Deep Learning Methods in Short-Term Traffic Prediction: A Survey

Abstract

Nowadays, traffic congestion has become a serious problem that plagues the development of many cities around
the world and the travel and life of urban residents. Compared with the costly and long implementation cycle
measures such as the promotion of public transportation construction, vehicle restriction, road reconstruction, etc., traffic prediction is the lowest cost and best means to solve traffic congestion. Relevant departments
can give early warnings on congested road sections based on the results of traffic prediction, rationalize the
distribution of police forces, and solve the traffic congestion problem. At the same time, due to the increasing
real-time requirements of current traffic prediction, short-term traffic prediction has become a subject of widespread concern and research. Currently, the most widely used model for short-term traffic prediction are deep
learning models. This survey studied the relevant literature on the use of deep learning models to solve shortterm traffic prediction problem in the top journals of transportation in recent years, summarized the current
commonly used traffic datasets, the mainstream deep learning models and their applications in this field. Finally, the challenges and future development trends of deep learning models applied in this field are discussed.