Ozan, Erol; Yuanyuan Fu; and Brian Dunn
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Work zone crashes pose a significant problem due to their impact on safety and mobility. This study assessed the feasibility of using Internet of Things (IoT), artificial intelligence (AI), and computer vision technologies to improve roadway work zone safety. Two proof-of-concept systems were developed: (i) a work zone intrusion warning system and (ii) a vehicle queue detection system. The work zone intrusion alert system comprised a mobile device attached on a tripod to monitor the restricted area and that run a software application designed to alert workers when an intrusion occurs. The workers received alerts instantly through sounds and vibrations generated by their mobile devices. The findings of the tests indicated its potential to provide a robust technical approach to improving work zone safety. The queue detection system was designed to detect stopped vehicles inside a polygon area selected by the user to denote the work zone area. Both systems utilized AI-based object detection methods and were tested using publicly available highway traffic videos as the simulated test environment. The developed systems demonstrated the feasibility of building low-cost AI and computer vision-based systems that could be used to build smart work zones.

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Prepared by East Carolina University for the North Carolina DOT
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Using IoT Technology to Create Smart Work Zones
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