Freeway Accident Detection Model Based on Support Vector Machine

Q2 Engineering
Baizhu Chen
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引用次数: 2

Abstract

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 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.
基于支持向量机的高速公路事故检测模型
为了建立快速高效的事故检测系统,提高事故救援效率,对高速公路事故检测的关键技术进行了研究。在对现有模型进行特征分析的基础上,提出了基于支持向量机理论的高速公路事故检测模型。利用自主开发的ead - simulation系统建立数据库,对模型进行仿真实验。分析了不同核函数对检测性能的影响,研究了上游输入、上下游输入、特征组合不同输入等性能指标。结果表明,与加利福尼亚模型相比,该模型具有良好的性能。检出率提高179%;错误检测率下降0.50%,平均检测时间缩短81%。此外,还得到了上游占用率和流量相结合的最优输入特性。
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来源期刊
中国公路学报
中国公路学报 Engineering-Mechanical Engineering
CiteScore
3.80
自引率
0.00%
发文量
4760
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