Best Practices for Road Weather Management Version 2.0
Title:
Real-Time Detection of Crash Prone Conditions in Freeway High Crash Locations
Abstract:
In this paper such a proactive crash prevention solution, specifically for high crash areas, is explored by identifying the most relevant real time traffic metrics and incorporating them in a crash likelihood estimation model. This solution is based on a unique detection and surveillance infrastructure deployed on the freeway section experiencing the highest crash rate in the state of Minnesota. This state-of-the-art infrastructure allowed video recording of 110 live crashes, crash related traffic events, as well as contributing factors while simultaneously measuring traffic variables such as individual vehicle speeds and headways over each lane in several places inside the study area. This crash rich database was combined with visual observations and analyzed extensively to identify the most relevant real-time traffic measurements for detecting crash prone conditions and develop an online crash prone conditions model. This model successfully established a relationship between fast evolving real time traffic conditions and the likelihood of a crash. Testing was performed in real time during 10 days not previously used in the model development, under varying traffic and weather conditions (including rain, snow, as well as bright sunshine). The crash likelihood model and the detection algorithm succeeded in detecting 58 percent of the crashes accompanied by a 6.8 false decision rate. ...
Source(s):
85th Transportation Research Board (TRB) Annual Meeting, University of Minnesota. For a copy of this resource, please direct your request to WeatherFeedback@dot.gov.
Date: 2006
Author:
John Hourdakis, Garg, Michalopoulos, Davis
Keywords:
Incidents
Safety
Traffic modeling
Vehicle detection
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