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

Autor: Wei Li, Kai Xian, Jiateng Yin, Dewang Chen
Jazyk: angličtina
Rok vydání: 2019
Předmět:
Zdroj: Journal of Advanced Transportation, Vol 2019 (2019)
Druh dokumentu: article
ISSN: 0197-6729
2042-3195
DOI: 10.1155/2019/3072495
Popis: 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.
Databáze: Directory of Open Access Journals
Externí odkaz:
Nepřihlášeným uživatelům se plný text nezobrazuje