A Case Study on Multilane Roundabout Capacity Evaluation Using Computer Vision and Deep Learning
Publication: Journal of Computing in Civil Engineering
Volume 36, Issue 3
Abstract
Modern roundabouts are popular intersection control designs in many countries and are increasingly popular in the United States. Roundabouts facilitate reduced vehicle delays with naturally optimized conflict resolution for turning traffic, which also reduces the risks of severe crashes. However, evaluating the roundabout capacity for multilane configurations can be challenging due to the randomized decision making to accept or reject a headway to enter the roundabout. In addition, considering the follow-up headway between two vehicles entering the roundabout from the same lane is critical to evaluate accurate roundabout capacity. Several manual techniques are popularly used to evaluate roundabout capacity using computer vision powered by multiple video cameras and observers. However, manual processing of videos with a narrow field of view (FoV) requires significant computational effort. Traditional techniques used in manual processing involve a complex two-step time stamp recording and interpreting the parameters required for capacity evaluation. In this case study, a one-step gap-based methodology is proposed to accurately measure the roundabout capacity parameters. In addition, a computer vision algorithm is developed to integrate with deep learning to detect and track vehicular traffic in a multilane roundabout. A software-defined technique is developed to process videos with wider FoV powered by unmanned aerial vehicles (UAVs) and evaluate roundabout capacity parameters, such as accept, reject, and follow-up headways. Furthermore, the mean critical headway is calculated using a maximum likelihood estimation method. The evaluated roundabout capacity parameters are compared with manual technique results, and the corresponding values are published in the current standards.
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Data Availability Statement
Some or all of the data (collected video data, trained deep learning model, and code files) used during the study are proprietary or confidential in nature and may only be provided with restrictions. The data collection for this project was done in collaboration with the University of Cincinnati and the Ohio Department of Transportation (ODOT) through a funded project. Any access to the data will be through the University of Cincinnati and ODOT.
Acknowledgments
We wish to express our sincere gratitude to ODOT for funding this research work and to Kittelson & Associates for helping with the validation of results.
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Information & Authors
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Published In
Journal of Computing in Civil Engineering
Volume 36 • Issue 3 • May 2022
Copyright
© 2022 American Society of Civil Engineers.
History
Received: Mar 3, 2021
Accepted: Oct 22, 2021
Published online: Feb 7, 2022
Published in print: May 1, 2022
Discussion open until: Jul 7, 2022
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