基于ε-支持向量回归理论的区域交通信号智能控制

doi:10.11772/j.issn.1001-9081.2015.05.1361

计算机应用 ›› 2015, Vol. 35 ›› Issue (5): 1361-1366.DOI: 10.11772/j.issn.1001-9081.2015.05.1361

基于ε-支持向量回归理论的区域交通信号智能控制

游子毅^1,2, 陈世国^1,2, 王义^1,2

1. 贵州师范大学物理与电子科学学院, 贵阳 550001;
2. 贵州省教育厅汽车电子技术特色重点实验室, 贵阳 550001

收稿日期:2014-12-10 修回日期:2015-01-21 出版日期:2015-05-10 发布日期:2015-05-14
通讯作者: 游子毅
作者简介:游子毅(1982-),男,贵州贵阳人,副教授,博士,主要研究方向:无线网络、安全协议; 陈世国(1969-),男,贵州贵阳人,教授,博士,主要研究方向:网络信息处理; 王义(1957-), 男,贵州贵阳人,教授,博士,主要研究方向:智能网络控制.
基金资助:
国家自然科学基金资助项目(61262007);贵州省科学技术基金资助项目 (黔科合J字[2013]2222号);贵州师范大学博士科研启动基金资助项目.

Intelligent control based on ε-support vector regression theory for regional traffic signal system

YOU Ziyi^1,2, CHEN Shiguo^1,2, WANG Yi^1,2

1. School of Physics and Electronic Science, Guizhou Normal University, Guiyang Guizhou 550001, China;
2. Key Laboratory of Special Automotive Electronics Technology of the Education Department of Guizhou Province, Guiyang Guzhou 550001, China

Received:2014-12-10 Revised:2015-01-21 Online:2015-05-10 Published:2015-05-14

摘要/Abstract

摘要：

城市交通信号控制是当前智能交通领域的研究热点之一.针对区域交通信号协同控制的实时性和准确性,提出一种基于ε-支持向量回归(SVR)非线性回归理论的智能控制方法(ICSRTS).该方法在无线传感网络结构的基础上结合已有的数据汇聚算法,并采用分簇策略将区域交通控制系统建模成一类集成信息调度与控制的离散切换系统.在离散切换系统中,不仅考虑了数据包传输的网络时延和丢包率,而且观测器利用改进的ε-SVR训练方法实现对多数据源融合的交通信号状态的在线预测并通过控制器进行总体协调控制.运用Lyapunov 函数方法验证了该系统的渐近稳定性及其可调度性. 仿真结果表明,ICSRTS方法相比普通模糊神经网络控制和普通ε-SVR预测算法在交叉口平均延误时间方面具有较好的性能.因此,该方法能实时、有效地对区域交通信号进行协调控制,从而减少了区域内的交通拥堵和能源消耗.

关键词: 交通信号系统, 区域交通控制, 交通状态预测, ε-支持向量回归理论, Lyapunov函数

Abstract:

Intelligent control of urban traffic signal is an important element of intelligent transportation system. In order to meet the real-time and accuracy for the regional traffic signal coordinated control, this paper presented an Intelligent Control Strategy for Regional Traffic Signal (ICSRTS) based on ε-SVR (Support Vector Regression) nonlinear regression theory. Combining with the existing data aggregation algorithm, ICSRTS was based on the wireless sensor network structure, and it adopted the clustering strategy to create a model of discrete switching system, which integrated the information scheduling and control for the regional traffic system. In the discrete switching system, the network delay and packet loss rate for data transmission were considered, furthermore, the observer used the modified ε-SVR theory to realize the online prediction of the multi-source data based traffic state, then the controller carried out coordination control of the overall traffic signal. The asymptotic stability of discrete switching system was analyzed using Lyapunov function. Simulation results show that ICSRTS has better performance in the intersection average delay time compared with ordinary fuzzy neural control and ordinary ε-SVR prediction algorithm. Therefore, this method can realize the regional traffic signal coordinated control in real-time and effectively, and reduce the area of traffic congestion and energy consumption.

Key words: traffic signal system, regional traffic control, traffic state prediction, ε-SVR (Support Vector Regression) theory, Lyapunov function

中图分类号:

游子毅, 陈世国, 王义. 基于ε-支持向量回归理论的区域交通信号智能控制[J]. 计算机应用, 2015, 35(5): 1361-1366.

YOU Ziyi, CHEN Shiguo, WANG Yi. Intelligent control based on ε-support vector regression theory for regional traffic signal system[J]. Journal of Computer Applications, 2015, 35(5): 1361-1366.

参考文献

[1] YANG Z. An introduction to intelligent transportation system[M]. 2nd ed. Beijing: China Communications Press, 2009:12-34.(杨兆升. 智能运输系统概论[M].2版. 北京: 人民交通出版社, 2009:12-34.)
[2] TRIGUERO I, DERRAC J, GARCIA S, et al. A taxonomy and experimental study on prototype generation for nearest neighbor classification[J]. IEEE Transactions on Systems, Man, and Cybernetics- Part C: Applications and Reviews, 2012, 42(1):86-100.
[3] SUI L, LI W, SHI J, et al. The analysis and application actuality of traffic signal control system- SCOOT and ACTRA in Beijing[J].Road Traffic and Safety,2007,7(2):10-13. (隋莉颖,李威,石建军,等.SCOOT和ACTRA信号控制系统分析[J]. 道路交通与安全,2007,7(2):10-13.)
[4] LIANG C, FAN B, HAN Y. Coordination control method of regional traffic flow [J]. Journal of Traffic and Transportation Engineering,2011(3):112-117.(梁超,范炳全,韩印.区域交通流协调控制方法[J].交通运输工程学报,2011(3): 112-117.)
[5] NAIR B M, CAI J. A fuzzy logic controller for isolated signalized intersection with traffic abnormality considered[C]// Proceedings of the 2007 IEEE Intelligent Vehicles Symposium. Piscataway: IEEE, 2007: 1229-1233.
[6] GAO J, LI J, CHEN Y, et al. Optimize design and simulation of traffic signal two level fuzzy control system[J]. Journal of Beijing University of Technology, 2009,35(1):19-24.(高俊侠,李建更,陈阳舟,等.交通信号2级模糊控制系统的优化设计与仿真[J].北京工业大学学报,2009,35(1): 19-24.)
[7] LIN D, ZHENG C, CHEN S, et al. Study of traffic delay predicting at signalized intersection based on BP neural network[J].Journal of Dalian Jiaotong University, 2013, 34(4):53-56.(林得刚,郑长江,陈淑燕,等. 基于神经网络的信号交叉口进口车道交通延误预测[J]. 大连交通大学学报, 2013, 34(4): 53-56.)
[8] LIU Y. Study of traffic flow prediction based on fuzzy rough neural network[J].Journal of Hainan Normal University: Natural Science,2012,25(4):386-388.(刘琰.基于模糊粗糙神经网络的交通流研究[J]. 海南师范大学学报:自然科学版, 2012, 25(4): 386-388.)
[9] HAN M, WANG Y. Multivariate time series online predictor with Kalman filter trained reservoir[J].Acta Automatica Sinica,2010,36(1):169-173.(韩敏, 王亚楠. 基于Kalman 滤波的储备池多元时间序列在线预报器[J]. 自动化学报, 2010, 36(1): 169-173.)
[10] GAO X, LIU Y, WANG Q, et al. Prediction of short-term traffic flow with on-line support vector regression algorithm[J].Journal of Shandong University of Science and Technology: Natural Science,2011,30(1):78-82.(高学辉,刘艳忠,王巧芝,等.基于在线支持向量回归算法的短时交通流预测[J].山东科技大学学报:自然科学版,2011,30(1):78-82.)
[11] CRISTIANINI N, SHAWE-TAYLOR J. An Introduction to support vector machines and other kernel-based learning methods[M]. LI G, WANG M, ZENG H, translated. Beijing: Electronic Industry Press, 2004:82-105.(CRISTIANINI N, SHAWE-TAYLOR J.支持向量机导论[M]. 李国正,王猛,曾华军,译.北京:电子工业出版社,2004:82-105.)
[12] YOU Z, ZHANG J, CHEN S. A data aggregation scheme based on wireless sensor networks and its application research in intelligent transportation system[J].Application Research of Computers,2014,31(6):1719-1722.(游子毅,章俊华,陈世国.基于无线传感网络的数据融合方法及其在智能交通系统中的应用[J].计算机应用研究,2014,31(6):1719-1722.)
[13] ALPAYDIN E. Introduction to machine learning[M]. FAN M, translated. Beijing: China Machine Press,2009:9-20.(ALPAYDIN E. 机器学习导论[M]. 范明,译.北京:机械工业出版社, 2009:9-20.)
[14] WANG X, WANG J. A Survey on support vector machines training and testing algorithms[J].Computer Engineering and Applications,2004, 21(13):75-78.(王晓丹, 王积勤. 支持向量机训练和实现算法综述[J]. 计算机工程与应用, 2004, 21(13):75-78.)
[15] BAI S, JIANG Z, WANG T, et al. Application of online SVR on the dynamic liquid level soft sensing[C]// Proceedings of the 2013 25th Chinese Control and Decision Conference. Piscataway: IEEE,2013: 3003-3007.
[16] HE K, LI X, SCHICK B, et al. On-road video delivery with integrated heterogeneous wireless networks [J]. Ad Hoc Networks, 2013, 11(7): 1992-2001.
[17] OSSENBRUGGEN P, LAFLAMME E. Time series analysis and models of freeway performance[J]. Journal of Transportation Engineering, 2012, 138(8): 1030-1039.
[18] HUANG Y, CUI B. Stochastic stability and stabilization of networked control systems with uncertain data losses and long delay[J].Journal of Huaihai Institute of Technology: Natural Science Edition,2013,22(4):22-27.(黄逸彤,崔宝同.具有随机长时延与丢包的网络控制系统的随机稳定与镇定[J]. 淮海工学院学报:自然科学版,2013,22(4):22-27.)
[19] CHEN X. The theory of linear system[M]. Beijing: China Machine Press,2011: 1-30.(陈晓平. 线性系统理论[M]. 北京:机械工业出版社,2011: 1-30.)
[20] [LOU X. Urban road network signal design optimization considering the impact of intersections［J].Journal of Transportation Engineering and Information,2013,11(1):108-110.(楼小明.考虑交叉口影响的城市路网信号优化设计问题研究[J]. 交通运输工程与信息学报,2013,11(1):108-110.)
[21] LI X. The traffic flow and the signal control of intersections research with Matlab[D]. Wuhan: Central China Normal University, 2008: 17-39.(李星.基于Matlab的交通流及交叉口信号控制的仿真研究[D]. 武汉: 华中师范大学, 2008:17-39.)

基于ε-支持向量回归理论的区域交通信号智能控制

Intelligent control based on ε-support vector regression theory for regional traffic signal system

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 3

编辑推荐

Metrics

[1]	贾付金, 蒋沅. 基于反步法非线性纯反馈系统的控制器设计[J]. 计算机应用, 2018, 38(1): 300-304.
[2]	宗鑫, 崔艳. 具有随机通信时延的二阶多智能体系统的一致性控制[J]. 计算机应用, 2015, 35(5): 1358-1360.
[3]	张扬名刘国荣刘洞波刘欢. 基于Lyapunov方法和快速终端滑模的轨迹跟踪控制[J]. 计算机应用, 2012, 32(11): 3243-3246.