A Mathematical Model for Freeway Incident Detection and Characterization: a Fuzzy Approach
نویسندگان
چکیده
This article introduces the Alabama Freeway Incident Detection System-Incident Detection Module (AFIDS-IDM) as a methodology for the detection of freeway incidents. AFIDS-IDM invokes fuzzy cluster analysis in the identification of lane blocking incidents from comparisons of time varying patterns of incident induced and incident free traffic states. Lane traffic counts and density, collected at successive traffic sensors, are the two primary types of input data. State variables are defined from the spatial and temporal relationships of the raw data, and then evaluated quantitatively and qualitatively to determine the decision variables necessary for the determination of lane blocking incidents. The specified decision variable is then compared to a fuzzy cluster analysis algorithm to determine the existence of a lane blocking incident.
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