A Dynamic Correlation-Information-Fusion-Based Spatiotemporal Network for Traffic Flow Forecasting

Abstract

Traffic Flow Forecasting (TFF) is a foundational task in the development of Intelligent Transport Systems (ITSs). The primary challenge is to undertake a comprehensive exploration of the intrinsic dynamic spatiotemporal correlations of the road network, unveiling the long-term evolutionary traffic trends. Furthermore, most existing methods often solely depend on the single traffic condition and neglect the enhancement of correlated features collected from traffic sensors in prediction. To this end, we propose a dynamic correlation-information-fusion-based (DCIF) spatiotemporal network for TFF, which models the spatiotemporal correlations of road networks, thereby effectively capturing dynamically changing characteristics. Specifically, a spatiotemporal feature enhancement (STFE) mechanism is employed to capture the directional and location-aware characteristics of traffic flow, thereby enhancing the representation of traffic flow and the capability of spatiotemporal feature extraction. Then, a gated attention unit (GAU) is constructed to meticulously extract the deep dynamic trends inherent within traffic data. Finally, a dynamic feature matrix (DFM) is formulated, incorporating spatial graph convolution to provide comprehensive semantic contextual information. The DFM captures the dynamic topology of the deeper feature network in real time by fusing spatial node information and traffic speed features as correlation information. Extensive experiments demonstrate that DCIF significantly outperforms other baselines in prediction accuracy, thereby further substantiating its validity and reliability in TFF.

Keywords

dynamic feature matrix / dynamic spatiotemporal correlations / gated attention unit / multi-source information fusion / spatiotemporal feature enhancement mechanism / traffic flow forecasting

Funding

This work was supported in part by the National Natural Science Foundation of China (Grant 62462013), the High-Level Innovative Talent Project of Guizhou Province (Grant QKHPTRC-GCC2023027), the Science and Technology Innovation Talent Team Project of Data Science and Computational Intelligence of Guizhou Province (Grant QKHRC-CXTD2025038), the Key Project of the Basic Research Program of Guizhou Province (Grant QKHJCZD2026144), the Science and Technology Breakthrough Project of Hundred Schools and Thousand Enterprises of the Education Department of Guizhou Province (Grant QJJ2025011), the Key Project of Engineering Research Center of Micro-nano and Intelligent Manufacturing of Ministry of Education (Grant WZG202501), the Scientific Research Platform (Key Laboratory and Engineering Research Center) Project of Kaili University (Grants YTH-PT202501 and YTH-PT202602), the Science and Technology Foundation of Guizhou Province (Grants QKHJCMS2026727, QKHJC2024QN026, and QKHJC2024QN208), and the Natural Science Research Project of the Education Department of Guizhou Province (Grants QJJ2023012 and QJJ2023061).

Conflicts of Interest

The authors declare no conflicts of interest.

Data Availability Statement

The datasets are available from the corresponding author on reasonable request.

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