关键词: 智慧交通；交通路口；短周期；交通车流量；CNN-ARIMA；预测方法

Abstract: Aiming at the problems of low accuracy and unstable data of existing prediction models in the process of intelligent traffic vehicle flow prediction, this paper proposes a short-period traffic flow prediction method based on the combination of convolutional neural network (CNN) and time series prediction model auto regressive integrated moving average (ARIMA). In this paper, the historical traffic flow data of Wangxi road intersection in Wuhu city within a week is taken as the sample to predict the traffic vehicle flow in the next time period, and the obtained data are normalized and simulated. The simulation results show that the scheme can be applied to traffic control, and the scheme can effectively maintain a high accuracy without being affected by the size of the data set, and the prediction results can provide a better solution to the problem of vehicle congestion in advance.

Key words: Intelligent transportation; Traffic intersections; Short cycle; Traffic vehicle flow predic- tion; CNN-ARIMA; Prediction method