Reliable travel time prediction enables both road users and system controllers to be well informed about future conditions on roadways so that pretrip plans and traffic control strategies can be made to reduce travel time and relieve traffic congestion. The objective of this research was to use traffic and weather data from multiple data sources to develop an integrated model that could predict travel times under various weather conditions, especially severe weather conditions. Prediction models are compared, and their performance in case studies is investigated.
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