Long Term Traffic Prediction in Highway Using Parallel CNN

For navigation system, predicting future traffic conditions is crucial. To predict the traffic condition, statistical methods and neural network models have been studied. However, conventional methods have three limitations in which only the temporal properties are used, only narrow sections or time steps are predicted and not general road sections such as all section of highway but specific sections are used as test results. This paper proposes a parallel Convolutional Neural Network (CNN) that uses spatiotemporal properties and predicts for the next five hours and up to 400 km ranges in Korea's representative highway. Using a highway dataset, the proposed parallel CNN is trained and evaluated. As a result, the result of our model is improved by 10.6%, in terms of Root Mean Square Error (RMSE), compared to the conventional method. Moreover, in terms of the average of Average Speed Difference (ASD), the result of our model is improved by 63.5%.