During the winter season, real-time monitoring of road surface conditions is critical for the safetyof drivers and road maintenance operations. Previous research has evaluated the potential ofimage classification methods for detecting road snow coverage by processing images fromroadside cameras installed in RWIS (Road Weather Information System) stations. However, thereare a limited number of RWIS stations across Ontario, Canada; therefore, the network hasreduced spatial coverage. In this study, we suggest improving performance on this task throughthe integration of images and weather data collected from the RWIS stations with images fromother MTO (Ministry of Transportation of Ontario) roadside cameras and weather data fromEnvironment Canada stations. We use spatial statistics to quantify the benefits of integrating thethree datasets across Southern Ontario, showing evidence of a six-fold increase in the number ofavailable roadside cameras and therefore improving the spatial coverage in the most populousecoregions in Ontario. Additionally, we evaluate three spatial interpolation methods for inferringweather variables in locations without weather measurement instruments and identify the one thatoffers the best tradeoff between accuracy and ease of implementation.
展开▼
