| 基于支持向量机的高速公路事故实时风险预测 |
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| 引用本文： | 樊博,马筱栎,雷小诗,马新露.基于支持向量机的高速公路事故实时风险预测[J].工业工程,2021,24(4):143-149. |
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| 作者姓名： | 樊博 马筱栎 雷小诗 马新露 |
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| 作者单位： | 重庆交通大学 交通运输学院，重庆 400074 |
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| 基金项目： | 重庆市科技局资助项目(cstc2018jscx-mszdX0112) |
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| 摘 要： | 以高速公路事故数据、交通流数据和天气数据为基础，以交通流为事故主要影响因素，建模预测高速公路事故实时风险。将事故记录作为病例组，采用病例对照方法来配对匹配实验样本，通过随机森林算法从众多变量中筛选出对事故风险影响最重要的10 个特征变量，以支持向量机建立模型预测事故实时风险。实验表明，通过随机森林筛选重要的特征变量，再使用支持向量机建模预测事故风险具有可行性，且以高斯核、Sigmoid核作为支持向量机的核函数比线性核函数和多项式核函数时分类准确性更高；其中，高斯核下支持向量机模型对事故风险预判的准确率达73.20%，对正常交通流的分类达91.44%。
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| 关 键 词： | 交通工程 事故风险 支持向量机 病例-对照研究 随机森林 |
| 收稿时间： | 2020-04-22 |
Real-time Accident Risk Prediction on Freeway Based on Support Vector Machine |
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| FAN Bo,MA Xiaoli,LEI Xiaoshi,MA Xinlu.Real-time Accident Risk Prediction on Freeway Based on Support Vector Machine[J].Industrial Engineering Journal,2021,24(4):143-149. |
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| Authors: | FAN Bo MA Xiaoli LEI Xiaoshi MA Xinlu |
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| Affiliation: | College of Traffic and Transportation, Chongqing Jiaotong University, Chongqing 400074, China |
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| Abstract: | Based on the freeway accident data, traffic data, and weather data, a model is built to predict the real-time accident risk by taking traffic flow as the main influencing factor of accidents. Firstly, regarding each accident in the records as a case and the experimental case group is obtained, and then by the case-control study method, the matched corresponding control group set up. Then the random forest algorithm is used to screen out the 10 most important variables impacting the accident risk most. Finally, a model is built to predict real-time accident risk based on the support vector machine. Experiments show the SVM model we built works when predicting the accident risk. At the same time, the SVM model performance with the Gaussian and Sigmoid kernel is better than with the linear and polynomial kernel. Experiments showed the SVM model we built works when predicting the accident risk. At the same time, the SVM model performance with the Gaussian or Sigmoid kernel is better than with the linear or polynomial kernel. Especially the accuracy of the SVM model with the Gaussian kernel reaches 73.20% and 91.44% respectively when predicting the accident and non-accident situation. |
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| Keywords: | traffic engineering accident risk support vector machine case-control study random forest |
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