  In modern transportation systems, an enormous amount of traffic data is
generated every day. This has led to rapid progress in short-term traffic
prediction (STTP), in which deep learning methods have recently been applied.
In traffic networks with complex spatiotemporal relationships, deep neural
networks (DNNs) often perform well because they are capable of automatically
extracting the most important features and patterns. In this study, we survey
recent STTP studies applying deep networks from four perspectives. 1) We
summarize input data representation methods according to the number and type of
spatial and temporal dependencies involved. 2) We briefly explain a wide range
of DNN techniques from the earliest networks, including Restricted Boltzmann
Machines, to the most recent, including graph-based and meta-learning networks.
3) We summarize previous STTP studies in terms of the type of DNN techniques,
application area, dataset and code availability, and the type of the
represented spatiotemporal dependencies. 4) We compile public traffic datasets
that are popular and can be used as the standard benchmarks. Finally, we
suggest challenging issues and possible future research directions in STTP.
