Egemen Tanin — accepted AI-conference papers

2026

Generalising Traffic Forecasting to Regions Without Traffic Observations

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

AAAI 2026technical

Traffic forecasting is essential for intelligent transportation systems. Accurate forecasting relies on continuous observations collected by traffic sensors. However, due to high deployment and maintenance costs, not all regions are equipped with such sensors. This paper aims to forecast for region

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2025

DualCast: A Model to Disentangle Aperiodic Events from Traffic Series

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

IJCAI 2025

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-

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