Image-to-Image Learning to Predict Traffic Speeds by Considering Area-Wide Spatio-Temporal Dependencies

Abstract

Spatio-temporal dependencies are the key to predicting the traffic parameters of an urban arterial network. However, their inclusion in forecasting traffic states has been hampered due to both the absence of a robust model and the computational burden. Recently, an innovative way to tackle the problem was developed by adopting a convolutional neural network (CNN) to deal with map images representing traffic states. Unlike previous studies that utilized map images only for input, the present study adopted images for both the input and the output of a CNN model to predict traffic speeds. The results show that the performance of the proposed model based on image-to-image learning is superior to that of the existing models.