基于支持向量机的高速公路意外事件检测模型

Freeway Accident Detection Model Based on Support Vector Machine

为建立快速高效的高速公路意外事件自动检测系统,提高意外事件救援效率,就高速公路意外事件检测中的关键技术进行了研究。在剖析现有模型特征的基础上,引入支持向量机理论,建立了基于支持向量机的高速公路意外事件检测模型。利用自主开发的EAD-Simulations系统所建立的数据库,对模型进行了仿真试验,分析了不同核函数对检测性能的影响,研究了单侧输入与双侧输入、不同输入特征因素组合的性能指标。结果表明:与California 8#算法相比,该模型检测率提高了179%,误检率降低至0.50%,平均检测时间缩短了81%;同时得到了上游占有率与流率组合的最优输入特征。

The key technology of freeway accident detection was studied in order to set up a quick and efficient accident detection system and promote the efficiency of accident rescue. On the basis of characteristic analysis of the existing models, the freeway accident detection model based on support vector machine （SVM） theory was put forward. With database established by self-developed EAD Simulations system, a simulation experiment was applied to the model. The effects of different kernel functions on detection performance were analyzed and the performance indexes, such as upstream input, upstream and downstream input and different input of features combination were studied. The results show that the excellent performances of the model are demonstrated by contrast with California 8^# model. The detection rate raises 179%; error detection rate drops at 0. 50% and average detection time cuts down 81%. in addition, the optimal input characteristic combined by occupancy and flow rate in upstream is received.