Synthesis of visibility detection systems.
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Synthesis of visibility detection systems.

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    • Abstract:
      Visibility is a critical component to the task of driving on all types of roads. The visibility detection and warning systems provide real-time, automated detection as well as appropriate responses to counteract reduced visibility conditions due to fog, heavy rain, snow, smoke, dust or haze by informing drivers of present conditions and lowering the speed limits to match the reduced visibility condition. The objective of this research project is to provide a synthesis of visibility detection systems and traffic control techniques that are developed and/or implemented in the United States and around the world. This report provides an overview of the best practices of fixed visibility systems at areas of recurrent dense fog and mobile systems for seasonal visibility reduction for areas of predicted seasonal fog or smoke from wildfires. Ongoing research efforts of developing new camera-based visibility detection systems are also discussed. In addition, a preliminary analysis of Fog/Smoke (FS) crashes in Florida, including detailed two-way analysis capturing interactions between various factors and fog and/or smoke related crashes is provided in this report. To identify and prioritize areas for treatment, an update of the statewide map with increased granularity of reduced visibility related crashes is generated. The visibility detection systems can help to mitigate the increased hazard of limited-visibility, however, such systems are not widely implemented and many locations with no systems are experiencing considerable number of fatal crashes due to reduction in visibility caused by fog and inclement weather. On the other hand, airports' weather stations continuously monitor all climate parameters in real-time, the gathered data may be utilized to mitigate the increased risk for the adjacent roadways. An additional research effort is also provided to examine primarily the possibility of using weather information collected by weather stations at airports within the vicinity of fog-prone areas. Bayesian logistic regression was utilized to link 6-year (2005-2010) of historical crash data to real-time weather information collected from 8 airports in the State of Florida, roadway characteristics and aggregate traffic parameters. The results from this research depicts that real-time weather data collected from adjacent airports may be good predictors to assess increased risk on highways.
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