Abstract
Data from weather stations at airports, far away locations or predictions using macro-level data may not be accurate enough to disseminate visibility related information to motorists in advance. Therefore, the objective of this research is to investigate the influence of contributing factors and develop visibility prediction models, at road link-level, by considering data from weather stations located within 1.6 km of state routes, US routes and interstates in the state of North Carolina (NC). Four years of meteorological data, from January 2011 to December 2014, were collected within NC. Ordinary least squares (OLS) and weighted least squares (WLS) regression models were developed for different visibility and elevation ranges. The results indicate that elevation and cloud cover are negatively associated with low visibility. The chances of low visibility are higher between six to twelve hours after rainfall when compared to the first six hours after rainfall. A visibility sensor was installed at four different locations in NC to compare hourly visibility from the selected regression model, High-Resolution Rapid Refresh (HRRR) data, and the nearest weather station. The results indicate that the number of samples with zero error range was higher for the selected regression model compared with the HRRR and weather station observations.
Highlights
From the year 2007 to the year 2016, on average, 1,235,145 weatherrelated crashes were reported in the United States
In the majority of the models, elevation and cloud cover are negatively associated with visibility
The results indicate that the number of samples with zero error range was higher for the selected regression model compared to High-Resolution Rapid Refresh (HRRR) and the weather station observations
Summary
From the year 2007 to the year 2016, on average, 1,235,145 weatherrelated crashes were reported in the United States. On average, 5,376 people are killed and over 418,000 people are injured in weatherrelated road crashes (U.S Department of Transportation, 2018). In terms of fog-related crashes, in the state of North Carolina (NC), 19,188 crashes were reported from the year 2003 to the year 2012 (Oliver, 2013). Out of these fog-related crashes, 43% of the crashes occurred between the months of November to January, and 49% of the crashes occurred during early morning hours from 5:00 AM to 7:00 AM. Predicting fog events, disseminating that information to motorists beforehand, and exploring technologies to dynamically adjust speed limits is vital to avoid fog-related crashes
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
