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Accurate prediction of incident duration and response strategies are two imperative aspects of traffic incident management. Past research has applied various types of regression models for predicting incident durations and quantification of associated factors. However, an important methodological aspect is unobserved heterogeneity which may be present due to several unobserved/omitted factors. Incorporation of heterogeneity has significant potential to enhance predictive capabilities as well as obtain more robust insights for designing practical incident management strategies. This study uses two frequentist/quasi-Bayesian statistical techniques—simulation-assisted random parameter and quantile regression models, to address unobserved heterogeneity and to compare both methodologies with respect to the two aspects of incident management. By using 2015 Virginia incident data related to more than 45,000 incidents, the heterogeneous associations of incident durations with several factors including detection source, incident type, roadway type, temporal factors, and incident characteristics are explored. Specifically, as the name implies, quantile regression models associations between different quantiles of incident duration and explanatory factors. This facilitates designing more appropriate strategies for small, medium and large-scale incidents. Compared to quantile regression and fixed parameter models, random parameter models can potentially give more accurate predictions of incident durations. However, they do not (typically) capture different quantiles of incident durations. By characterizing and harnessing the unobserved heterogeneity, the study proposes the concept of Incident Reduction Factors that can assist traffic incident managers and practitioners in developing customized strategies for small- and large-scale incidents. The practical implications of results are discussed from the perspectives of travelers and incident managers.

Heterogeneity assessment in incident duration modelling: Implications for development of practical strategies for small & large scale incidents