Real-time Highway Accident Prediction based on Support Vector Machines

摘要：

Traditional traffic accident prediction uses long-term traffic data such as annual average daily traffic and hourly volume. In contrast to traditional traffic accident prediction, real-time traffic accident prediction uses real-time traffic data, obtained from inductive loop detectors and usually collected every 20 or 30 seconds, to identify hazardous traffic conditions to potentially prevent the traffic accident occurrence. We aim at identifying traffic patterns leading to traffic accidents and not leading to traffic accidents in this study. Support vector machines (SVM) are used to classify traffic conditions into those two patterns with real-time traffic data. Traffic accident data and its corresponding real-time traffic data are collected from the traffic simulation software TSIS, which is a microscopic traffic simulation software. This is the first time the SVM method is applied for real-time traffic accident prediction. The experimental results show that it is promising for real-time traffic accident prediction by using the support vector machine method.

关键词： Traffic Accident Prediction Real-time Traffic Data Support Vector Machine Real-time Accident Prediction

作者: Yisheng Lv Shuming Tang Hongxia Zhao Shuang Li

作者单位: Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China Shandong University of Science and Technology, Qingdao 266510, China School of Transportation, Southeast University, Nanjing 210096, China

会议类型: 国际会议

会议名称: 2009年中国控制与决策会议(2009 Chinese Control and Decision Conference)

会议地点: 广西桂林

会议语种:英文

页码: 4403-4407

在线出版日期: 2009-06-17（万方平台首次上网日期，不代表论文的发表时间）