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

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.