frameworks Based on Fuzzy Reinforcement Learning

Author:

Li Wei12,Xian Kai2,Yin Jiateng3ORCID,Chen Dewang4ORCID

Affiliation:

1. School of Traffic and Transportation, Beijing Jiaotong University, Beijing, 100044, China

2. Beijing Transport Institute, No. 9, LiuLiQiao South Lane, Fengtai District, Beijing, China

3. State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing, 100044, China

4. College of Mathematics and Computer Science, Fuzhou University, Fuzhou 350116, China

Abstract

Train station parking (TSP) accuracy is important to enhance the efficiency of train operation and the safety of passengers for urban rail transit. However, TSP is always subject to a series of uncertain factors such as extreme weather and uncertain conditions of rail track resistances. To increase the parking accuracy, robustness, and self-learning ability, we propose new train station parking frameworks by using the reinforcement learning (RL) theory combined with the information of balises. Three algorithms were developed, involving a stochastic optimal selection algorithm (SOSA), a Q-learning algorithm (QLA), and a fuzzy function based Q-learning algorithm (FQLA) in order to reduce the parking error in urban rail transit. Meanwhile, five braking rates are adopted as the action vector of the three algorithms and some statistical indices are developed to evaluate parking errors. Simulation results based on real-world data show that the parking errors of the three algorithms are all within the ±30cm, which meet the requirement of urban rail transit.

Funder

National Natural Science Foundation of China

Publisher

Hindawi Limited

Subject

Strategy and Management,Computer Science Applications,Mechanical Engineering,Economics and Econometrics,Automotive Engineering

Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Intelligent train stopping control for railways: A deep learning approach;IET Intelligent Transport Systems;2023-06

2. Accurate Parking Control for Urban Rail Trains via Robust Adaptive Backstepping Approach;IEEE Transactions on Intelligent Transportation Systems;2022-11

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