Roadway construction workers have to work in close proximity to construction equipment as well as high-speed traffic, exposing them to an elevated risk of collisions. This research aims to develop an innovative holistic solution to reduce the risk of collisions at roadway work zones. To this end, a connected hazard detection and prevention system is developed to detect potential unsafe proximities in highway work zones and provide warning and instructions of imminent threats. This connected system collects real-time information from all the actors inside and outside of the work zone and communicates it with a cloud server. A hazard detection algorithm is developed to identify potential proximity hazards between workers and connected/automated vehicles (CAV) and/or construction equipment. Detected imminent threats are communicated to in-danger workers and/or drivers.
The trajectories and safety status of each actor is visualized on Virginia Connected Corridors (VCC) Monitor, a custom web-based situational awareness tool, in real-time. To assure the accuracy of hazard detection, the algorithm accommodates various parameters including variant threat zones for workers-on-foot, vehicles, and equipment, the direction of movement, workers' distance to the work zone border, shape of road, etc. The designed system is developed and evaluated through various experiments on the Virginia's Smart Roads located at Virginia Tech.
Data regarding activities of workers-on-foot was collected during experiments and was used and classified for activity recognition using supervised machine learning methods. A demonstration was held to evaluate the usability of the developed system, and the results proved the efficacy of the algorithm in successfully detecting potential collisions and provide prompt warnings and instructions.
The developed holistic system elevates safety of highway construction and maintenance workers at work sites. It also helps managers and inspectors to keep track of the real-time safety status of their work zone actors as well as the accidents occurrences. As such, with the connected work zone hazard detection system, the safety level and productivity of the workers is expected to be greatly enhanced. / Master of Science / In order to reduce the risk of collisions for roadway construction workers, this research aims to develop an innovative holistic solution at roadway work zones. In this research, a connected hazard detection and prevention system is developed to detect potential collision hazards in highway work zones and generate warning and instructions of imminent threats. This system collects real-time information from all the workers, construction equipment and connected/automated vehicles (CAV) of the work. A hazard detection algorithm is developed to identify potential proximity hazards between them as well as to recognize the activities of workers. The trajectories and safety status of each worker, equipment or vehicle is visualized on Virginia Connected Corridors (VCC) Monitor, a custom web-based tool, in real-time. A demonstration was held to evaluate the developed system, and the results proved the efficacy of the algorithm in successfully detecting potential collisions and provide prompt warnings and instructions. The developed holistic system helps managers and inspectors to keep track of the real-time safety status of their work zone worker, equipment and vehicles as well as the accidents occurrences. As such, with the connected work zone hazard detection system, the safety level and productivity of the workers is expected to be greatly enhanced.
| Identifer | oai:union.ndltd.org:VTETD/oai:vtechworks.lib.vt.edu:10919/90896 |
| Date | 02 July 2019 |
| Creators | Han, Wenjun |
| Contributors | Industrial and Systems Engineering, Madigan, Michael L., Roofigari-Esfahan, Nazila, Srinivasan, Divya |
| Publisher | Virginia Tech |
| Source Sets | Virginia Tech Theses and Dissertation |
| Detected Language | English |
| Type | Thesis |
| Format | ETD, application/pdf |
| Rights | In Copyright, http://rightsstatements.org/vocab/InC/1.0/ |
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