Naive Bayesian Classifier for Incident Duration Prediction

When choosing the appropriate response to an incident, it is important to predict the potential impact of the incident, including its duration, as accurately as possible. Therefore, we develop a probabilistic model based on a naïve Bayesian classifier to assist with prediction of incident duration. Two significant advantages of this model are its ability to readily accommodate incomplete information, or information received at different points in time, both of which are characteristic of the incident management process. This model was calibrated and tested using incident records from the Georgia Department of Transportation, and is shown to perform favorably compared to a standard linear regression model, as well as under a variety of information provision scenarios.

Language

  • English

Media Info

  • Media Type: CD-ROM
  • Features: Figures; References; Tables;
  • Pagination: 11p
  • Monograph Title: TRB 86th Annual Meeting Compendium of Papers CD-ROM

Subject/Index Terms

Filing Info

  • Accession Number: 01049548
  • Record Type: Publication
  • Report/Paper Numbers: 07-1801
  • Files: TRIS, TRB
  • Created Date: Feb 8 2007 6:29PM