Development of a real-time roadway debris hazard spotting tool using connected vehicle data to enhance roadway safety and system efficiency

Roadway debris and other unexpected obstructions, such as surface damage, or lane hydroplaning due to weather conditions like snow or precipitation, can lead to significant traffic delays or worse, crashes. The presence of roadway debris is particularly concerning in high-traffic and high-speed roadways where dense traffic conditions reduce visibility and large volumes of vehicles are exposed to risk. Although prevention of the various causes of obstructions and defensive driving can reduce these consequences, the problem cannot be eliminated entirely. Currently, unexpected roadway obstructions are handled by relying on drivers’ self-reporting (e.g., through the Waze app), which is inefficient and unsafe because it can lead to distracted driving. In addition, this pinpointing the exact debris location can be challenging and adds to in delays between notifications and actual removal from the responsible transportation agency. This study seeks to take advantage of the real-time transmission of Basic Safety Messages (BSM) and travel metrics generated by a connectively-enhanced sample of 1,600 commuter-vehicles as part of the US DOT Connected Vehicle (CV) Pilot Deployment, Tampa, Florida. This research will leverage results and advancements from the Tampa CV Pilot and will provide the high-frequency BSM and driving metrics leading to the development and testing of this tool. The tool will be tested in cooperation with the Tampa-Hillsborough Expressway Authority (THEA). THEA's Lee Roy Selmon Expressway has been approved by the U.S. Department of Transportation (USDOT) as a connected vehicle test bed. THEA’s facility provides a contained environment in which to safely test and refine the road-debris-identifying algorithms using real-time connected vehicle data from THEA’s CV Pilot Deployment.