Proceedings

DualCast: A Model to Disentangle Aperiodic Events from Traffic Series

Xinyu Su, Feng Liu, Yanchuan Chang, Egemen Tanin, Majid Sarvi, Jianzhong Qi

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Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence. Main Track. Pages 3290-3298.

https://doi.org/10.24963/ijcai.2025/366

Keywords

  • Data Mining: DM: Frequent pattern mining
  • Data Mining: DM: Mining spatial and/or temporal data

Abstract

Traffic forecasting is crucial for transportation systems optimisation. Current models minimise the mean forecasting errors, often favouring periodic events prevalent in the training data, while overlooking critical aperiodic ones like traffic incidents. To address this, we propose DualCast, a dual-branch framework that disentangles traffic signals into intrinsic spatial-temporal patterns and external environmental contexts, including aperiodic events. DualCast also employs a cross-time attention mechanism to capture high-order spatial-temporal relationships from both periodic and aperiodic patterns. DualCast is versatile. We integrate it with recent traffic forecasting models, consistently reducing their forecasting errors by up to 9.6% on multiple real datasets.

Xinyu Su, Feng Liu, Yanchuan Chang, Egemen Tanin, Majid Sarvi, and Jianzhong Qi. 2025. DualCast: A Model to Disentangle Aperiodic Events from Traffic Series. In Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence, IJCAI-25. IJCAI, 3290–3298. https://doi.org/10.24963/ijcai.2025/366
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