Improving Freeway Crash Prediction Models Using Disaggregate Flow State Information

Friday, February 21, 2020 - 19:00

Crash analysis methods typically use annual average daily traffic as an exposure measure, which can be too aggregate to capture the safety effects of variations in traffic flow and operations that occur throughout the day.  Flow characteristics such as variation in speed and level of congestion play a significant role in crash occurrence and are not currently accounted for in the American Association of State Highway and Transportation Officials’ Highway Safety Manual.  This study developed a methodology for creating crash prediction models using traffic, geometric, and control information that is provided at sub-daily aggregation intervals.  Data from 110 rural four-lane segments and 80 urban six-lane segments were used.  The volume data used in this study came from detectors that collect data ranging from continuous counts throughout the year to counts from only a couple of weeks every other year (short counts).  Speed data were collected from both point sensors and probe data provided by INRIX. The full report can be viewed at http://www.virginiadot.org/vtrc/main/online_reports/pdf/20-R15.pdf

 


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