Connected Vehicle Solution for Winter Road Surface Condition Monitoring

Abstract

A connected vehicle–based winter road surface condition (RSC) monitoring solution that combines vehicle-based image data with data from a road weather information system is described. The proposed solution was intended as an improvement to a smartphone-based system evaluated in previous research. Three machine learning classification methods—namely, artificial neural networks, random trees, and random forests—were evaluated for potential application in the connected vehicle–based system for RSC monitoring. Field data collected during the 2014–2015 winter season were used for model calibration and validation. Results showed that all models improved the accuracy of the smartphone-based RSC classification substantially, with random forest having the highest classification performance. The models, however, were found to lack transferability; therefore, individual models will require local calibration before being used at any location.