Long-term traffic flow prediction based on multi-scale perceptual change

Jia Lu; Maonian Wu

doi:10.1117/12.3016002

20 February 2024 Long-term traffic flow prediction based on multi-scale perceptual change

Jia Lu, Maonian Wu

Proceedings Volume 13064, Seventh International Conference on Traffic Engineering and Transportation System (ICTETS 2023); 130640I (2024) https://doi.org/10.1117/12.3016002
Event: 7th International Conference on Traffic Engineering and Transportation System (ICTETS 2023), 2023, Dalian, China

Abstract

Traffic flow prediction is very important to urban traffic management and public safety, and is also a key link in the development of intelligent transportation system. However, the long-term traffic flow data has complex time characteristics, which brings severe challenges to the traffic flow forecasting research. Deep learning methods are mainly used in time series prediction tasks such as traffic flow prediction. Recently, Transformer-based approaches have been able to improve traffic flow forecasting results, but there are still limitations such as high computational costs and difficulty capturing overall trends in traffic flow data. We propose a multi-scale sensing neural network (MSPFormer) based on Transformer, which uses multi-layer perceptrons to achieve more efficient feature extraction in step prediction, while designing a progressive decomposition architecture to preserve historical information authenticity as much as possible. Detailed experimental studies have shown that our approach can improve the accuracy of traffic flow prediction in Transformer architecture by 4% to 24% without consuming additional computing resources.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Jia Lu and Maonian Wu "Long-term traffic flow prediction based on multi-scale perceptual change", Proc. SPIE 13064, Seventh International Conference on Traffic Engineering and Transportation System (ICTETS 2023), 130640I (20 February 2024); https://doi.org/10.1117/12.3016002

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