TITLE:
Predictor Variables Influencing Visibility Prediction Based on Elevation and Its Range for Improving Traffic Operations and Safety
AUTHORS:
Ajinkya Sadashiv Mane, Srinivas Subrahmanyam Pulugurtha, Venkata Ramana Duddu, Christopher Michael Godfrey
KEYWORDS:
Visibility, Prediction, Weather Station, Linear Regression Model, Elevation
JOURNAL NAME:
Journal of Transportation Technologies,
Vol.12 No.3,
July
21,
2022
ABSTRACT: Low visibility condition hinders both air traffic
and road traffic operations. Accurate
forecasting of visibility condition helps aircraft operators and
travelers to make better decisions and improve their safety. It is, therefore,
essential to investigate and identify the predictor variables that could
influence and help predict visibility. The objective of this study is to
identify the predictor variables that influence visibility. Four years of
surface weather observations, from January 2011 to December 2014, were
collected from the weather stations located in and around the state of North
Carolina, USA for the model development. Ordinary least squares (OLS) and
weighted least squares (WLS) regression models were developed for different
visibility and elevation ranges. The results indicate that elevation, cloud
cover, and precipitation are negatively associated with the visibility in visibility
less than 15,000 m model. The elevation,
cloud cover and the presence of water bodies within the vicinity play an
important role in the visibility less than 2000 m model. The chances of low
visibility condition are higher between six to twelve hours after the rainfall
when compared to the first six hours after the rainfall. The results from this
study help to understand the influence of predictor variables that should be
dealt with to improve the traffic operations and safety concerning the
visibility near the airports/road transportation network.