Vol. 8 No. 1 (2022): Special Issue on Embedded Systems for Computer Vision
Special Issue on Embedded Systems for Computer Vision

Micro- and Macroscopic Road Traffic Analysis using Drone Image Data

Friedrich Kruber
Technische Hochschule Ingolstadt, Esplanade 10, Ingolstadt, Germany
Eduardo Sánchez Morales
Technische Hochschule Ingolstadt, Esplanade 10, Ingolstadt, Germany
Robin Egolf
Technische Hochschule Ingolstadt, Esplanade 10, Ingolstadt, Germany
Jonas Wurst
Technische Hochschule Ingolstadt, Esplanade 10, Ingolstadt, Germany
Samarjit Chakraborty
University of North Carolina at Chapel Hill (UNC), Department of Computer Science, NC 27599, USA
Michael Botsch
Technische Hochschule Ingolstadt, Esplanade 10, Ingolstadt, Germany

Published 2022-11-16


  • traffic data analysis,
  • trajectory data,
  • drone image data

How to Cite

Kruber, F., Sánchez Morales, E., Egolf, R., Wurst, J., Chakraborty, S. and Botsch, M. 2022. Micro- and Macroscopic Road Traffic Analysis using Drone Image Data. Leibniz Transactions on Embedded Systems. 8, 1 (Nov. 2022), 02:1–02:27. DOI:https://doi.org/10.4230/LITES.8.1.2.


The current development in the drone technology, alongside with machine learning based image processing, open new possibilities for various applications. Thus, the market volume is expected to grow rapidly over the next years. The goal of this paper is to demonstrate the capabilities and limitations of drone based image data processing for the purpose of road traffic analysis.
In the first part a method for generating microscopic traffic data is proposed. More precisely, the state of vehicles and the resulting trajectories are estimated. The method is validated by conducting experiments with reference sensors and proofs to achieve precise vehicle state estimation results. It is also shown, how the computational effort can be reduced by incorporating the tracking information into a neural network. A discussion on current limitations supplements the findings. By collecting a large number of vehicle trajectories, macroscopic statistics, such as traffic flow and density can be obtained from the data. In the second part, a publicly available drone based data set is analyzed to evaluate the suitability for macroscopic traffic modeling. The results show that the method is well suited for gaining detailed information about macroscopic statistics, such as traffic flow dependent time headway or lane change occurrences. In conclusion, this paper presents methods to exploit the remarkable opportunities of drone based image processing for joint macro- and microscopic traffic analysis.


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