摘要
Abstract
Traditional methods often need to manually select and extract features,which is inefficient for large-scale and high-dimensional traffic data,and it is difficult to dynamically adapt to the changes of urban traffic state,resulting in poor recognition effect of urban road traffic congestion state.Therefore,an urban road traffic congestion state recognition method based on machine learning is proposed,which takes the urban road traffic video image as the basic data.Based on machine learning method and deep learning technology,features are automatically learned from data to improve the efficiency of feature extraction.After setting the traffic parameters and the congestion critical point,the Logistic regression model for the evaluation of urban road traffic congestion intensity is established.The traffic congestion intensity in the current urban road traffic video image is evaluated by the model.Then the evaluation results of urban road traffic congestion intensity are input into the support vector machine model of machine learning algorithm,and then the support vector machine model is improved by means of the sparrow algorithm to obtain the optimal parameters of the support vector machine model.After training the support vector machine model with the optimal parameters,the identification results of urban road traffic congestion state are output.The experimental results show that the method can effectively evaluate the traffic congestion intensity of different types of urban roads,and output the urban road traffic congestion state by means of the support vector machine model in the machine learning algorithm.Its application effect is better.关键词
机器学习/城市道路/交通拥塞/状态识别/Logistic回归模型/支持向量机模型/麻雀算法Key words
machine learning/urban road/traffic congestion/state recognition/Logistic regression model/support vector machine model/sparrow algorithm分类
信息技术与安全科学
