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14 June 2023 Locality-aware spatial-temporal attention neural network with contrast learning for traffic flow prediction
Zheng Shi, Jingping Wang, Yingjun Zhang, Hui Yin, Hua Huang, Yanglong Zhong
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Proceedings Volume 12708, 3rd International Conference on Internet of Things and Smart City (IoTSC 2023）; 127082U (2023) https://doi.org/10.1117/12.2684225
Event: 3rd International Conference on Internet of Things and Smart City (IoTSC 2023）, 2023, Chongqing, China
Abstract
Traffic flow prediction is a crucial component of establishing the intelligent transportation system. Accurate and real-time traffic flow prediction is of great significance for urban traffic management. With the recent development of artificial intelligence, deep learning-based methods have been effective tools for traffic flow prediction. However, locality unawareness and data scarcity are still two open issues to be considered in traffic flow prediction tasks. To address these problems, we propose a novel locality-aware spatial-temporal attention neural network, named LASTANN, in this paper. Specifically, we propose two elaborate attention modules to perceive local information in spatial and temporal dimensions. We also propose an auxiliary module based on contrast learning to strengthen the representation ability of model. We verify the effectiveness of the proposed model on two real-world traffic flow datasets. The experimental results demonstrate that the proposed LASTANN outperforms other baselines consistently and each component enhances the prediction performances significantly.
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Zheng Shi, Jingping Wang, Yingjun Zhang, Hui Yin, Hua Huang, and Yanglong Zhong "Locality-aware spatial-temporal attention neural network with contrast learning for traffic flow prediction", Proc. SPIE 12708, 3rd International Conference on Internet of Things and Smart City (IoTSC 2023）, 127082U (14 June 2023); https://doi.org/10.1117/12.2684225
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KEYWORDS
Data modeling
Modeling
Neural networks
Spatial learning
Deep learning
Intelligence systems
Transportation
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