Short-Term Traffic Prediction With Deep Neural Networks: A Survey

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