Incident Duration Prediction with Hybrid Tree-based Quantile Regression

Accurate prediction of incident duration is critical for efficient incident management which aims to minimize the impact of non-recurrent congestion. In this chapter, a hybrid tree-based quantile regression method is proposed for incident duration prediction and quantification of the effects of various incident and traffic characteristics that determine duration. Hybrid tree-based quantile regression incorporates the merits of both quantile regression modeling and tree-structured modeling: robustness to outliers, simple interpretation, flexibility in combining categorical covariates and capturing nonlinear associations. The predictive models presented here are based on variables associated with incident characteristics as well as the traffic conditions before and after incident occurrence. Compared to previous approaches, the hybrid tree-based quantile regression offers higher predictive accuracy.

By: Qing He, Yiannis Kamarianakis, Klayut Jintanakul, Laura Wynter

Published in: RC25175 in 2011


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