[go: up one dir, main page]

Skip to main content

Traffic Monitoring on City Roads Using UAVs

  • Conference paper
  • First Online:
Ad-Hoc, Mobile, and Wireless Networks (ADHOC-NOW 2019)

Part of the book series: Lecture Notes in Computer Science ((LNCCN,volume 11803))

Included in the following conference series:

  • 1973 Accesses

Abstract

Unmanned Aerial Vehicles (UAVs) based systems are a suitable solution for monitoring, more particularly for traffic monitoring. The mobility, the low cost, and the broad view range of UAVs make them an attractive solution for traffic monitoring of city roads. UAVs are used to collect and send information about vehicles and unusual events to a traffic processing center, for traffic regulation. Existing UAVs based systems use only one UAV with a fixed trajectory. In this paper, we are using multiple cooperative UAVs to monitor the road traffic. This approach is based on adaptive UAVs trajectories, adjusted by moving points in UAVs fields of view. We introduced a learning phase to search for events locations with a frequent occurrence and to place UAVs above those locations. Our approach allows the detection of a lot of events and permits the reduction of UAVs energy consumption.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Kim, N.V., Chervonenkis, M.A.: Situation control of unmanned aerial vehicles for road traffic monitoring. Modern Appl. Sci. 9(5), 1 (2015)

    Google Scholar 

  2. Coifman, B., McCord, M., et al.: Roadway traffic monitoring from an unmanned aerial vehicle. In: IEE Proceedings-Intelligent Transport Systems, IET 2006, vol. 153, no. 1, pp. 11–20 (2006)

    Google Scholar 

  3. Rasmussen, S., Kalyanam, K., et al.: Field experiment of a fully autonomous multiple UAV/UGS intruder detection and monitoring system. In: 2016 International Conference on Unmanned Aircraft Systems (ICUAS), pp. 1293–1302. IEEE (2016)

    Google Scholar 

  4. Ke, R., Li, Z., et al.: Real-time bidirectional traffic flow parameter estimation from aerial videos. IEEE Trans. Intell. Transp. Syst. 18, 890–901 (2016)

    Article  Google Scholar 

  5. Pattanaik, V., Singh, M., et al.: Smart real-time traffic congestion estimation and clustering technique for urban vehicular roads. In: IEEE Region 10 Conference (TENCON), pp. 3420–3423. IEEE (2016)

    Google Scholar 

  6. Pongpaibool, P., Tangamchit, P., et al.: Evaluation of road traffic congestion using fuzzy techniques. In: 2007 IEEE Region 10 Conference, TENCON 2007, pp. 1–4. IEEE (2007)

    Google Scholar 

  7. Abdelhafid, Z., Harrou, F., et al.: An efficient statistical-based approach for road traffic congestion monitoring. In: 2017 5th International Conference on Electrical Engineering-Boumerdes (ICEE-B), pp. 1–5. IEEE (2017)

    Google Scholar 

  8. More, R., Mugal, A., et al.: Road traffic prediction and congestion control using artificial neural networks. In: International Conference on Computing, Analytics and Security Trends (CAST), pp. 52–57. IEEE (2016)

    Google Scholar 

  9. Fouladgar, M., Parchami, M., et al.: Scalable deep traffic flow neural networks for urban traffic congestion prediction. In: 2017 International Joint Conference on Neural Networks (IJCNN), pp. 2251–2258. IEEE (2017)

    Google Scholar 

  10. Al Najada, H., Mahgoub, I.: Anticipation and alert system of congestion and accidents in VANET using big data analysis for intelligent transportation systems. In: 2016 IEEE Symposium Series on Computational Intelligence (SSCI), pp. 1–8. IEEE (2016)

    Google Scholar 

  11. El Khatib, A., Mourad, A., et al.: A cooperative detection model based on artificial neural network for VANET QoS-OLSR protocol. In: 2015 IEEE International Conference on Ubiquitous Wireless Broadband (ICUWB), pp. 1–5. IEEE (2015)

    Google Scholar 

  12. Leduc, G.: Road traffic data: collection methods and applications. Working Papers on Energy, Transport and Climate Change, vol. 1, no. 55 (2008)

    Google Scholar 

  13. Wang, L., Chen, F., et al.: Detecting and tracking vehicles in traffic by unmanned aerial vehicles. Autom. Constr. 72, 294–308 (2016)

    Article  Google Scholar 

  14. Reshma, R., Ramesh, T., et al.: Security situational aware intelligent road traffic monitoring using UAVs. In: 2016 International Conference on VLSI Systems, Architectures, Technology and Applications (VLSI-SATA), pp. 1–6. IEEE (2016)

    Google Scholar 

  15. Abdulla, A.E., Fadlullah, Z.M., et al.: An optimal data collection technique for improved utility in UAS-aided networks. In: 2014 Proceedings IEEE INFOCOM, pp. 736–744. IEEE (2014)

    Google Scholar 

  16. Guido, G., Gallelli, V., Rogano, D., Vitale, A.: Evaluating the accuracy of vehicle tracking data obtained from Unmanned Aerial Vehicles. Int. J. Transp. Sci. Technol. 5, 136–151 (2016)

    Article  Google Scholar 

  17. Rosenbaum, D., Kurz, F., et al.: Towards automatic near real-time traffic monitoring with an airborne wide angle camera system. Eur. Transp. Res. Rev. 1(1), 11–21 (2009)

    Article  Google Scholar 

  18. Elloumi, M., Dhaou, R., et al.: Monitoring road traffic with a UAV-based system. In: 2018 IEEE Wireless Communications and Networking Conference (WCNC), pp. 1–6. IEEE (2018)

    Google Scholar 

  19. Ongsulee, P.: Artificial intelligence, machine learning and deep learning. In: 2017 15th International Conference on ICT and Knowledge Engineering (ICT&KE), pp. 1–6. IEEE (2017)

    Google Scholar 

  20. https://towardsdatascience.com/supervised-vs-unsupervised-learning-14f68e32ea8d. Accessed 22 May 2018

  21. http://crawdad.org/. Accessed 06 Apr 2018

  22. http://kolntrace.project.citi-lab.fr/. Accessed 06 Apr 2018

  23. Shu, W., Zheng, Z.: Performance analysis of Kalman-based filters and particle filters for non-linear/non-Gaussian Bayesian tracking. IFAC Proc. Vol. 38(1), 1131–1136 (2005)

    Article  Google Scholar 

  24. Martín, F., Veloso, M.: Effective real-time visual object detection. Prog. Artif. Intell. 1(4), 259–265 (2012)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding authors

Correspondence to Mouna Elloumi or Riadh Dhaou .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Elloumi, M., Dhaou, R., Escrig, B., Idoudi, H., Saidane, L.A., Fer, A. (2019). Traffic Monitoring on City Roads Using UAVs. In: Palattella, M., Scanzio, S., Coleri Ergen, S. (eds) Ad-Hoc, Mobile, and Wireless Networks. ADHOC-NOW 2019. Lecture Notes in Computer Science(), vol 11803. Springer, Cham. https://doi.org/10.1007/978-3-030-31831-4_42

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-31831-4_42

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-31830-7

  • Online ISBN: 978-3-030-31831-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics