Metadata about: There are many factors that affect urban traffic flow. In the case of severe traffic congestion, the vehicle speed is very slow, which results in the GPS positioning system’s estimation of the vehicle speed being very inaccurate, which in turn leads to poor reliability of the estimated congestion time of the road segment. The main contents of this study are: in the case of urban traffic congestion, the prediction and analysis of the degree of traffic congestion and the length of congestion. Taking the dynamic traffic data of Shenzhen on June 9, 2014 as an example, the road section of Binhe Avenue is selected, and the data of traffic flow, average speed of traffic volume and traffic volume density in the current time period are calculated after data preprocessing, as a measure of traffic. The main impact indicators of congestion status. Then we use the fuzzy comprehensive evaluation method to divide TSI as a traffic congestion evaluation index and divide the road congestion into four levels. In this way, we can get the congestion of the road in each time period of the day and the time required to pass. Then we use the random forest, adaboost, GBDT, Lasso CV and BP neural networks in the machine learning algorithm to build models to measure traffic congestion for training and testing. Finally, the BP neural network has the best effect on this problem, and mean square error is 0.0190. Finally, we used BP neural network to predict and congest the road in the next three hours. From the experimental simulation results, this method can effectively analyze and predict the real-time traffic congestion.

About: There are many factors that affect urban traffic flow. In the case of severe traffic congestion, the vehicle speed is very slow, which results in the GPS positioning system’s estimation of the vehicle speed being very inaccurate, which in turn leads to poor reliability of the estimated congestion time of the road segment. The main contents of this study are: in the case of urban traffic congestion, the prediction and analysis of the degree of traffic congestion and the length of congestion. Taking the dynamic traffic data of Shenzhen on June 9, 2014 as an example, the road section of Binhe Avenue is selected, and the data of traffic flow, average speed of traffic volume and traffic volume density in the current time period are calculated after data preprocessing, as a measure of traffic. The main impact indicators of congestion status. Then we use the fuzzy comprehensive evaluation method to divide TSI as a traffic congestion evaluation index and divide the road congestion into four levels. In this way, we can get the congestion of the road in each time period of the day and the time required to pass. Then we use the random forest, adaboost, GBDT, Lasso CV and BP neural networks in the machine learning algorithm to build models to measure traffic congestion for training and testing. Finally, the BP neural network has the best effect on this problem, and mean square error is 0.0190. Finally, we used BP neural network to predict and congest the road in the next three hours. From the experimental simulation results, this method can effectively analyze and predict the real-time traffic congestion.

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