Visibility is one of the most important impacts weather can have on road system; weather-related visibility is often due to fog. Florida is among the top-rated states in the United States with regards to traffic safety problems resulting from adverse visibility conditions caused by fog.
This study plans to validate an alternative low-cost approach that can meet or exceed existing performance of traditional technologies. With supplemental meteorological data sets, the studies will seek to identify the unique characteristics/signature of the “fingerprint” of fog formation. The computer software algorithms will be trained to adapt to micro-local conditions to improve accuracy.
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This study will develop software algorithms to process and analyze the data from the multi-level arrays of temperature and relative humidity, wind speed/direction, and subsurface moisture in conjunction with measurements from the co-located visibility sensors. This study will alsoconsider the human factors issues relevant to implementing a visibility system on highways. This includes drivers’ responses to different messages in reduced visibility conditions and their preferences.
Improved roadway safety can result from this project as improved detection and prediction of visibility obstructions can help avoid crashes, improve traffic management from reduced congestion, and save money via more efficient advance deployment of law enforcement or other crews necessary to monitor deteriorating visibility conditions.