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Research on Traffic Flow Prediction Methods Based on Deep Learning
- 1 International College, Wenzhou Business College, Wenzhou, China
- 2 Oulu College, Nanjing Institute of Technology, Nanjing, China
* Author to whom correspondence should be addressed.
Abstract
In recent years, traffic flow prediction technology has been transformed from statistics based parametric methods and machine learning driven non-parametric methods to big data driven deep learning methods. This paper summarizes and summarizes the existing methods and improvement measures of long and short term traffic flow prediction based on deep learning. The time range of traffic flow forecast based on the model is divided into long-term and short term. The short-term traffic flow forecasting methods are subdivided into time series model, non-parametric forecasting model and probability forecasting model, and the advantages and disadvantages of each method and the feasibility of the specific methods are summarized. As for the long-term model, it is mainly based on the application of GCN model to other models, and then the specific methods of its hybrid model are outlined, systematically describing the value of deep learning in traffic flow prediction. Finally, the future research direction and development trend in this field are predicted and prospected.
Keywords
Traffic flow prediction, deep learning, long and short term traffic flow.
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Cite this article
Chen,J.;Song,J. (2024).Research on Traffic Flow Prediction Methods Based on Deep Learning.Applied and Computational Engineering,111,72-80.
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Volume title: Proceedings of CONF-MLA 2024 Workshop: Mastering the Art of GANs: Unleashing Creativity with Generative Adversarial Networks
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