Abstract
Highway queue warning provides early congestion estimation to motorists and reduces potential delay by allowing them to either change lanes or take alternate routes. It can facilitate effective highway operations as well as lower energy consumption and greenhouse gas emission by preventing traffic from congestion. Quality of the estimation depends heavily on real-time traffic data. This paper proposes a framework for early queue warning and presents an initial effort on intelligent network flow optimization with near real-time data collected by passive sensory systems. Along with near real-time traffic data, highway performance is visualized and traffic congestion can be observed from its beginnings. The framework not only shows a systematic approach to conduct queue warning for highway operations but also severs as a platform to bridge the demand and supply of sensing technologies. Primary components of the framework and future needs on traffic data are also discussed.
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