A comparative study of models for the incident duration prediction

Gaetano Valenti, Maria Lelli, Domenico Cucina

Research output: Contribution to journalArticle

50 Citations (Scopus)


Purpose: This study is intended to investigate the reliability of different incident duration prediction models for real time application with a view to contribute to the development of a decision aid tool within the incident management process context where rough incident duration estimates are currently provided by traffic operators or police on the basis of their skill and past experience. Methods: Five predictive models, ranging from parametric models, to non-parametric and neural network models, have been considered and compared evaluating their capacity of predicting incident duration. The data set used in this study for developing and testing the prediction models includes 237 incident events and contains information about the incident characteristics, the personnel and equipment involved to clear the incident and the related response times, including the beginning and ending time of the incident. Results: Testing results have demonstrated that the proposed models are able to achieve good performance in terms of prediction accuracy especially for incidents with duration less than 90 min. This finding is partly due to the fact that the dataset has a relatively small number of severe incidents. Furthermore a linear combination of predictions from models was applied with negligible gain in accuracy. Conclusions: A deeper investigation is suggested for a future work to evaluate potential improvements from the application of other combination methods. Moreover each proposed model is able to reach best performance for incidents within a particular duration range. Thus a preliminary incident classification scheme could be more convenient in order to select the more appropriate prediction model. © The Author(s) 2010.
Original languageEnglish
Pages (from-to)103 - 111
Number of pages9
JournalEuropean Transport Research Review
Issue number2
Publication statusPublished - Jun 2010


All Science Journal Classification (ASJC) codes

  • Automotive Engineering
  • Transportation
  • Mechanical Engineering

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