Adaptive learning in bayesian networks for incident duration prediction

Sami Demiroluk, Kaan Ozbay

Research output: Contribution to journalArticlepeer-review


The development of a practical model for incident management is investigated through Bayesian networks (BNs) in this study. BNs are capable of accurately predicting incident durations and can easily be incorporated into incident management activities of traffic management centers to improve the real-time decision-making process. Three structure learning algorithms were used to construct BN structures. They were estimated by using 2005 New Jersey incident data; the best-performing one was chosen for the incident duration prediction with the use of the 10-fold cross-validation method and the Bayesian information criterion statistic. To demonstrate the performance of Bayesian learning, the chosen model was fed by 2011 New Jersey incident data on a monthly and quarterly basis. Comparing the prediction results for 2011 data with and without adaptive learning showed that the developed BN had the capability to automatically adapt itself to future conditions by learning the patterns of new incidents and their respective durations.

Original languageEnglish (US)
Pages (from-to)77-85
Number of pages9
JournalTransportation Research Record
Issue number1
StatePublished - Dec 1 2014

ASJC Scopus subject areas

  • Civil and Structural Engineering
  • Mechanical Engineering


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