Control model of variable speed limit based on finite horizon Markov decision-making
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摘要: 分析了高速公路主线可变限速控制的作用, 研究了现有的限速方法, 将高速公路主线可变限速控制过程看作是离散时间的马尔可夫决策过程, 提出基于强化学习与有限阶段马尔可夫决策的可变限速控制模型, 通过与交通环境的交互学习进行模型的动态调整。采用有限阶段向后递归迭代的算法对模型进行求解, 运用Paramics仿真软件对长吉高速公路全程进行仿真。仿真结果表明: 在平均限速值低于设计时速6.25%的情况下, 平均流量不仅没有降低反而增加了3.20%。可见, 该模型可以有效提高交通流量, 改善高速公路主线的交通状况。Abstract: The function of variable speed limit(VSL) on expressway mainline was analyzed, and the existing methods of speed limit were studied.The control process of VSL was taken as Markov decision-making process of discrete time.A model of VSL based on reinforcement learning and finite horizon Markov decision-making was proposed.The model was dynamically adjusted through interacting with traffic environment, and solved by using finite horizon backward recursive iterative algorithm.The traffic environment of Chang-Ji Expressway was simulated by using Paramics.Analysis result shows that average traffic volume doesn't reduce, but increases by 3.20% when average limit speed decreases by 6.25% compared with design speed.So the model is feasible to increase traffic volume and improve traffic condition on expressway mainline effectively.
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[1] 中投顾问. 2011-2015年中国高速公路行业投资分析及前景预测报告[R]. 深圳: 中投顾问, 2011.CIConsulting. 2011-2015 China highway industry invest ment analysis and forecast report[R]. Shenzhen: CIConsulting. (in Chinese) [2] ABDEL-ATY M, CUNNINGHAMRJ, GAYAH V V, et al. Dynamic variable speed limit strategies for real-time crash risk reduction on freeways[J]. Transportation Research Record, 2008(2078): 108-116. [3] LIN P, KANG KP, CHANG G L. Exploring the effectiveness of variable speed limit controls on highway work-zone operations[J]. Journal of Intelligent Transportation Systems, 2004, 8(3): 155-168. doi: 10.1080/15472450490492851 [4] 陈大山. 高速公路主线可变限速控制研究[D]. 西安: 长安大学, 2009.CHEN Da-shan. Variable speed control of highway[D]. Xi'an: Changan University, 2009. (in Chinese) [5] VAN DEN HOOGEN E, SMULDERS S, HEIDEMIJ A. Control by variable speed signs: results of the dutch experiment[C]∥IEEE. Seventh International Conference on Road Traffic Monitoring and Control. London: IEEE, 1994: 145-149. [6] HEGYI A, DE SCHUTTER B, HELLENDOORN H. Model predictive control for optimal coordination of ramp metering and variable speed limits[J]. Transportation Research Part C: Emerging Technologies, 2005, 13(3): 185-209. doi: 10.1016/j.trc.2004.08.001 [7] LYLES R W, TAYLOR WC, LAVANSIRI D, et al. A field test and evaluation of variable speed limits in work zones[C]∥TRB. TRB Annual Meeting Proceedings. Washington DC: TRB, 2004: 1-21. [8] ALLABY P, HELLINGA B, BULLOCK M. Variable speed limits: safety and operational impacts of a candidate control strategy for freeway applications[J]. IEEE Transactions on Intelligent Transportation Systems, 2006, 8(4): 671-680. [9] 梁新荣, 刘智勇, 孙德山, 等. 基于支持向量机的高速公路限速控制[J]. 计算机工程与应用, 2005, 41(34): 178-180. https://www.cnki.com.cn/Article/CJFDTOTAL-JSGG200534056.htmLIANG Xin-rong, LIU Zhi-yong, SUN De-shan, et al. Control speed limitation on freeway based on support vectormachine[J]. Computer Engineering and Applications, 2005, 41(34): 178-180. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-JSGG200534056.htm [10] 干宏程, 孙立军. 高速公路可变限速控制技术研究[J]. 交通科技, 2004(6): 91-93. https://www.cnki.com.cn/Article/CJFDTOTAL-SKQB200406033.htmGAN Hong-cheng, SUN Li-jun. A study on the variable speed limits technology for freeways[J]. Transportation Science & Technology, 2004(6): 91-93. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-SKQB200406033.htm [11] 梁新荣, 刘智勇, 毛宗源. 高速公路模糊神经网络限速控制与仿真研究[J]. 公路交通科技, 2005, 22(11): 123-125, 129. https://www.cnki.com.cn/Article/CJFDTOTAL-GLJK200511031.htmLIANG Xin-rong, LIU Zhi-yong, MAO Zong-yuan. Neurofuzzy control for speed limit on expressway and its simulation study[J]. Journal of Highway and Transportation Researchand Development, 2005, 22(11): 123-125, 129. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-GLJK200511031.htm [12] 张汝波, 顾国昌, 刘照德, 等. 强化学习理论、算法及应用[J]. 控制理论与应用, 2000, 17(5): 637-642. doi: 10.3969/j.issn.1000-8152.2000.05.002ZHANG Ru-bo, GU Guo-chang, LIU Zhao-de, et al. Reinforcement learning theory, algorithms and its application[J]. Control Theory and Applications, 2000, 17(5): 637-642. (in Chinese) doi: 10.3969/j.issn.1000-8152.2000.05.002 [13] 黄炳强. 强化学习方法及其应用研究[D]. 上海: 上海交通大学, 2007.HUANG Bing-qiang. Research on the reinforcement learning method and its application[D]. Shanghai: Shanghai Jiaotong University, 2007. (in Chinese) [14] 郑宇. 分层强化学习算法及其应用研究[D]. 北京: 北京交通大学, 2009.ZHENG Yu. Research on hierarchy reinforcement learning algorithm and its application[D]. Beijing: Beijing Jiaotong University, 2009. (in Chinese) -
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