Geographic Information Traffic Detection Model
Liu Chenxi, Yilin Cai, Xinlu Zhang, Jiatong Tan · 2021
With the acceleration of the urbanization process and the development of social economy, the motor vehicle ownership rate of urban residents is increasing day by day. Therefore, traffic congestion is becoming a major problem in cities around the world. These phenomena not only waste residents’ time and money, but also cause serious pollution to the environment. Therefore, traffic congestion monitoring is of great significance for analyzing the problem of traffic flow stability. To improve the prediction accuracy of urban road traffic congestion, this paper proposes a conges-tion discrimination model based on Pattern Mining according to relevant res. Then we combine a lot of data for data mining. Given the urban road traffic congestion real-time identification problem, we extract some key factors. Based on the Stata flow prediction model, we used the Beijing traffic data set model to verify the multi-log linear regression. Finally, we obtain the high accuracy traffic congestion regression model.