Transportation agencies manage hundreds of active work zones, often spread across large geographic regions. Traditionally, verifying compliance with maintenance of traffic (MOT) plans has required in-person inspections by staff or contractors which is time-consuming, resource-intensive, and often requires extensive driving with an image or video recording device. With tight staffing requirements, it is only possible to inspect a small subset of work zones using field visits. The emergence of commercial truck dash cameras that can provide images at 1 second intervals from several trucks a day now enables an agency to review dash camera images to “virtually” drive their work zones. This has the benefit of not only drastically reducing travel time and costs, but also provides an opportunity to perform repetitive, weekly inspections of the work zones to determine compliance with an agency’s practices. A series of case studies covering sign placement, lane configuration, temporary work zones, maintenance closures, and pavement marking applications is presented. The paper concludes with an example of two different artificial intelligence (AI) models that can be used to process these images to determine the presence of a work zone on interstate roads. Across a suite of 40 images from 8 states and 20 interstate routes, both models performed very well and demonstrate considerable opportunity to integrate commercial dash camera images with AI models to screen work zones at scale for further human review.
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