基于朴素贝叶斯分类的高速公路交通事件检测  

A Naive Bayesian Classifier-based Algorithm for Freeway Traffic Incident Detection

作　　者：张轮[1] 杨文臣[1] 刘拓[1] 施奕骋 

机构地区：[1]同济大学道路与交通工程教育部重点实验室,上海201804

出　　处：《同济大学学报（自然科学版）》2014年第4期558-563,595,共7页Journal of Tongji University:Natural Science

基　　金：国家自然科学基金(50408034);上海市创新基金(11ZZ27)

摘　　要：提出一种基于朴素贝叶斯分类的高速公路非重现交通事件检测算法.将交通事件的检测看作是0-1分类问题,采用交通波动理论建立交通事件的特征属性概念模型,并利用分段离散化的方法将连续特征变量转换为离散特征变量,设计基于朴素贝叶斯算法的交通事件分类器.以典型高速公路的一条路段进行VISSIM仿真试验.结果表明:该算法的检测率高,且在高强度状况下,算法鲁棒性良好,适用于高速公路交通事件检测系统.This paper presents a naive Bayesian classifier- based algorithm for freeway non-recurrent traffic incident detection to enhance the accuracy and learning ability of intelligent traffic incident detection algorithm. The traffic wave theory is employed to establish a conceptual characteristic model of traffic incident, continuous characteristic variables are transferred into discrete characteristic variables via sub-discretization, and the naive bayesian-based traffic incident classifier is designed by regarding traffic incident detection as ＂0-1＂ classification problems. An experiment is carried on a section of a typical freeway, and the performance of the presented model and algorithm is validated via VISSIM simulation. Extensive simulation results show that the algorithm in freeway traffic incident detection system is of high accuracy and strong robustness even if the traffic volumes increase.

关 键 词：交通事件 朴素贝叶斯 特征离散 模式识别 高速公路运营 

分 类 号：U492.85[交通运输工程—交通运输规划与管理] TP391.9[交通运输工程—道路与铁道工程]