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Predicting traffic incident duration is a major challenge for many trafficcentres around the world. Most research studies focus on predicting theincident duration on motorways rather than arterial roads, due to a highnetwork complexity and lack of data. In this paper we propose a bi-levelframework for predicting the accident duration on arterial road networks inSydney, based on operational requirements of incident clearance target which isless than 45 minutes. Using incident baseline information, we first deploy aclassification method using various ensemble tree models in order to predictwhether a new incident will be cleared in less than 45min or not. If theincident was classified as short-term, then various regression models aredeveloped for predicting the actual incident duration in minutes byincorporating various traffic flow features. After outlier removal andintensive model hyper-parameter tuning through randomized search andcross-validation, we show that the extreme gradient boost approach outperformedall models, including the gradient-boosted decision-trees by almost 53%.Finally, we perform a feature importance evaluation for incident durationprediction and show that the best prediction results are obtained whenleveraging the real-time traffic flow in vicinity road sections to the reportedaccident location.

Arterial incident duration prediction using a bi-level framework of extreme gradient-tree boosting