Deep Learning's Current and Future Applications in Predicting Vehicle Traffic Flow

Yiyang Hu; Zhaohang He; Huayu Jiang

doi:10.54097/dqxg8v21

Authors

Yiyang Hu
Zhaohang He
Huayu Jiang

DOI:

https://doi.org/10.54097/dqxg8v21

Keywords:

Deep learning, highway traffic flow prediction, traffic flow dynamic analysis, real-time traffic prediction, traffic flow optimization.

Abstract

With the acceleration of urbanization and the continuous growth of traffic flow, accurate prediction of highway traffic flow has become a key demand for optimizing traffic management and alleviating congestion. Traditional prediction methods are often inefficient in dealing with complex traffic patterns, while deep learning technology brings new solutions for traffic flow prediction with its powerful feature learning and pattern recognition capabilities. This paper reviews deep learning applications' current state and future trends in highway traffic flow prediction. It introduces deep learning technology and its advantages in traffic flow prediction. The paper then analyzes the specific applications of Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and their variants in data processing, feature extraction, and prediction performance. The challenges currently faced, such as data preprocessing and the capture of long-term dependencies, are also discussed, along with proposed solutions. Finally, the paper looks forward to the future development of deep learning in the field of traffic flow prediction, highlighting its importance in improving traffic management efficiency.

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