Spatiotemporal Dynamic Multi-Hop Network for Traffic Flow Forecasting

Wenguang Chai, Qingfeng Luo, Zhizhe Lin, Jingwen Yan, Jinglin Zhou () and Teng Zhou ()
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Wenguang Chai: School of Computer Science and Technology, Guangdong University of Technology, Guangzhou 510006, China
Qingfeng Luo: School of Computer Science and Technology, Guangdong University of Technology, Guangzhou 510006, China
Zhizhe Lin: School of Cyberspace Security, Hainan University, Haikou 570228, China
Jingwen Yan: College of Engineering, Shantou University, Shantou 515063, China
Jinglin Zhou: School of Philosophy, Fudan University, Shanghai 200433, China
Teng Zhou: School of Cyberspace Security, Hainan University, Haikou 570228, China

Sustainability, 2024, vol. 16, issue 14, 1-21

Abstract: Accurate traffic flow forecasting is vital for intelligent transportation systems, especially with urbanization worsening traffic congestion, which affects daily life, economic growth, and the environment. Precise forecasts aid in managing and optimizing transportation systems, reducing congestion, and improving air quality by cutting emissions. However, predicting outcomes is difficult due to intricate spatial relationships, nonlinear temporal patterns, and the challenges associated with long-term forecasting. Current research often uses static graph structures, overlooking dynamic and long-range dependencies. To tackle these issues, we introduce the spatiotemporal dynamic multi-hop network (ST-DMN), a Seq2Seq framework. This model incorporates spatiotemporal convolutional blocks (ST-Blocks) with residual connections in the encoder to condense historical traffic data into a fixed-dimensional vector. A dynamic graph represents time-varying inter-segment relationships, and multi-hop operation in the encoder’s spatial convolutional layer and the decoder’s diffusion multi-hop graph convolutional gated recurrent units (DMGCGRUs) capture long-range dependencies. Experiments on two real-world datasets METR-LA and PEMS-BAY show that ST-DMN surpasses existing models in three metrics.

Keywords: intelligent transportation systems; sustainability; traffic flow forecasting; spatiotemporal dependency learning; graph structure learning (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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