Authors: Woon Kim, Gang-Len Chang, Steven M. Rochon
Conference: The 15th World Congress on Intelligent Transport Systems, New York, 2008
This paper presents a methodology for developing a model for estimating and predicting incident duration and identifying variables influencing the incident duration in the state of Maryland. The incident information from years 2003 to 2005 from the Maryland State Highway (MDSHA) database is used for model development, and year 2006 for the model validation. Classification Trees (CT) were used for a preliminary analysis to understand the influence of the variables associated with an incident. Based on the findings from CT, this study employed the Rule-Based Tree Model (RBTM) to develop the primary prediction model. The overall confidence for the estimated model was over 80% with several remarkable findings regarding the associations between factors and incident duration. Although the estimated results from RBTM are quite acceptable, supplemental models along with better quality database are required to improve the prediction accuracy for the duration of a detected incident.