基于模糊聚类支持向量机的高速公路事件检测

高速公路自动事件检测(AID)系统作为智能交通系统(ITS)的重要组成部分,通过及时发现高速公路上发生的事故隐患,尽量减少事故发生的不利影响,可以有效地减少交通延误,保障道路安全,减少环境污染。文章采用一种强有力的分类工具—支持向量机(SVM)来进行高速公路事件检测,针对数据集在支持向量机中所起作用的不同以及可能存在噪声及孤立点的情况,采用了一种改进的模糊C均值聚类方法对训练样本进行预处理,大大地减少了训练样本数量,提高了支持向量机的训练速度,并且具有很好的鲁棒性。仿真实验的结果表明了该方法的可行性和有效性。

Freeway incident detection based on fuzzy-cluster Support Vector Machines

Freeway Incident Detection（AID） system as a very important part of Intelligent Transportation System，through discovering incidents that occur in freeway in time and trying the best to decrease the negative influence of incidents，can reduce traffic jam，guarantee freeway safety and reduce environment pollution.The paper introduces a strong and powerful classify tool—Support Vector Machines（SVM） to carry out freeway incident detection.Considering training data playing different roles in SVM and some isolate point or noise existence，we take a modified fuzzy-c-cluster method to pretreat the training data which can highly lessen training data and promote the training speed of SVM，and moreover，it has a favorable robustness.Simulation experiment shows the algorithm’s feasibility and validity.