SINN: a structural information-based neural network for traffic flow forecasting in large-scale road networks

Weiguo Zhu; Caiyuan Liu; Xingyu Zhang; Yongqi Sun

doi:10.1117/12.3034837

7 August 2024 SINN: a structural information-based neural network for traffic flow forecasting in large-scale road networks

Weiguo Zhu, Caiyuan Liu, Xingyu Zhang, Yongqi Sun

Author Affiliations +

Proceedings Volume 13224, 4th International Conference on Internet of Things and Smart City (IoTSC 2024); 132242I (2024) https://doi.org/10.1117/12.3034837
Event: 4th International Conference on Internet of Things and Smart City, 2024, Hangzhou, China

Abstract

Current traffic flow forecasting is a crucial component within intelligent transportation systems (ITSs). However, most existing studies still cannot be employed on large-scale data flow forecasting in real-world scenarios. In this paper, we design a spatiotemporal neural network called SINN for large-scale traffic flow forecasting tasks in real-world scenarios. First, we propose a transformer-based neural network to integrate the historical traffic flow into further traffic flow. Second, we propose a structural information-based hierarchical graph neural network module suitable for large-scale network structures, which can capture relevant node features within large-scale road networks for forecasting future traffic flow. Finally, we collect a large-scale traffic flow dataset and execute evaluation experiments. The experimental results indicate that SINN is capable of processing datasets with thousands of nodes. Compared with the methods that can execute the same scale datasets, SINN can make significant improvements by 44.29%, 26.54%, and 25.23% in the metrics of MAE, RMSE, and MAPE.

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

Citation Download Citation

Weiguo Zhu, Caiyuan Liu, Xingyu Zhang, and Yongqi Sun "SINN: a structural information-based neural network for traffic flow forecasting in large-scale road networks", Proc. SPIE 13224, 4th International Conference on Internet of Things and Smart City (IoTSC 2024), 132242I (7 August 2024); https://doi.org/10.1117/12.3034837

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KEYWORDS

Neural networks

Roads

Data modeling

Machine learning

Intelligence systems

Design

Transportation

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