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Estimation of Lane-Level Traffic Flow Using a Deep Learning Technique

Department of Electrical Engineering, National Cheng-Kung University, Tainan City 70101, Taiwan
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Author to whom correspondence should be addressed.
Academic Editor: Tiago M. Fernández-Caramés
Appl. Sci. 2021, 11(12), 5619; https://doi.org/10.3390/app11125619
Received: 17 May 2021 / Revised: 8 June 2021 / Accepted: 11 June 2021 / Published: 17 June 2021
(This article belongs to the Topic Intelligent Transportation Systems)
This paper proposes a neural network that fuses the data received from a camera system on a gantry to detect moving objects and calculate the relative position and velocity of the vehicles traveling on a freeway. This information is used to estimate the traffic flow. To estimate the traffic flows at both microscopic and macroscopic levels, this paper used YOLO v4 and DeepSORT for vehicle detection and tracking. The number of vehicles passing on the freeway was then calculated by drawing virtual lines and hot zones. The velocity of each vehicle was also recorded. The information can be passed to the traffic control center in order to monitor and control the traffic flows on freeways and analyze freeway conditions. View Full-Text
Keywords: traffic flow; object detection; object tracking; deep learning traffic flow; object detection; object tracking; deep learning
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MDPI and ACS Style

Liu, C.-M.; Juang, J.-C. Estimation of Lane-Level Traffic Flow Using a Deep Learning Technique. Appl. Sci. 2021, 11, 5619. https://doi.org/10.3390/app11125619

AMA Style

Liu C-M, Juang J-C. Estimation of Lane-Level Traffic Flow Using a Deep Learning Technique. Applied Sciences. 2021; 11(12):5619. https://doi.org/10.3390/app11125619

Chicago/Turabian Style

Liu, Chieh-Min, and Jyh-Ching Juang. 2021. "Estimation of Lane-Level Traffic Flow Using a Deep Learning Technique" Applied Sciences 11, no. 12: 5619. https://doi.org/10.3390/app11125619

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