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DSTAN: attention-enhanced dynamic spatial-temporal network for traffic forecasting

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Abstract

Traffic forecasting is an enduring research topic in the design of intelligent transportation systems and spatial-temporal data mining. Accurate prediction can help facilitate urban resource optimization and improve road efficiency. However, the complex spatial-temporal dependencies and dynamic urban conditions make it extremely challenging. Although many spatial-temporal modeling approaches have been proposed recently, they still suffer from the following three problems: (1) Inadequate modeling of temporal correlations; (2) Ignoring the fundamental fact that the location dependence of road networks changes dynamically over time; (3) Difficulty in extracting deeper spatial-temporal features layer by layer. In this paper, we propose a novel Dynamic Spatial-Temporal Attention-enhanced Network called DSTAN for traffic prediction. In DSTAN, we combine gated temporal units with trend-aware multi-head temporal attention to jointly capture local and long-range temporal dependencies. We also employ learnable node embeddings to extract heterogeneous information and integrate this with the spatial attention module to learn dynamic spatial correlations without any expert knowledge. Structurally, we stack multiple spatial-temporal blocks to improve the model’s capability to identify complex patterns. Extensive experiments have been conducted on four widely used datasets, demonstrating that our method surpasses all baseline methods while exhibiting strong interpretability.

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Availability of Data and Materials

The traffic speed datasets are provided by the open source work DCRNN (https://github.com/liyaguang/DCRNN).

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Funding

Chunjiang Zhu is supported by UNCG Start-up Funds and Faculty First Award. Detian Zhang is partially supported by the Collaborative Innovation Center of Novel Software Technology and Industrialization, the Priority Academic Program Development of Jiangsu Higher Education Institutions.

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Authors and Affiliations

  1. Institute of Artificial Intelligence, School of Computer Science and Technology, Soochow University, Su Zhou, China

    Xunlian Luo & Detian Zhang

  2. Department of Computer Science, UNC Greensboro, Greensboro, NC, USA

    Chunjiang Zhu

  3. Department of Computing, The Hong Kong Polytechnic University, Hong Kong, China

    Qing Li

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  1. Xunlian Luo
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  2. Chunjiang Zhu
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  4. Qing Li
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Contributions

Xunlian Luo, Chunjiang Zhu, and Detian Zhang conceived of the presented idea. Xunlian Luo and Chunjiang Zhu wrote the main manuscript text. Xunlian Luo carried out the experiment. Xunlian Luo, Chunjiang Zhu, Detian Zhang and Qing Li reviewed the manuscript. Detian Zhang supervised the project.

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Correspondence to Detian Zhang.

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Luo, X., Zhu, C., Zhang, D. et al. DSTAN: attention-enhanced dynamic spatial-temporal network for traffic forecasting. World Wide Web 28, 15 (2025). https://doi.org/10.1007/s11280-025-01328-0

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