Citation

Zou G, Lai Z, Ma C, Li Y, Wang T. Transp. Res. C Emerg. Technol. 2023; 154: e104263.

Copyright

(Copyright © 2023, Elsevier Publishing)

DOI

10.1016/j.trc.2023.104263

PMID

unavailable

Abstract

Accurately predicting the highway traffic speed can reduce traffic accidents and transit time, which is of great significance to highway management. Three essential elements should be considered in the long-term highway traffic speed prediction: (1) adaptability to speed fluctuation, (2) exploring the spatio-temporal correlation effectively, and (3) prediction of non-error propagation. This paper proposes a novel spatio-temporal generative inference network (STGIN) driven by data and long-term prediction. STGIN consists of three parts; semantic enhancement, spatio-temporal correlation extraction block (ST-Block), and generative inference. Semantic enhancement is first used to model the contextual semantics of traffic speed, improving the adaptability to speed fluctuations. The ST-Block is then used to extract the spatio-temporal correlations of the highway network. Finally, generative inference is used to pay attention to the correlation between historical- and target-sequences to generate the target hidden outputs rather than a dynamic step-by-step decoding way; it avoids long-term prediction error propagation in the spatial and temporal dimensions. The evaluation experiments use the monitoring data of highway in Yinchuan City, Ningxia Province, China. For long-term highway speed prediction, the experimental results demonstrate that the performance of the proposed method is better than that of the baseline methods.

Language: en

Keywords

Generative inference; Graph neural networks; Long short-term memory network; Long-term highway traffic speed prediction; Multi-head self-attention; Spatio-temporal correlation