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Traffic flow prediction using multiscale diffusion model guided by wavelet transform
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AI
DOI：10.1007/s10489-026-07408-6.png)
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Abstract
En 中文
Precise traffic flow forecasting is crucial for the development of intelligent transportation systems, enabling proactive traffic management, congestion alleviation, and efficient resource allocation. Traditional predictive models frequently fail to accurately capture the intricate nonlinear dynamics and multiscale temporal correlations present in traffic data. Multiscale Temporal- Spatial Diffusion Model informed by Wavelet Transform (MTSDM/WT) is proposed to overcome these constraints. The MTSDM/WT architecture begins with wavelet decomposition to decompose traffic time series into high-frequency components (capturing short-term fluctuations such as sudden congestion) and low-frequency components (reflecting long-term trends like rush-hour patterns). Specifically, the High-Frequency Refinement Module (HFRM) leverages multiscale convolution and attention mechanisms to precisely model localized temporal variations in the high-frequency domain, enhancing the capture of transient dynamics. Meanwhile, the low-frequency components undergo a forward diffusion process that incrementally adds noise, followed by a reverse denoising diffusion process to learn and reconstruct their temporal distribution, enabling accurate modeling of long-term trends. Finally, the refined high-frequency components and reconstructed low-frequency components are fused via inverse wavelet transform to generate comprehensive and robust traffic flow predictions. Extensive experiments were conducted on real-world traffic flow datasets from key urban intersections in Linyi City, Shandong Province, China. The MTSDM/WT framework exhibits notable improvements across critical evaluation metrics-Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Root Mean Squared Error (RMSE), and Continuous Ranked Probability Score (CRPS)-with a 3% performance gain observed, for instance, on the MJ dataset at a 10-minute prediction horizon. These findings validate that MTSDM/WT provides superior prediction accuracy and resilience, especially in settings marked by fluctuating traffic patterns and noise disruption, with theoretical implications for advancing diffusion model applications in spatiotemporal series analysis and practical value for intelligent transportation system optimization.
Keywords:
Traffic forecasting
Diffusion model
Wavelet transform
Time series analysis
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