Abstract
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.
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Notes
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Incidents associated with scheduled road closures or without any log were excluded from the analysis. Duplicated incidents were identified by incident reporting time and location and were excluded as well while their logs were reviewed and merged. An automatic text recognition program was developed to parse incident logs.
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He, Q., Kamarianakis, Y., Jintanakul, K., Wynter, L. (2013). Incident Duration Prediction with Hybrid Tree-based Quantile Regression. In: Ukkusuri, S., Ozbay, K. (eds) Advances in Dynamic Network Modeling in Complex Transportation Systems. Complex Networks and Dynamic Systems, vol 2. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-6243-9_12
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