Short-Term Travel Speed Prediction for Urban Expressways Based on Convolutional Neural Network with Inception Module

Abstract:

In order to effectively learn the mixed traffic congestion patterns from the urban expressways and improve the accuracy of short-term travelling speed prediction， based on the convolution neural network， and incorporated with the Inception Module， a short-term travelling speed prediction model was established. The travelling speed information was arranged into two-dimensional matrices which could represent the traffic states， and the features represented by the input time-space travel speed images were learnt. The optimum model was obtained as the result of a systematic neural network design and training process， with the ability to automatically recognize multi-scale mixed traffic congestion patterns and extract high-dimensional features of the traffic data. Besides the regression analysis method as well as the gradient magnitude similarity deviation indicator was introduced to conduct a comprehensive evaluation. The case study shows that the proposed model outperforms other models in learning the temporal/spatiotemporal features from traffic data with a high prediction accuracy， which can be further applied to making short-term prediction for other traffic parameters.