The invention provides a traffic accident risk prediction method for a highway confluence area, and the method comprises the following steps: obtaining vehicle dangerous driving behavior historical data and traffic flow historical data corresponding to the vehicle dangerous driving behavior historical data, and taking the vehicle dangerous driving behavior historical data and the traffic flow historical data as model independent variables, historical data of traffic accidents in the highway confluence area are obtained and used as model dependent variables, and a logic regression model based on machine learning is established and trained; the method comprises the following steps: acquiring real-time data of vehicles in an expressway confluence area and real-time data of traffic flow in the expressway confluence area, identifying various driving behaviors of the vehicles through an end point detection algorithm and a dynamic time bending algorithm, and classifying the various driving behaviors of the vehicles through a Bayesian classification algorithm; and inputting real-time data into the trained logistic regression model, and predicting the traffic accident risk. According to the method, the vehicle driving behaviors of the highway confluence area can be actively identified, and the traffic accident risk of the highway confluence area can be effectively predicted.
本发明提供一种高速公路合流区域的交通事故风险预测方法,该方法包括以下步骤:获取车辆危险驾驶行为历史数据及其对应的交通流量历史数据并将其作为模型自变量,获取高速公路合流区域交通事故历史数据并将其作为模型因变量,建立基于机器学习的逻辑回归模型并进行训练;获取高速公路合流区域车辆实时数据以及高速公路合流区域交通流量实时数据,通过端点检测算法和动态时间弯曲算法识别车辆的多种驾驶行为,通过贝叶斯分类算法将车辆的多种驾驶行为分类;将实时数据输入训练完毕的逻辑回归模型,预测交通事故风险。本发明能够主动对高速公路合流区域的车辆驾驶行为进行识别,并对高速公路合流区域的交通事故风险进行有效预测。
Traffic accident risk prediction method for highway confluence area
一种高速公路合流区域的交通事故风险预测方法
2024-07-23
Patent
Electronic Resource
Chinese
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