United States Department of Transportation
i
Fly-By Image Processing for Real-Time Congestion Mitigation [supporting datasets]
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2021-04-14
[PDF-170.12 KB]
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Alternative Title:STRIDE Project: Fly-By Image Processing for Real Time Congestion Mitigation (Project H2)
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Abstract:Traffic monitoring is the centerpiece of congestion mitigation and traffic management. Whilst surveillance technologies have matured enough to provide informative depiction for the traffic, the current state-of-the-art systems cannot support immediate congestion problems. Proactive congestion mitigation requires a) real-time surveillance for traffic parameters, b) prediction for imminent congestion onset, to: c) inform responsible parties to take immediate actions to prevent congestion. The proposed congestion mitigation approach is based on the premise that a short time analysis (1-5 minutes) will be sufficient to manage the congestion. We foresee that using a “flock” of interconnected, self-managed drones, can establish a deployable system to perform immediate monitoring/assessment for traffic conditions to infer if congestion is approached. To detect vehicles, a faster technique of Convolutional Neural Network (CNN) called YOLOv3 is used. In this technique, a single neural network is used to the full image which divides the image into regions and predicts bounding boxes and probabilities for each region. Then these bounding boxes are weighted by the predicted probabilities. This technique requires huge computational power and therefore, GPUs are used to process the videos recorded by drones’ cameras. By calibrating the camera using real values compared to their apparent values in images, the detected vehicles can be tracked. The targeted feature (herein, features correlated to traffic congestion) were reproduced utilizing a traffic simulation model. The proposed methodology was tested by collecting and investigating video images from drones. The project, if continued further, has the potential to advance the state of proactive traffic and congestion management by embedding a distributed, simulation-based traffic state prediction system within the integrated drone surveillance software to enable congestion mitigation actions to be undertaken before congestion happens rather than after traffic flow has already broken down.
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