Traffic Index Prediction And Classification Considering Characteristics Of Time Series Based On Autoregressive Integrated Moving Average Convolutional Neural Network Model

We propose an autoregressive integrated moving average convolutional neural network (ARIMA-CNN) to address the problems of the large amount of computation required for typical networks, the low effectiveness of traditional machine learning in traffic index prediction, and the weak recognition ability of traditional methods based on distance and features to improve the traffic index in traffic scheduling. The ARIMA-CNN can accurately predict the traffic index and distinguish its model categories. The model includes two steps: traffic index prediction and prediction index classification. The first step uses the augmented Dickey-Fuller (ADF) test to determine the type of traffic index series and then converts a nonstationary series to a stationary series by the difference operation. The ARIMA is fitted with the Bayesian information criterion (BIC) matrix, and the traffic index is predicted by the ARIMA. The second step obtains the best CNN model based on the traffic index feature information extracted from the training time series, integrates the feature information into a one-dimensional feature vector, determines the feature vector pattern category according to the Softmax classifier, and decides the category of the predicted traffic index. We used the traffic index data of Beijing for three consecutive years (from 2016 to 2018) as an example. The time series of the traffic index in the experimental data was accurately predicted, the prediction results were consistent with the variation characteristics of the real series data, and the prediction mode of recognition was the Monday mode. Our results were consistent with the actual situation and prove the validity of the model. On the basis of the identification results and the corresponding threshold curve of this category, we were able to find abnormal points, supervise the traffic status, and obtain early warnings about possible abnormal traffic patterns. This research has important practical significance for helping traffic management departments to make traffic control decisions in advance.