Linguistic Dynamic Analysis of Traffic Light Timing Design within the Time-varying Universe
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摘要: 城市道路不同时刻的车流量变化很大，建立与车流量变化相适应的红绿灯动态配时模型有利于缓解交通拥堵，减少出行者的等待时间.本文通过综合时变论域、平行控制理论、语言动力系统（Linguistic dynamic system，LDS），提出了一种新的红绿灯控制方法.该方法以红绿灯不同时刻周期时长所形成的序列为时变论域，由各相位的排队长度确定对应的通行序列与时长，得到时变论域下红绿灯配时方案.该方案形成一个由实时车流数据驱动的动态模糊规则库来对红绿灯配时周期及相位通行序列与时长进行动态调整，进而形成红绿灯配时演化过程的语言动力学轨迹，最后通过实例验证该方案的有效性.Abstract: Urban traffic flow always changes sharply at different moments. A dynamic timing design model adapting to the change of traffic flow may alleviate traffic congestion and reduce the waiting time of travelers. In this paper, a new method for traffic light control is presented by synthesizing time-varying universe, parallel control and linguistic dynamic systems (LDS). In the method, the time-varying universe is constructed by the series of cyclic lengths of traffic lights at different times, and the corresponding traffic sequences and durations are decided by their respective phase queue lengths, thus a timing scheme based on time-varying universe is obtained. By this timing scheme, a dynamic fuzzy rule base is formed to adjust the cyclic length and constituent sequence light time dynamically. At the same time the dynamic fuzzy rule base which is driven by real time traffic data adjusts the traffic sequence and duration. Then the linguistic dynamic orbits of time-design for traffic light are analyzed within the time-varying universe. Finally, an example is given to verify the validity of this method.1) 本文责任编委 徐昕
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表 1 交叉路口相关设定
Table 1 Related notations
红绿灯编号 受控车道 通行方向 $TL_1$ $Road A$_2、3、4 北向南直行 $TL_2$ $Road A$_1 北向南左转 $TL_3$ $Road B_5$、6、7 南向北直行 $TL_4$ $Road C_9$ 西向东左转 
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[1] Zhou J, Chen C L Philip, Chen L, Zhao W. A user-customizable urban traffic information collection method based on wireless sensor networks. IEEE Transactions on Intelligent Transportation Systems, 2013, 14(3):1119-1128 doi: 10.1109/TITS.2013.2252902 [2] Zhou J, Chen C L Philip, Chen L, Li H X. A collaborative fuzzy clustering algorithm in distributed network environments. IEEE Transactions on Fuzzy Systems, 2014, 22(6):1443-1456 doi: 10.1109/TFUZZ.2013.2294205 [3] Zhang J P, Wang F Y, Wang K F, Lin W H, Xu X, Chen C. Data-driven intelligent transportation systems:a survey. IEEE Transactions on Intelligent Transportation Systems, 2011, 12(4):1624-1639 doi: 10.1109/TITS.2011.2158001 [4] 翟高寿, 查建中, 鄂明成.集成智能城市交通控制系统体系结构的提出.系统工程理论与实践, 2000, 20(7):80-84 http://d.wanfangdata.com.cn/Thesis/Y392083Zhai Gao-Shou, Cha Jian-Zhong, E Ming-Cheng. The architecture of integrated intelligent urban traffic control system. System Engineering-Theory & Practice, 2000, 20(7):80-84 http://d.wanfangdata.com.cn/Thesis/Y392083 [5] 李盛春, 孔令江, 刘慕仁, 郑容森.智能交通灯对交叉路口交通流的影响.物理学报, 2009, 58(4):2266-2270 doi: 10.7498/aps.58.2266Li Sheng-Chun, Kong Ling-Jiang, Liu Mu-Ren, Zheng Rong-Sen. The effects of intelligent traffic light on the crossing traffic flow. Acta Physica Sinica, 2009, 58(4):2266-2270 doi: 10.7498/aps.58.2266 [6] Pappis C P, Mamdani E H. A fuzzy logic controller for a traffic junction. IEEE Transactions on Systems, Man, and Cybernetics, 1977, 7(10):707-717 doi: 10.1109/TSMC.1977.4309605 [7] Niittymaki J, Kosonen T, Nevala R. Fuzzy traffic signal control in major arterials. In:Proceedings of the 2001 IEEE Intelligent Transportation Systems. Oakland, CA, USA:IEEE, 2001. 520-531 http://ieeexplore.ieee.org/document/948681/ [8] Bi Y R, Srinivasan D, Lu X B, Sun Z, Zeng W L. Type-2 fuzzy multi-intersection traffic signal control with differential evolution optimization. Expert Systems with Applications, 2014, 41(16):7338-7349 doi: 10.1016/j.eswa.2014.06.022 [9] Balaji P G, Srinivasan D. Distributed multi-agent type-2 fuzzy architecture for urban traffic signal control. In:Proceedings of the 2009 IEEE International Conference on Fuzzy Systems. Jeju Island, South Korea:IEEE, 2009. 1627-1632 http://ieeexplore.ieee.org/document/5277360/ [10] Balaji P, Srinivasan D. Type-2 fuzzy logic based urban traffic management. Engineering Applications of Artificial Intelligence, 2011, 24(1):12-22 doi: 10.1016/j.engappai.2010.08.007 [11] Collotta M, Bello L L, Pau G. A novel approach for dynamic traffic lights management based on Wireless Sensor Networks and multiple fuzzy logic controllers. Expert Systems with Applications, 2015, 42(13):5403-5415 doi: 10.1016/j.eswa.2015.02.011 [12] Abbas M K, Karsiti M N, Napiah M, Samir B B, Al-Jemeli M. High accuracy traffic light controller for increasing the given green time utilization. Computers and Electrical Engineering, 2015, 41:40-51 doi: 10.1016/j.compeleceng.2014.12.011 [13] 陈洪, 陈森发.单路口交通实时模糊控制的一种方法.信息与控制, 1997, 26(3):227-233 http://kns.cnki.net/KCMS/detail/detail.aspx?filename=xxyk703.010&dbname=CJFD&dbcode=CJFQChen Hong, Chen Sen-Fa. A method for real time traffic fuzzy control of a single intersection. Information and Control, 1997, 26(3):227-233 http://kns.cnki.net/KCMS/detail/detail.aspx?filename=xxyk703.010&dbname=CJFD&dbcode=CJFQ [14] Li L, Lv Y S, Wang F Y. Traffic signal timing via deep reinforcement learning. IEEE/CAA Journal of Automatica Sinica, 2016, 3(3):247-254 doi: 10.1109/JAS.2016.7508798 [15] Li L, Wen D. Parallel Systems for traffic control:a rethinking. IEEE Transactions on Intelligent Transportation Systems, 2016, 17(4):1179-1182 doi: 10.1109/TITS.2015.2494625 [16] Abdi J, Moshiri B, Abdulhai B, Sedigh A K. Forecasting of short-term traffic-flow based on improved neurofuzzy models via emotional temporal difference learning algorithm. Engineering Applications of Artificial Intelligence, 2012, 25(5):1022-1042 doi: 10.1016/j.engappai.2011.09.011 [17] Abdi J, Moshiri B, Abdulhai B, Sedigh A K. Short-term traffic flow forecasting:parametric and nonparametric approaches via emotional temporal difference learning. Neural Computing and Applications, 2013, 23(1):141-159 doi: 10.1007/s00521-012-0977-3 [18] 丛冬栋, 王振家.基于FNNC的城市交通智能红绿灯控制系统.控制工程, 2003, 10(S):14-17 http://www.docin.com/p-1432556349.htmlCong Dong-Dong, Wang Zhen-Jia. Intelligent traffic light control system of the urban transportation based on FNNC. Control Engineering of China, 2003, 10(S):14-17 http://www.docin.com/p-1432556349.html [19] Bretherton D, Bodger M, Cowling J. SCOOT-managing congestion, control and communications. Traffic Engineering & Control, 2006, 47(3):88-92 http://www.nsfc.gov.cn/Portals/0/fj/fj20170118_03.xls [20] Wiering M, van Veenen J, Vreeken J, Koopman A. Intelligent Traffic Light Control, UU-CS-2004-029, Institute of Information and Computing Sciences, Utrecht University, Utrecht, Netherlands, 2004. [21] Box S, Waterson B. An automated signalized junction controller that learns strategies from a human expert. Engineering Applications of Artificial Intelligence, 2012, 25(1):107-118 doi: 10.1016/j.engappai.2011.09.008 [22] Box S, Waterson B. An automated signalized junction controller that learns strategies by temporal difference reinforcement learning. Engineering Applications of Artificial Intelligence, 2013, 26(1):652-659 doi: 10.1016/j.engappai.2012.02.013 [23] 刘脐锺, 李兵.基于改进遗传算法的区域交通信号优化控制.西华大学学报(自然科学版), 2014, 33(6):48-52 doi: 10.3969/j.issn.1673-159X.2014.06.0011Liu Qi-Zhong, Li Bing. The optimization control for regional traffic signal based on improved genetic algorithm. Journal of Xihua University (Nature Science), 2014, 33(6):48-52 doi: 10.3969/j.issn.1673-159X.2014.06.0011 [24] Bingham E. Reinforcement learning in neurofuzzy traffic signal control. European Journal of Operational Research, 2001, 131(2):232-241 doi: 10.1016/S0377-2217(00)00123-5 [25] 王飞跃.平行控制:数据驱动的计算控制方法.自动化学报, 2013, 39(4):293-302 http://www.aas.net.cn/CN/abstract/abstract17915.shtmlWang Fei-Yue. Parallel control:a method for data-driven and computational control. Acta Automatica Sinica, 2013, 39(4):293-302 http://www.aas.net.cn/CN/abstract/abstract17915.shtml [26] 王飞跃.平行系统方法与复杂系统的管理和控制.控制与决策, 2004, 19(5):485-489, 514 http://www.cqvip.com/QK/91549X/200405/10335921.htmlWang Fei-Yue. Parallel system methods for management and control of complex systems. Control and Decision, 2004, 19(5):485-489, 514 http://www.cqvip.com/QK/91549X/200405/10335921.html [27] Wang F Y. Parallel control and management for intelligent transportation systems:concepts, architectures, and applications. IEEE Transactions on Intelligent Transportation Systems, 2010, 11(3):630-638 doi: 10.1109/TITS.2010.2060218 [28] 莫红.时变论域下的语言动力学轨迹.自动化学报, 2012, 38(10):1585-1594 http://www.aas.net.cn/CN/abstract/abstract17767.shtmlMo Hong. Linguistic dynamic orbits in the time varying universe of discourse. Acta Automatica Sinica, 2012, 38(10):1585-1594 http://www.aas.net.cn/CN/abstract/abstract17767.shtml [29] 王飞跃.词计算和语言动力学系统的基本问题和研究.自动化学报, 2005, 31(6):844-852 http://www.aas.net.cn/CN/abstract/abstract15941.shtmlWang Fei-Yue. Fundamental issues in research of computing with words and linguistic dynamic system. Acta Automatica Sinica, 2005, 31(6):844-852 http://www.aas.net.cn/CN/abstract/abstract15941.shtml [30] Zadeh L A. Fuzzy sets. Information and Control, 1965, 8(3):338-353 doi: 10.1016/S0019-9958(65)90241-X [31] 宁滨, 王飞跃, 董海荣, 李润梅, 文丁, 李莉.基于ACP方法的城市轨道交通平行系统体系研究.交通运输系统工程与信息, 2010, 10(6):22-28 http://kns.cnki.net/KCMS/detail/detail.aspx?filename=ysxt201006002&dbname=CJFD&dbcode=CJFQNing Bin, Wang Fei-Yue, Dong Hai-Rong, Li Run-Mei, Wen Ding, Li Li. Parallel systems for urban rail transportation based on ACP approach. Journal of Transportation Systems Engineering and Information Technology, 2010, 10(6):22-28 http://kns.cnki.net/KCMS/detail/detail.aspx?filename=ysxt201006002&dbname=CJFD&dbcode=CJFQ -
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