Research on Traffic Flow Prediction Methods Based on Deep Learning
Year: 2024
Type: article
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.
Authors: Junjin Chen, Jiatong Song
Institutions: Wenzhou Business College, Nanjing Institute of Technology
Language: English
Subfield: Building and Construction
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4404798927