TY  - JOUR
PY  - 2021//
TI  - Model free identification of traffic conditions using unmanned aerial vehicles and deep learning
JO  - Journal of big data analytics in transportation
A1  - Vlahogianni, Eleni I.
A1  - Del Ser, Javier
A1  - Kepaptsoglou, Konstantinos
A1  - Laña, Ibai
SP  - 1
EP  - 13
VL  - 3
IS  - 1
N2  - The purpose of this paper is to provide a methodological framework to identify traffic conditions based on non-calibrated video recordings captured from unmanned aerial vehicles (UAV) using deep learning. To this end, we propose two complementary to each other approaches: (i) identify in real time, with minimal computational cost, traffic conditions, (ii) localize, classify vehicles and approximate traffic variables (volume, speed, density) on a road segment from video captured by UAVs. Both problems are formulated as classification problems and tackled using Convolutional Neural Networks (CNN). The use of pre-trained CNNs is also investigated. Both approaches are, then, analysed based on their accuracy and feasibility in implementation. <br><br>FINDINGS indicate that all models developed achieve a detection accuracy of 89% and higher. The CNN with the best performance can classify traffic conditions between constrained and unconstrained traffic with 91% accuracy higher than what a pretrained model achieved and with significantly faster training times. Furthermore, findings indicated that pretrained neural network for traffic localization was able to predict the position and type of vehicles with a precision of 0.91. Based on the fundamental traffic diagram, it was shown that the two approaches provide compatible results and a feasible representation of traffic on the study area. Finally, possible applications in the field of transportation and traffic monitoring are discussed.<p />  <p>Language: en</p>
LA  - en
SN  - 2523-3556
UR  - http://dx.doi.org/10.1007/s42421-021-00038-z
ID  - ref1
ER  -