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Article

An Analytical Framework for Accurate Traffic Flow Parameter Calculation from UAV Aerial Videos

1
Chair of Geoinformatics, Faculty of Geodesy, University of Zagreb, Kačićeva 26, 10000 Zagreb, Croatia
2
Department of Transport Planning, Faculty of Transport and Traffic Sciences, University of Zagreb, Vukelićeva 4, 10000 Zagreb, Croatia
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(22), 3844; https://doi.org/10.3390/rs12223844
Received: 25 September 2020 / Revised: 19 November 2020 / Accepted: 21 November 2020 / Published: 23 November 2020
Unmanned Aerial Vehicles (UAVs) represent easy, affordable, and simple solutions for many tasks, including the collection of traffic data. The main aim of this study is to propose a new, low-cost framework for the determination of highly accurate traffic flow parameters. The proposed framework consists of four segments: terrain survey, image processing, vehicle detection, and collection of traffic flow parameters. The testing phase of the framework was done on the Zagreb bypass motorway. A significant part of this study is the integration of the state-of-the-art pre-trained Faster Region-based Convolutional Neural Network (Faster R-CNN) for vehicle detection. Moreover, the study includes detailed explanations about vehicle speed estimation based on the calculation of the Mean Absolute Percentage Error (MAPE). Faster R-CNN was pre-trained on Common Objects in COntext (COCO) images dataset, fine-tuned on 160 images, and tested on 40 images. A dual-frequency Global Navigation Satellite System (GNSS) receiver was used for the determination of spatial resolution. This approach to data collection enables extraction of trajectories for an individual vehicle, which consequently provides a method for microscopic traffic flow parameters in detail analysis. As an example, the trajectories of two vehicles were extracted and the comparison of the driver’s behavior was given by speed—time, speed—space, and space—time diagrams. View Full-Text
Keywords: image processing; object detection; traffic data collection; traffic flow parameters; UAVs image processing; object detection; traffic data collection; traffic flow parameters; UAVs
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MDPI and ACS Style

Brkić, I.; Miler, M.; Ševrović, M.; Medak, D. An Analytical Framework for Accurate Traffic Flow Parameter Calculation from UAV Aerial Videos. Remote Sens. 2020, 12, 3844. https://doi.org/10.3390/rs12223844

AMA Style

Brkić I, Miler M, Ševrović M, Medak D. An Analytical Framework for Accurate Traffic Flow Parameter Calculation from UAV Aerial Videos. Remote Sensing. 2020; 12(22):3844. https://doi.org/10.3390/rs12223844

Chicago/Turabian Style

Brkić, Ivan, Mario Miler, Marko Ševrović, and Damir Medak. 2020. "An Analytical Framework for Accurate Traffic Flow Parameter Calculation from UAV Aerial Videos" Remote Sensing 12, no. 22: 3844. https://doi.org/10.3390/rs12223844

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