Fast-TrafficNet: A hybrid model for efficient prediction of nonlinear traffic flow with sparse data

Zhihao Xu, Zhiqiang Lv and Jianbo Li

Chaos, Solitons & Fractals, 2025, vol. 201, issue P1

Abstract: In the field of Intelligent Transportation Systems (ITS), nonlinear sparse traffic flow prediction faces significant challenges of high sparsity, nonlinear spatial-temporal heterogeneity, and “accuracy-speed” balance. Statistical learning methods and traditional machine learning models usually find it difficult to capture the spatial-temporal continuous evolution of nonlinear traffic flow dynamics systems. The code executing time of deep spatial-temporal models with many parameters is usually long, and researchers have not deeply addressed the computing efficiency problem. To address the above problems, a novel hybrid model (Fast-TrafficNet) is proposed, which integrates Spatial-Temporal Graph Neural Control Differential Equations (STG-NCDE) and Block-Term Decomposition (BTD) for efficient prediction of nonlinear spatial-temporal dynamical systems of sparse traffic flow. STG-NCDE captures the non-Euclidean nonlinear spatial dependence features. BTD extracts global patterns and local heterogeneous features through efficient tensor low-rank mapping, which in turn effectively compresses the high-dimensional sparse data and suppresses the noise interference to reduce the model complexity. Experiments show that under four types of real-world datasets and 10 %–75 % sparse data, Fast-TrafficNet reduces mean average error and root mean square error by an average of 17.3 % and 13.6 % compared with the optimal baseline, and consumes only 4.22 % of the code executing time. This study provides a new paradigm for modeling nonlinear sparse traffic flows, and the proposed Fast-TrafficNet can be extended to other nonlinear sparse traffic dynamics system prediction tasks, which contributes to the construction and sustainable development of ITS and smart cities. Open-source link: https://github.com/qdu318/Fast-TrafficNet.

Keywords: Nonlinear sparse traffic flow prediction; “Accuracy-speed” balance; Graph neural control differential equations; Block-term decomposition (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:chsofr:v:201:y:2025:i:p1:s0960077925012433

DOI: 10.1016/j.chaos.2025.117230

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