Traffic Speed Prediction and Congestion Source Exploration: A Deep Learning Method Jingyuan Wangy, Qian Guy, Junjie Wuz*, Guannan Liuz, Zhang Xiongyx y School of Computer Science and Engineering, Beihang University, Beijing, China z School of Economics and Management, Beihang University, Beijing, China x Research Institute of Beihang University in Shenzhen, Shenzhen, China Email: fjywang, gqian, wujj, liugn,
[email protected], *Corresponding author Abstract—Traffic speed prediction is a long-standing and mostly concerned with traffic flow and congestion predic- critically important topic in the area of Intelligent Trans- tions [4], [5]. Traffic speed prediction, therefore, is still an portation Systems (ITS). Recent years have witnessed the open problem in the deep-learning era, with two notable encouraging potentials of deep neural networks for real- life applications of various domains. Traffic speed prediction, challenges as follows: however, is still in its initial stage without making full use • How to characterize the latent interactions of road seg- of spatio-temporal traffic information. In light of this, in this ments in traffic speeds so as to improve the predictive paper, we propose a deep learning method with an Error- feedback Recurrent Convolutional Neural Network structure performance of a deep neural network? (eRCNN) for continuous traffic speed prediction. By integrating • How to model the abrupt changes of traffic speeds in the spatio-temporal traffic speeds of contiguous road segments case of emerging events such as morning peaks and as an input matrix, eRCNN explicitly leverages the implicit traffic accidents? correlations among nearby segments to improve the predictive accuracy. By further introducing separate error feedback These indeed motivate our study.