Scale-Disentangled spatiotemporal Modeling for Long-term Traffic Emission Forecasting

• URL: http://arxiv.org/abs/2508.11923v1
• Date: Sat, 16 Aug 2025 06:06:47 GMT
• Title: Scale-Disentangled spatiotemporal Modeling for Long-term Traffic Emission Forecasting
• Authors: Yan Wu, Lihong Pei, Yukai Han, Yang Cao, Yu Kang, Yanlong Zhao,
• Abstract summary: We propose a Scale-Disd S-temporal Modeling (SDSTM) framework for long-term traffic emission forecasting.<n>The proposed model achieves state-of-the-art performance.
• Score: 10.643363557001955
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• GraFSTNet: Graph-based Frequency SpatioTemporal Network for Cellular Traffic Prediction [9.622104744379675]
  We propose a cellular traffic prediction framework that integrates temporal-temporal modeling with time-frequency analysis.<n>We introduce an adaptive-scale Logosh loss function, which adjusts the error penalty based on traffic magnitude.<n> Experiments on three open-sourced datasets demonstrate that the proposed method achieves prediction performance superior to state-of-the-art approaches.
  arXiv  Detail & Related papers  (2026-02-06T16:21:06Z)
• RainDiff: End-to-end Precipitation Nowcasting Via Token-wise Attention Diffusion [64.49056527678606]
  We propose a Token-wise Attention integrated into not only the U-Net diffusion model but also the radar-temporal encoder.<n>Unlike prior approaches, our method integrates attention into the architecture without incurring the high resource cost typical of pixel-space diffusion.<n>Our experiments and evaluations demonstrate that the proposed method significantly outperforms state-of-the-art approaches, robustness local fidelity, generalization, and superior in complex precipitation forecasting scenarios.
  arXiv  Detail & Related papers  (2025-10-16T17:59:13Z)
• ResAD: Normalized Residual Trajectory Modeling for End-to-End Autonomous Driving [64.42138266293202]
  ResAD is a Normalized Residual Trajectory Modeling framework.<n>It reframes the learning task to predict the residual deviation from an inertial reference.<n>On the NAVSIM benchmark, ResAD achieves a state-of-the-art PDMS of 88.6 using a vanilla diffusion policy.
  arXiv  Detail & Related papers  (2025-10-09T17:59:36Z)
• Elucidated Rolling Diffusion Models for Probabilistic Weather Forecasting [52.6508222408558]
  We introduce Elucidated Rolling Diffusion Models (ERDM)<n>ERDM is the first framework to unify a rolling forecast structure with the principled, performant design of Elucidated Diffusion Models (EDM)<n>On 2D Navier-Stokes simulations and ERA5 global weather forecasting at 1.5circ resolution, ERDM consistently outperforms key diffusion-based baselines.
  arXiv  Detail & Related papers  (2025-06-24T21:44:31Z)
• Multi-Step Embed to Control: A Novel Deep Learning-based Approach for Surrogate Modelling in Reservoir Simulation [0.0]
  Reduced-order models, also known as proxy model or surrogate model, are approximate models that are less computational expensive as opposed to fully descriptive models. This paper introduces a deep learning-based surrogate model, referred as multi-step embed-to-control model, for the construction of proxy models with improved long-term prediction performance.
  arXiv  Detail & Related papers  (2024-09-16T01:35:34Z)
• Physics-guided Active Sample Reweighting for Urban Flow Prediction [75.24539704456791]
  Urban flow prediction is a nuanced-temporal modeling that estimates the throughput of transportation services like buses, taxis and ride-driven models. Some recent prediction solutions bring remedies with the notion of physics-guided machine learning (PGML) We develop a atized physics-guided network (PN), and propose a data-aware framework Physics-guided Active Sample Reweighting (P-GASR)
  arXiv  Detail & Related papers  (2024-07-18T15:44:23Z)
• A Multi-Channel Spatial-Temporal Transformer Model for Traffic Flow Forecasting [0.0]
  We propose a multi-channel spatial-temporal transformer model for traffic flow forecasting. It improves the accuracy of the prediction by fusing results from different channels of traffic data. Experimental results on six real-world datasets demonstrate that introducing a multi-channel mechanism into the temporal model enhances performance.
  arXiv  Detail & Related papers  (2024-05-10T06:37:07Z)
• Unveiling Delay Effects in Traffic Forecasting: A Perspective from Spatial-Temporal Delay Differential Equations [20.174094418301245]
  Traffic flow forecasting is a fundamental research issue for transportation planning and management. In recent years, Graph Neural Networks (GNNs) and Recurrent Neural Networks (RNNs) have achieved great success in capturing spatial-temporal correlations for traffic flow forecasting. However, two non-ignorable issues haven't been well solved: 1) The message passing in GNNs is immediate, while in reality the spatial message interactions among neighboring nodes can be delayed.
  arXiv  Detail & Related papers  (2024-02-02T08:55:23Z)
• PDFormer: Propagation Delay-Aware Dynamic Long-Range Transformer for Traffic Flow Prediction [78.05103666987655]
  spatial-temporal Graph Neural Network (GNN) models have emerged as one of the most promising methods to solve this problem. We propose a novel propagation delay-aware dynamic long-range transFormer, namely PDFormer, for accurate traffic flow prediction. Our method can not only achieve state-of-the-art performance but also exhibit competitive computational efficiency.
  arXiv  Detail & Related papers  (2023-01-19T08:42:40Z)
• Haar Wavelet based Block Autoregressive Flows for Trajectories [129.37479472754083]
  Prediction of trajectories such as that of pedestrians is crucial to the performance of autonomous agents. We introduce a novel Haar wavelet based block autoregressive model leveraging split couplings. We illustrate the advantages of our approach for generating diverse and accurate trajectories on two real-world datasets.
  arXiv  Detail & Related papers  (2020-09-21T13:57:10Z)
• A nudged hybrid analysis and modeling approach for realtime wake-vortex transport and decay prediction [0.0]
  Long short-term memory (LSTM) nudging framework for enhancement of reduced order models (ROMs) of fluid flows utilized noisy measurements for air traffic improvements. We build on the fact that in realistic application, there are uncertainties in initial and boundary conditions, model parameters, as well as measurements. In the presented LSTM nudging (LSTM-N) approach, we fuse forecasts from a combination of imperfect GROM and uncertain state estimates, with sparseian sensor measurements to provide more reliable predictions in a dynamical data assimilation framework.
  arXiv  Detail & Related papers  (2020-08-05T23:47:15Z)
• Spatial-Temporal Transformer Networks for Traffic Flow Forecasting [74.76852538940746]
  We propose a novel paradigm of Spatial-Temporal Transformer Networks (STTNs) to improve the accuracy of long-term traffic forecasting. Specifically, we present a new variant of graph neural networks, named spatial transformer, by dynamically modeling directed spatial dependencies. The proposed model enables fast and scalable training over a long range spatial-temporal dependencies.
  arXiv  Detail & Related papers  (2020-01-09T10:21:04Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.