Citation

Yan J, Li H, Zhang D, Bai Y, Xu Y, Han C. Sci. Rep. 2024; 14(1): e14264.

Copyright

(Copyright © 2024, Nature Publishing Group)

DOI

10.1038/s41598-024-65040-1

PMID

38902350

Abstract

The traffic flow prediction is the key to alleviate traffic congestion, yet very challenging due to the complex influence factors. Currently, the most of deep learning models are designed to dig out the intricate dependency in continuous standardized sequences, which are dependent to high requirements for data continuity and regularized distribution. However, the data discontinuity and irregular distribution are inevitable in the real-world practical application, then we need find a way to utilize the powerful effect of the multi-feature fusion rather than continuous relation in standardized sequences. To this end, we conduct the prediction based on the multiple traffic features reflecting the complex influence factors. Firstly, we propose the ATFEM, an adaptive traffic features extraction mechanism, which can select important influence factors to construct joint temporal features matrix and global spatial features matrix according to the traffic condition. In this way, the feature's representation ability can be improved. Secondly, we propose the MFSTN, a multi-feature spatial-temporal fusion network, which include the temporal transformer encoder and graph attention network to obtain the latent representation of spatial-temporal features. Especially, we design the scaled spatial-temporal fusion module, which can automatically learn optimal fusion weights, further adapt to inconsistent spatial-temporal dimensions. Finally, the multi-layer perceptron gets the mapping function between these comprehensive features and traffic flow. This method helps to improve the interpretability of the prediction. Experimental results show that the proposed model outperforms a variety of baselines, and it can accurately predict the traffic flow when the data missing rate is high.

Language: en

Keywords

Transformer; Graph attention network; Spatial–temporal data; Traffic flow prediction