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Iowa State University--Becoming the Best

Secondary Accident Data Fusion for Assessing Long-Term Performance of Transportation Systems

Researcher(s)

Principal investigator:

Project status

Completed

Start date: 07/01/05
End date: 03/31/07

Publications

Project webpage: http://www.ctre.iastate.edu/mtc/projects/2005-04.htm

Report: Secondary Accident Data Fusion for Assessing Long-Term Performance of Transportation Systems (537 kb pdf) March 2007

Sponsor(s)/partner(s)

Sponsor(s): Midwest Transportation Consortium

About the research

Abstract: Secondary accident statistics can be useful for studying the impact of traffic incident management strategies. An easy-to-implement methodology is presented for classifying secondary accidents using data fusion of a police accident database with intranet incident reports. A current method for classifying secondary accidents uses a static threshold that represents the spatial and temporal region of influence of the primary accident, such as two miles and one hour. An accident is considered secondary if it occurs upstream from the primary accident and is within the duration and queue of the primary accident. However, using the static threshold may result in both false positives and negatives because accident queues are constantly varying.

The methodology presented in this report seeks to improve upon this existing method by making the threshold dynamic. An incident progression curve is used to mark the end of the queue throughout the entire incident. Four steps in the development of incident progression curves are described. Step one is the processing of intranet incident reports. Step two is the filling in of incomplete incident reports. Step three is the nonlinear regression of incident progression curves. Step four is the merging of individual incident progression curves into one master curve. To illustrate this methodology, 5,514 accidents from Missouri freeways were analyzed. The results show that secondary accidents identified by dynamic versus static thresholds can differ by more than 30%.