Research Article
A Survey of Traffic Prediction Based on Deep Neural Network: Data, Methods and Challenges
@INPROCEEDINGS{10.1007/978-3-030-99191-3_2, author={Pengfei Cao and Fei Dai and Guozhi Liu and Jinmei Yang and Bi Huang}, title={A Survey of Traffic Prediction Based on Deep Neural Network: Data, Methods and Challenges}, proceedings={Cloud Computing. 11th EAI International Conference, CloudComp 2021, Virtual Event, December 9--10, 2021, Proceedings}, proceedings_a={CLOUDCOMP}, year={2022}, month={3}, keywords={Deep neural network Traffic forecasting Spatio-temporal data}, doi={10.1007/978-3-030-99191-3_2} }- Pengfei Cao
Fei Dai
Guozhi Liu
Jinmei Yang
Bi Huang
Year: 2022
A Survey of Traffic Prediction Based on Deep Neural Network: Data, Methods and Challenges
CLOUDCOMP
Springer
DOI: 10.1007/978-3-030-99191-3_2
Abstract
Traffic prediction plays an important role in the intelligent transportation system (ITS), because it can increase people’s travel convenience. Despite the deep neural network has been widely used in the field of traffic prediction, literature surveys of such methods and data categories are rare. In this paper, we have a summary of traffic forecasting from data, methods and challenges. Firstly, we are according to the difference of in spatio-temporal dimensions, divide the data into three types, including the spatio-temporal static data, spatial static time dynamic data, and spatio-temporal dynamic data. Secondly, we explore three significant neural networks of deep learning in traffic prediction, including the convolutional neural network (CNN), the recurrent neural network (RNN), and the hybrid neural networks models. These methods are used in many aspects of traffic prediction, including road traffic accidents forecast, road traffic flow prediction, road traffic speed forecast, and road traffic congestion forecast introduced. Finally, we provide a discussion of some current challenges and development prospects.
