Traffic flow prediction for highway vehicle detectors through decomposition and machine learning

ABSTRACT

Traffic flow prediction is of significant importance in traffic planning. Currently, traffic flow data are primarily collected through loop detectors. However, factors such as road conditions can affect the accuracy of these data. To address this issue, this paper proposes a traffic flow prediction method based on decomposition and machine learning. The improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) method decomposes the sequence into multiple intrinsic mode functions (IMFs). The complexity of each IMF is calculated using the sample entropy (SE), and then the IMFs are reconstructed. Parameters of the variational mode decomposition (VMD) are optimized using the whale optimization algorithm (WOA) for the secondary decomposition, and predictions are made using gated recurrent units (GRU). Finally, the prediction results are reconstructed to obtain the final prediction values. In the case study section, experiments are conducted using datasets from three detectors to explore different decomposition forms and methods.

KEYWORDS:

• Traffic flow prediction
• improved complete ensemble empirical mode decomposition with adaptive noise
• variational mode decomposition
• gated recurrent units
• ensemble framework

Acknowledgments

We would like to extend our gratitude to Gui’an Supercomputing Center, operated by Gui’an New Area Science and Technology Innovation Industries Development Co., Ltd., for providing the computational resources and technical support crucial for conducting the experiments in this study.

Disclosure statement

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Additional information

Funding

This work was supported by the National Natural Science Foundation of China (Grant No. 12161016 and No. 11661018), and the Guizhou Provincial Basic Research Program (Natural Science) (Grant No. QianKeHeJiChu-ZK[2024]YiBan082).