title: Estimation of incident clearance times using Bayesian Networks approach.
abstract: Effective incident management requires a full understanding of various characteristics of incidents to accurately estimate incident durations and to help make more efficient decisions to reduce the impact of non-recurring congestion due to these accidents. Our goal is thus to have a comprehensive and clear description of incident clearance patterns and to represent these patterns with formalisms based on Bayesian Networks (BNs). BNs can be used to create dynamic incident duration estimation trees that can be extracted in the presence of a real incident for which data might only be partially available. This capability will enable traffic operators to create case-specific incident management strategies in the presence of incomplete information. In this paper, we employ a unique database created using incident data collected in Northern Virginia. This database is then used to demonstrate the advantages of employing BNs as a powerful modeling and analysis tool especially due to their ability to consider the stochastic variations of the data and to allow bi-directional induction in decision-making. In addition to the presentation of the basic theory behind BNs in the context of our problem and the validation of our estimation results, the dependency relations among all variables in the estimated BN that can be used for both quantitative and qualitative analysis are also discussed in detail.
authors.affiliation: Civil and Environmental Engineering and Center for Advanced Infrastructure and Transportation (CAIT), Rutgers University, Piscataway, NJ 08854-8014, USA. kaan@rci.rutgers.edu
authors.forename: Kaan
authors.initials: K
authors.lastname: Ozbay
authors.forename: Nebahat
authors.initials: N
authors.lastname: Noyan
mesh_terms: D000063:Accidents, Traffic / Q000517:prevention & control* / Q000706:statistics & numerical data; D000465:Algorithms; D001336:Automobiles / Q000706:statistics & numerical data*; D001499:Bayes Theorem; D016208:Databases, Factual; D003657:Decision Making; D003663:Decision Trees; D004189:Disaster Planning / Q000379:methods*; D004526:Efficiency; D006801:Humans; D015994:Incidence; D015233:Models, Statistical; D018570:Risk Assessment; D013995:Time; D014768:Virginia