Short-Time Traffic Forecasting in Tourist Service Areas Based on a CNN and GRU Neural Network

Abstract

The continuous development of highway construction projects has prompted the function of service areas to be improved day by day. A traditional service area gradually transforms from a single traffic service mode to a complex traffic service mode. The continuous enrichment and perfection of the service area’s function makes the surrounding highway network more attractive, which leads to a sudden increase in highway traffic volume in a short period of time. In order to better improve the service level of a tourist service area by predicting the short-term traffic volume of the toll station around the tourist service area, this paper proposes a model combining a convolutional neural network and a gated recurrent unit (CNN plus GRU) to solve the problem of short-term traffic volume prediction. The data from 17 toll stations of the Yu’an Expressway in Guizhou Province were selected for the experiment to test the prediction effect of the CNN plus GRU-based model. The experimental results show that the prediction accuracy, the MAE and RMSE, are 1.8101 and 2.7021, respectively, for the toll stations with lower traffic volumes, and 3.820 and 5.172, respectively, for the toll stations with higher traffic volumes. Compared with a single model, the model’s prediction accuracy is improved, to different degrees. Therefore, the use of a convolutional neural network operation is better when the total traffic volume is low, considering the algorithm’s time and error. When using the combined convolutional neural network and gated recurrent unit model and when the total traffic volume is high, the algorithm error is significantly reduced and the prediction results are better.