Municipalities and contractors in Canada and other parts of the world rely on roadsurface condition information during and after a snow storm to optimize maintenance operationsand planning. With an ever increasing demand for safer and more sustainable roadnetwork there is an ever increasing demand for more reliable, accurate and up-to-date roadsurface condition information while working with the limited available resources. Such highdependence on road condition information is driving more and more attention towards analyzingthe reliability of current technology as well as developing new and more innovativemethods for monitoring road surface condition. This research provides an overview of thevarious road condition monitoring technologies in use today. A new machine vision basedmobile road surface condition monitoring system is proposed which has the potential toproduce high spatial and temporal coverage. The proposed approach uses multiple modelscalibrated according to local pavement color and environmental conditions potentiallyproviding better accuracy compared to a single model for all conditions. Once fully developed,this system could potentially provide intermediate data between the more reliable xed monitoring stations, enabling the authorities with a wider coverage without a heavyextra cost. The up to date information could be used to better plan maintenance strategiesand thus minimizing salt use and maintenance costs.
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An Automatic Image Recognition System for Winter Road Condition Monitoring
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