Arterial incident duration prediction using a bi-level framework of extreme gradient-tree boosting
This work addresses a specific challenge for traffic management centers by focusing on arterial roads, which are often neglected due to complexity and data scarcity, representing an incremental advancement with domain-specific application.
The paper tackled the problem of predicting traffic incident duration on arterial roads in Sydney, achieving a 53% improvement over gradient-boosted decision-trees using an extreme gradient boost approach within a bi-level framework.
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 paper 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. After outlier removal and intensive model hyper-parameter tuning through randomized search and cross-validation, we show that the extreme gradient boost approach outperformed all models, including the gradient-boosted decision-trees by almost 53%. Finally, we perform a feature importance evaluation for incident duration prediction and show that the best prediction results are obtained when leveraging the real-time traffic flow in vicinity road sections to the reported accident location.