{"type":"ScholarlyArticle","title":"UniST-Pred: A Robust Unified Framework for Spatio-Temporal Traffic Forecasting in Transportation Networks Under Disruptions","authors":[{"name":"Yue Wang","slug":"yue-wang","given":"Yue","family":"Wang","affiliation":""},{"name":"Djellel Difallah","slug":"djellel-difallah","given":"Djellel","family":"Difallah","affiliation":""},{"name":"Areg Karapetyan","slug":"areg-karapetyan","given":"Areg","family":"Karapetyan","affiliation":""},{"name":"Samer Madanat","slug":"samer-madanat","given":"Samer","family":"Madanat","affiliation":""}],"abstract":"Spatio-temporal traffic forecasting is a core component of intelligent transportation systems, supporting various downstream tasks such as signal control and network-level traffic management. In real-world deployments, forecasting models must operate under structural and observational uncertainties, conditions that are rarely considered in model design. Recent approaches achieve strong short-term predictive performance by tightly coupling spatial and temporal modeling, often at the cost of increased complexity and limited modularity. In contrast, efficient time-series models capture long-range temporal dependencies without relying on explicit network structure. We propose UniST-Pred, a unified spatio-temporal forecasting framework that first decouples temporal modeling from spatial representation learning, then integrates both through adaptive representation-level fusion. To assess robustness of the proposed approach, we construct a dataset based on an agent-based, microscopic traffic simulator (MATSim) and evaluate UniST-Pred under severe network disconnection scenarios. Additionally, we benchmark UniST-Pred on standard traffic prediction datasets, demonstrating its competitive performance against existing well-established models despite a lightweight design. The results illustrate that UniST-Pred maintains strong predictive performance across both real-world and simulated datasets, while also yielding interpretable spatio-temporal representations under infrastructure disruptions. The source code and the generated dataset are available at https://anonymous.4open.science/r/UniST-Pred-EF27.","doi":"10.24963/ijcai.2026/834","url":"https://ijcai.org/proceedings/2026/834","pdf":"https://ijcai.org/proceedings/2026/0834.pdf","markdown":"https://ijcai.org/proceedings/2026/834.md","bibtex":"https://ijcai.org/proceedings/2026/bibtex/834","volume":{"slug":"2026","title":"Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2026","url":"https://ijcai.org/proceedings/2026/","year":2026,"location":"Bremen, Germany","dates":"15-21 August 2026","editors":"Diego Calvanese","isbn":"978-1-956792-09-6","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2026"},"section":"AI and Social Good","track":"AI and Social Good","number":834,"pages":{"first":7501,"last":7511},"keywords":["Multidisciplinary Topics and Applications: Multidisciplinary Topics and Applications","Machine Learning: Machine Learning"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}