Abstract: Accurate traffic forecasting remains challenging when sensor data are noisy, incomplete, or non-stationary. Recent advances in spatio-temporal learning have combined Graph Neural Networks (GNNs) with recurrent, convolutional, or attention mechanisms to capture spatio-temporal dependencies. However, most existing approaches remain largely deterministic and rely on fixed or pre-learned adjacency matrices, limiting their adaptability when network structures evolve or sensor reliability varies. Some methods further stack multiple adjacency matrices to represent complex spatial relations, yet still lack explicit mechanisms to model uncertainty, resulting in reduced robustness under degraded data conditions. This work introduces the Latent Topology Graph State-Space Model (LT-GSSM), a probabilistic framework designed to enhance robustness and adaptability in traffic forecasting. LT-GSSM represents the road network as a latent dynamic graph whose structure evolves over-time through dynamic adjacency learning based on past hidden states and observations, enabling the model to capture evolving spatial correlations such as congestion propagation. Temporal dependencies are modelled by a nonlinear state-space function implemented with a Temporal Convolutional Network (TCN), which captures long-range temporal patterns without recurrence. The probabilistic state-space formulation explicitly represents sensor noise and handles missing data through probabilistic estimation inspired by Kalman filtering. By jointly integrating dynamic graph learning, explicit noise modelling, and nonlinear temporal transitions, LT-GSSM achieves greater stability and resilience to data uncertainty. Experiments on SUMO simulations and real-world PeMS datasets show that LT-GSSM consistently outperforms static and adaptive-graph models, providing a strong foundation for robust spatio-temporal forecasting under uncertain conditions.

Selma Kerdous. “Latent-Topology Graph State-Space Model (LT-GSSM) for Robust Traffic Fore-Casting”. International Journal of Advanced Computer Science and Applications (IJACSA) 16.12 (2025). http://dx.doi.org/10.14569/IJACSA.2025.0161208

@article{Kerdous2025,
title = {Latent-Topology Graph State-Space Model (LT-GSSM) for Robust Traffic Fore-Casting},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2025.0161208},
url = {http://dx.doi.org/10.14569/IJACSA.2025.0161208},
year = {2025},
publisher = {The Science and Information Organization},
volume = {16},
number = {12},
author = {Selma Kerdous}
}