基于支持向量机的高速公路事件检测
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摘要
针对交通领域中的事件检测(无事件模式和事件模式)模式识别问题,描述了支持向量机(SVM)支持的基本方法,建立了基于线性(linear function)、多项式(polynomial function)和径向基(radial basis function)3种核函数的事件检测SVM模型。采用高速公路路段I-880线圈数据集和事件数据集验证模型,结果发现:无论对于向北、向南或混合方向的事件检测,在3个SVM模型中,SVM(P)检测效果最好,SVM(L)最差。SVM算法具有避免局部最小,实现全局最优化,更好的泛化效果的优点,是高速公路事件检测的一种很有潜力的算法。
Directing at the problem of mode identification of incident detection in transportation field, this paper describes the basic methods of support vector machine (SVM) in incident detection, and establishes three SVM models of incident detection respectively based on linear function, polynomial function and radial basis function. An illustration was conducted to validate these models by taking the data from the I-880 section on highway, the result shows that SVM(P) is the best and SVM(L) is the worst among the three SVM models. SVM is an effective algorithm in incident detection and superior to neural network in avoiding local minimum and achieving global optimization.
Directing at the problem of mode identification of incident detection in transportation field, this paper describes the basic methods of support vector machine (SVM) in incident detection, and establishes three SVM models of incident detection respectively based on linear function, polynomial function and radial basis function. An illustration was conducted to validate these models by taking the data from the I-880 section on highway, the result shows that SVM(P) is the best and SVM(L) is the worst among the three SVM models. SVM is an effective algorithm in incident detection and superior to neural network in avoiding local minimum and achieving global optimization.
引文
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[15]沈晓蓉,王川久,郝佳等.基于数据评估交通事件检测中神经网络应用性的研究[J].公路交通科技,2005,22(9):127~129,145
[16]Ma,J.,Ahalt,S..OSU SVM Classifier Matlab Toolbox(Ver2.00)[OL].http://eewww.eng.ohio-state.edu/~maj/osu_svm,2005-05-22
[2]刘娜,胡刚.试论我国道路交通危机管理体系的构建[J].中国安全科学学报,2005,15(12):52~56
[3]张敬磊,王晓原.交通事件检测算法研究进展[J].武汉理工大学学报(交通科学与工程版),2005,29(2):215~218
[4]Hussein Dia,Geoff Rose.Development and evaluation of neural network freeway incident and detection models using field data[J].Transportation Research Part C,1997,5(5):313~331
[5]R.Weil,J.Wootton,A.Garcia-ortiz.Traffic incident detection:sensors and algorithms[J].Mathematical and Computer Modeling,1998,7(9-11):257~291
[6]汪丹,张亚非.SVM和BP算法在气体识别中的对比研究[J].传感技术学报,2005,18(1):201~204
[7]Fang Yuan,Ruey Long Cheu.Incident detection using support vector machines[J].Transportation Research Part C,2003(11):309~328
[8]赵春玲,黄昆鸟,陈森发.一种多源信息的最优融合方法研究[J].信息与控制,2005,31(1):66~70
[9]李昆仑,黄厚宽,田盛丰等.入侵检测的1类支持向量机模型[J].中国安全科学学报,2003,13(6):72~75
[10]周鹏,曾晖,周原等.支持向量机用于芳烃类化合物对芳烃受体亲和性QSAR研究[J].环境科学学报,2006,26(1):124~129
[11]刘清坤,阙沛文,郭华伟等.基于支持向量机和特征选择的超声缺陷识别方法研究[J].中国机械工程,2006,17(1):9~12
[12]王炜,刘悦,李国正等.我国大陆强震预测的支持向量机方法[J].地震学报,2006,28(1):29~36
[13]V.D.Sanchez.Advanced support vector machines and kernel methods[J].Neurocomputing,2003(55):5~20
[14]Karl Petty.The Analysis Software for the FSP Project[OL].http://ipa.eecs.berkeley.edu/~pettyk/FSP,2005-01-10
[15]沈晓蓉,王川久,郝佳等.基于数据评估交通事件检测中神经网络应用性的研究[J].公路交通科技,2005,22(9):127~129,145
[16]Ma,J.,Ahalt,S..OSU SVM Classifier Matlab Toolbox(Ver2.00)[OL].http://eewww.eng.ohio-state.edu/~maj/osu_svm,2005-05-22
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