Scale-Disentangled spatiotemporal Modeling for Long-term Traffic Emission Forecasting
This work addresses urban air pollution management by improving traffic emission forecasting, though it appears incremental as it builds on existing spatiotemporal graph methods with a novel decomposition and fusion approach.
The paper tackles the problem of long-term traffic emission forecasting by addressing multi-scale entanglement in spatiotemporal data, which causes error amplification, and proposes a scale-disentangled framework that achieves state-of-the-art performance on a road-level dataset in Xi'an.
Long-term traffic emission forecasting is crucial for the comprehensive management of urban air pollution. Traditional forecasting methods typically construct spatiotemporal graph models by mining spatiotemporal dependencies to predict emissions. However, due to the multi-scale entanglement of traffic emissions across time and space, these spatiotemporal graph modeling method tend to suffer from cascading error amplification during long-term inference. To address this issue, we propose a Scale-Disentangled Spatio-Temporal Modeling (SDSTM) framework for long-term traffic emission forecasting. It leverages the predictability differences across multiple scales to decompose and fuse features at different scales, while constraining them to remain independent yet complementary. Specifically, the model first introduces a dual-stream feature decomposition strategy based on the Koopman lifting operator. It lifts the scale-coupled spatiotemporal dynamical system into an infinite-dimensional linear space via Koopman operator, and delineates the predictability boundary using gated wavelet decomposition. Then a novel fusion mechanism is constructed, incorporating a dual-stream independence constraint based on cross-term loss to dynamically refine the dual-stream prediction results, suppress mutual interference, and enhance the accuracy of long-term traffic emission prediction. Extensive experiments conducted on a road-level traffic emission dataset within Xi'an's Second Ring Road demonstrate that the proposed model achieves state-of-the-art performance.