遗传算法优化支持向量机的城市交通状态识别  

Urban Traffic State Recognition Based on Genetic Algorithm Optimized Support Vector Machine

作　　者：李巧茹 郝恩强[1] 陈亮 范忠国 杨文伟[1] LI Qiaoru;HAO Enqiang;CHEN Liang;FAN Zhongguo;YANG Wenwei(School of Civil Engineering and Transportation,Hebei University of Technology,Tianjin 300401,China;Smart Infrastructure Research Institute,Hebei University of Technology,Tianjin 300401,China)

机构地区：[1]河北工业大学土木与交通学院,天津300401 [2]河北工业大学智慧基础设施研究院,天津300401

出　　处：《重庆交通大学学报（自然科学版）》2020年第8期1-5,13,共6页Journal of Chongqing Jiaotong University(Natural Science)

基　　金：国家自然科学基金项目(51678212);河北省高等学校科学技术研究项目(QN2018231)。

摘　　要：城市交通状态识别是智能交通控制、诱导和协同系统的基础。为提高支持向量机(support vector machine,SVM)在城市交通状态识别研究方面的泛化能力,将遗传算法(genetic algorithm,GA)与支持向量机相结合,利用遗传算法全局搜索优势对支持向量机的关键参数——惩罚系数C和核函数参数σ进行优化,建立基于遗传算法优化支持向量机(GA-SVM)的城市交通状态识别模型,并在MATLAB平台下进行实例验证。研究结果表明:相较于SVM模型,GA-SVM模型克服了依靠经验确定参数方法的缺点,识别精度提高3.75%,即模型可更好地识别城市交通状态。Urban traffic state recognition is the foundation of the intelligent traffic control,induction and coordination system.In order to improve the generalization ability of support vector machine in urban traffic state recognition,Genetic Algorithm(GA) and Support Vector Machine(SVM) were combined,and the key parameters of the support vector machine—the penalty coefficient C and the kernel function parameters σ were optimized by using the global search advantages of genetic algorithm.The urban traffic state recognition model based on genetic algorithm optimized support vector machine(GA-SVM) was established and verified by an example in MATLAB platform.The research results show that:compared with the SVM model,the GA-SVM model overcomes the shortcomings of determining the parameter method by means of experience,and the recognition accuracy is improved by 3.75%,that is to mean,the proposed model can better identify the urban traffic state.

关 键 词：交通运输工程 遗传算法 支持向量机 城市道路交通 交通状态 模式识别 

分 类 号：U491.1[交通运输工程—交通运输规划与管理]