2026· E3S Web of Conferences· Vol 723, pp. 01013· 0 citations· 8 references
TL;DR
SRGNet is proposed, a spectrally-regularized graph network combining three targeted innovations: spectral normalization on all weight matrices to bound the Lipschitz constant, disruption-aware training augmentation synthesizing incident-like flow drops, and stochastic depth creating an implicit ensemble.
Abstract
Spatio-temporal graph neural networks achieve strong traffic forecasting accuracy, yet their robustness under out-of-distribution (OOD) conditions, such as traffic incidents, remains poorly understood. We propose SRGNet, a spectrally-regularized graph network combining three targeted innovations: (1) spectral normalization on all weight matrices to bound the Lipschitz constant; (2) disruption-aware training augmentation synthesizing incident-like flow drops; and (3) stochastic depth creating an implicit ensemble. We evaluate on PEMS-BAY using an impact-verified OOD protocol with 996 real incidents ( 30% flow reduction). SRGNet achieves the lowest OOD degradation (+116.0%) among competitive models, the best local OOD RMSE (0.987) at the most-impacted sensors, and a standard RMSE of 0.3123, demonstrating the best accuracy–robustness tradeoff.
This study develops a framework for preserving network-wide traffic information while reconstructing flow and density at unobserved links and the macroscopic fundamental diagram, enabling a broad range of sensor budgets in large-scale networks without strict limits on the number of sensors.
Ying Zhang, F. Fakhrmoosavi, Arash E. Zaghi· 0 citations
F$^2$STNet is proposed, a federated forecasting framework that combines truncated graph-Fourier features, a lightweight diagonal state-space temporal encoder, graph convolution, and Fairness-aware Federated Aggregation (FFA).
Jia-Yi Zhang, Jin-Feng Xu, Hewei Wang et al.· 0 citations
Rapid evaluation of many simultaneous road-link disruptions requires a practical compromise between exact spectral recomputation and local approximation. We estimate relative algebraic-connectivity loss after multi-edge deletion using graph neural networks (GNNs) that learn a bounded correction to a first-order Fiedler...
Increasing penetration of renewable energy in distribution networks severely disrupts traditional source-load aggregation, particularly under complex operating conditions. This paper proposes a condition aware dynamic source-load aggregation(CA-DSSA,), validated on the IEEE 33-bus distribution system across seven disti...
Qinghe Sun, Ai-Hua Zhou, Min Xu et al.· International Conference on...· 0 citations
A robust focused comparative evaluation of seven traffic forecasting approaches suggests that traffic forecasting models should be assessed not only by clean-data accuracy but also by their robustness under degraded sensing conditions before deployment in real intelligent transportation systems.
Shreya N. Desai, Kasim Ishaque Ghanchi, Ali Mehdi Mirza et al.· International journal of res...· 0 citations
The prediction of traffic flow has evolved from an empirical extrapolation method to a spatiotemporal correlation-based method due to the development of artificial intelligence and intelligent algorithms. In order to remedy the problem that the disturbance in the future causes the prediction error to increase sharply,...
L.-M. Chen, Z.-H. Jiang, J. Yang· Advanced Electromagnetics· 0 citations
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