This study explores the feasibility of improving road work zone safety by using state-of-the-art Internet of Things (IoT), artificial intelligence (AI), and computer vision technologies. This project included in-depth analysis of the key technologies and systems that have the potential to improve work zone safety. In order to gain an understanding of the major triggers of the most harmful crashes in work zones, the project team analyzed the crash data in North Carolina. Driven by insights gained through an extensive literature review and the analysis of North Carolina work zone crash data, this project developed two proof-of-concept systems using IoT, AI, and computer vision technologies for work zone safety. The developed systems provide capabilities for two functions: (1) work zone intrusion warning and (2) vehicle queue detection. The proof-of-concept intrusion alert system comprised a mobile device attached on a tripod to monitor a restricted area and a system to alert the workers when an intrusion occurs. The workers receive alerts instantly through alarm sounds and vibrations generated by their mobile devices. The systems were tested using a simulated test environment and the findings of the tests indicated their potential to provide a robust technical approach. A proof-of-concept queue warning system was also developed and tested. The results indicated its potential to be used in smart work zones as a low-cost and easy-to-deploy system. Both systems were implemented with the capability to run on Android smartphones. However, the software is extremely portable, and therefore, the technical design can be embedded in any type of hardware. This report also identifies three commercially available devices that have good potential to be used in the field as part of a smart work zone to improve the work zone safety.
Publication Date: 2020
Full Text URL: Link to URL
Publication Types: Books, Reports, Papers, and Research Articles
Topics: Artificial Intelligence; Crash Data; Intelligent Transportation Systems; Intrusion Alarms; Traffic Queuing; Warning Systems; Work Zone Safety; Work Zones; Worker Safety