Machine Learning For Traffic Disruptions

Predicting traffic incident duration is a major challenge for many traffic centres around the world. Most research studies focus on predicting the incident duration on motorways rather than arterial roads, due to a high network complexity and lack of data. In this work we propose a bi-level framework for predicting the accident duration on arterial road networks in Sydney, based on operational requirements of incident clearance target which is less than 45 minutes.


Using incident baseline information, we first deploy a classification method using various ensemble tree models in order to predict whether a new incident will be cleared in less than 45min or not. If the incident was classified as short-term, then various regression models are developed for predicting the actual incident duration in minutes by incorporating various traffic flow features and run intensive hyper-parameter tuning through randomized search and cross-validation.


Accepted Paper

Mihaita, A.S., Liu, Z., Cai, C., Rizoiu, M.A “Arterial incident duration prediction using a bi-level framework of extreme gradient-tree boosting”, ITS World Congress 2019, Singapore, 21-25 Oct 2019, Preprint link