Short-term Traffic Prediction with Deep Neural Networks: A Survey

Advances in transportation systems have resulted in the generation of a large amount of traffic data from various sources [1-3]. In everyday life, GPS sensors installed in smartphones carried by millions of people can collect crowd flow data. Furthermore, taximeters and bus card readers can collect crowd demand data, and vehicle loop detectors can collect traffic flow or speed data. In the mean time, Deep Neural Networks (DNNs) have achieved promising performance improvements in various application areas. They can classify images into thousands of classes [4,5] as well as recognize human speech [6,7], with only small errors.