The study proposes a model that forecasts visibility in winter by using either multiple-regressionanalysis or the Kalman filter. We have been developing the Road Visibility Information System(RVIS), which calculates road visibility information as the weighted intensity of power spectra(WIPS) and the present-time road visibility index (RVI) from daytime road images recorded bymultiple closed-circuit television (CCTV) cameras along the roads. The objective of this study isto develop the visibility forecast model based on 1-km-mesh meteorological data. We used dataof the WIPS values and RVI ranks recorded by the RVIS and 1-km-mesh meteorological datarecorded by the Japan Weather Association during the winter of 2009-2010 at a 35-km section ofNational Route 40 in Hokkaido, Japan. A multiple-regression model and the Kalman filter wereemployed to reveal the relationship between WIPS data from road images as a dependentvariable and the meteorological data as independent variables. The Kalman filter can be regardedas the preferable of the two visibility forecast models examined in the study. Also, the1-km-mesh meteorological data of air temperature, wind speed and snowfall were determined tobe informative independent variables in the forecast models.
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