AI RESEARCH
Long-Horizon Traffic Forecasting via Incident-Aware Conformal Spatio-Temporal Transformers
arXiv CS.LG
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ArXi:2603.16857v1 Announce Type: new Reliable multi-horizon traffic forecasting is challenging because network conditions are stochastic, incident disruptions are intermittent, and effective spatial dependencies vary across time-of-day patterns. This study is conducted on the Ohio Department of Transportation (ODOT) traffic count data and corresponding ODOT crash records. This work utilizes a Spatio-Temporal Transformer (STT) model with Adaptive Conformal Prediction (ACP) to produce multi-horizon forecasts with calibrated uncertainty.