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https://www.nationalacademies.org/trb/events

Enhancing Traffic Delay Prediction Utilizing Data-Driven Techniques

A model that accurately predicts both traffic delays and the queues that result from work zones would be a valuable tool to Arizona Department of Transportation (ADOT), helping the agency to manage traffic, enhance work zone planning, reduce congestion, and improve road safety. Currently, ADOT lacks the ability to generate estimates of congestion and delays that result from lane closures and other forms of planned or unplanned roadway capacity reduction. Instead, the agency relies on rough generalities to manage traffic and maintain safe operating conditions around work zones. Integrating a data-driven model—one that is based on roadway capacity and travel demand—into the work-zone management process would help the Traffic Operations Center (TOC) and other ADOT groups respond to both planned and unplanned traffic-delay events. Information that predicts potential problems before they occur could help the TOC prepare more efficiently for closures and other events by anticipating messaging and communication needs to the traveling public.