Abstract
The Georgia Department of Transportation (GDOT) has developed a proactive high-friction surface treatment (HFST) program for curve sites prone to run-off-road (ROR) crashes. Using crash data and a single-criterion, ball bank indicator (BBI) value, GDOT seeks to maximize the return on its HFST investment. GDOT has partnered with Georgia Tech to identify additional factors for its HFST site-selection (HFST-SS) decision-making process by leveraging high-resolution, full-coverage sensor data (e.g., GPS and LiDAR). This paper proposes a methodology to identify site characteristics that can be used in GDOT’s HFST-SS process by leveraging the sensor data and automatically extracting roadway curve features as follows: (a) roadway data collection using state-of-the-art sensing technologies, (b) automatic extraction of detailed site characteristics data and curve information, (c) curved-based roadway segmentation using the extracted curve information; (d) spatial integration of curve-site characteristics data (CSCD); (e) analysis of CSCD and ROR crashes to identify additional factors for HFST site selection. A case study using CSCD extracted from Georgia State Route 2 demonstrates the proposed methodology. Results show that on sharp curves having comparable site characteristics, vertical grades greater than 3% play an important role in ROR crashes. Therefore, a vertical grade greater than 3% could be considered as an additional HFST-SS factor along with the current BBI criterion.
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