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    Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/141637


    Title: Q-Learning 與 Deep Q-Learning 於都市路邊停車位搜尋之研究
    Study on Q-Learning and Deep Q-Learning in Urban Roadside Parking Space Search
    Authors: 李俊毅
    Lee, Chun-Yee
    Contributors: 張宏慶
    Jang, Hung-Chin
    李俊毅
    Lee, Chun-Yee
    Keywords: 深度學習
    強化學習
    停車位搜尋
    Date: 2022
    Issue Date: 2022-09-02 15:04:38 (UTC+8)
    Abstract: 隨著近年物聯網、5G網路應用及深度學習等技術的發展。智慧城市的發展也逐漸受到重視，其中智慧交通更是政府主要的推廣的領域。對於停車位搜尋問題，目前最常見的做法是在每個停車位設置感測器，透過提供駕駛即時的停車位資訊令駕駛能找到合適的停車位，也因此近年停車位搜尋問題的相關研究多半著重在改善對於停車空位的偵測能力。雖然直接提供每個固定停車位的即時資訊，可以為駕駛選擇行駛路線提供幫助，但在使用者驅車前往該停車空位的過程，停車空位仍有先被其他駕駛使用的可能性。這會導致使用者必須在途中重新判斷並選擇行駛路線，而無法在原先估計的地點順利停車，形成欲透過提供停車位資訊間接解決停車問題時的盲點。為解決此問題，停車位搜尋問題的另一種研究方向是將重心放在找到合適的停車路徑而非鎖定特定的停車空位上。透過預測停車空位存在的機率，為使用者導航較高機率的停車行駛路線，進而減少車輛因尋找停車位導致徘徊的行車時間。
    由於目前台灣的實體環境適用於停車空位統計的相關資料較少，且設置大量相關感測器的成本高昂，短期難以實現大規模的建設。本研究擬以需要設置較少感測器的深度強化學習方法，Deep Q-Learning解決停車位搜尋問題，並加入及LSTM(Long Short-Term Memory)及GRU(Gated Recurrent Unit)神經網路模型提升深度強化學習模型中對於Q值估算的精確度。最終透過深度強化學習模型引導車輛行駛停車路線，達到降低停車所需行駛時間的目的。而缺乏適用於研究問題統計資料的問題，本研究將透過SUMO模擬器(Simulation of Urban MObility)，根據整體的停車頻率及車流量產生擬真的車流環境，以此作為學習模型的訓練資料及實驗資料。模擬資料將隨機產生一般車流與停車車流。一般車流是指目的地在停車範圍外不會停留於停車範圍內的車流，這類車流將以最短路徑在模擬環境中行經停車範圍。停車車流則是目的地設定在停車範圍內的車流，用以模擬實際車流環境中可能會在目標車輛搜尋停車路徑中停車的其他車輛。本研究旨在以模擬車流資料驗證使用深度強化學習解決停車位搜尋問題的有效性，並比較強化學習及深度學習方法在解決停車位搜尋問題的表現，評估不同深度學習方法在進一步解決停車問題上的效益。
    Reference: 1. Chase Dowling, Tanner Fiez, Lillian Ratliff, Baosen Zhang, “How Much Urban Traffic is Searching for Parking”, arXiv:1702.06156, Feb. 2017
    2. Asma Houissa, Dominique Barth, Nadège Faul, Thierry Mautor, “A Learning Algorithm to Minimize the Expectation Time of Finding a Parking Place In Urban Area”, 22nd IEEE Symposium on Computers and Communication: Workshops – ISUT, 2017
    3. Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Alex Graves, Ioannis Antonoglou, Daan Wierstra Martin Riedmiller, “Playing Atari with Deep Reinforcement Learning”, DeepMind Technologies, Dec. 2013
    4. Shuguan Yang, Wei Ma, Xidong Pi, Sean Qian , “A Deep Learning Approach to Real-Time Parking Occupancy Prediction in Spatio-Temporal Networks Incorporating Multiple Spatio-Temporal Data Sources”, arXiv:1901.06758v5, May 2019
    5. B. Xu, O. Wolfson, J. Yang, L. Stenneth, P. S. Yu and P. C. Nelson, “Real-Time Street Parking Availability Estimation,” 2013 IEEE 14th International Conference on Mobile Data Management, 2013
    6. Zeng, C. Ma, K. Wang and Z. Cui, “Parking Occupancy Prediction Method Based on Multi Factors and Stacked GRU-LSTM”, in IEEE Access, vol. 10, pp. 47361-47370, 2022
    7. “HISTORY OF INTELLIGENT TRANSPORTATION SYSTEM”, U.S. Department of Transportation, 2021
    8. 台北市交通政策白皮書, 台北市政府交通局, 2018
    9. X. Fang, R. Xiang, L. Peng, H. Li and Y. Sun, “SAW: A Hybrid Prediction Model for Parking Occupancy Under the Environment of Lacking Real-Time Data”, IECON 2018 - 44th Annual Conference of the IEEE Industrial Electronics Society, 2018
    10. K. Kashihara, “Deep Q learning for traffic simulation in autonomous driving at a highway junction”, 2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2017
    11. M. Liu, J. Naoum-Sawaya, Y. Gu, F. Lecue and R. Shorten, “A Distributed Markovian Parking Assist System,” in IEEE Transactions on Intelligent Transportation Systems, vol. 20, no. 6, pp. 2230-2240, June 2019
    12. Gould Michael, Montoliu Raul, Torres-Sospedra Joaquín, Huerta Joaquín, “An Occupancy Simulator for a Smart Parking System: Developmental Design and Experimental Considerations”, ISPRS International Journal of Geo-Information; Basel Vol. 8, Iss. 5, 2019
    13. 自用小客車使用狀況調查報告, 中華民國交通部統計處, 2019
    14. T. Rajabioun and P. A. Ioannou, “On-Street and Off-Street Parking Availability Prediction Using Multivariate Spatiotemporal Models”, in IEEE Transactions on Intelligent Transportation Systems, vol. 16, no. 5, pp. 2913-2924, Oct. 2015
    15. F. Bock, S. Di Martino and A. Origlia, “Smart Parking: Using a Crowd of Taxis to Sense On-Street Parking Space Availability”, in IEEE Transactions on Intelligent Transportation Systems, vol. 21, no. 2, pp. 496-508, Feb. 2020
    16. I. Aydin, M. Karakose and E. Karakose, “A navigation and reservation based smart parking platform using genetic optimization for smart cities”, 2017 5th International Istanbul Smart Grid and Cities Congress and Fair (ICSG), 2017
    17. 鄔德傳, 臺北市推動智慧停車之挑戰與對策, 國家文官學院, T&D飛訊第266期, 2020 May
    18. 臺北市路邊停車格位圖層, 政府資料開放平臺, 臺北市停車管理工程處, 2022
    19. L. Xiangdong, C. Yuefeng, C. Gang and X. Zengwei, “Prediction of short-term available parking space using LSTM model”, 2019 14th International Conference on Computer Science & Education (ICCSE), 2019
    20. Clare Chen, Vincent Ying, Dillon Laird, “Deep Q-Learning with Recurrent Neural Networks”, 2016
    21. J. Yu, K. Zhang and L. Peng, “Integrated Prediction of Regional Traffic Situation Based on Multi-Task Spatial-Temporal Network”, IECON 2021 – 47th Annual Conference of the IEEE Industrial Electronics Society, 2021
    22. L. Zheng, X. Xiao, B. Sun, D. Mei and B. Peng, “Short-Term Parking Demand Prediction Method Based on Variable Prediction Interval”, in IEEE Access, vol. 8, pp. 58594-58602, 2020
    23. Shudong Yang, Xueying Yu, Ying Zhou, “LSTM and GRU neural network performance comparison study”, International Workshop on Electronic Communication and Artificial Intelligence (IWECAI), 2020
    24. J. Lin, S. -Y. Chen, C. -Y. Chang and G. Chen, “SPA: Smart Parking Algorithm Based on Driver Behavior and Parking Traffic Predictions”, in IEEE Access, vol. 7, pp. 34275-34288, 2019
    25. X. Ye, J. Wang, T. Wang, X. Yan, Q. Ye and J. Chen, “Short-Term Prediction of Available Parking Space Based on Machine Learning Approaches”, in IEEE Access, vol. 8, pp. 174530-174541, 2020
    Description: 碩士
    國立政治大學
    資訊科學系
    106753020
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0106753020
    Data Type: thesis
    DOI: 10.6814/NCCU202201502
    Appears in Collections:[Department of Computer Science ] Theses

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