Skip to main content

Traffic Incident Recognition Using Empirical Deep Convolutional Neural Networks Model

Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST,volume 217)


Traffic incident detection plays an important role for a broad range of intelligent transport systems and applications such as driver- assistant, accident warning, and traffic data analysis. The primary goal of traffic incident detection systems in real-world is to identify traffic violations happening on the road in real-time. Although research community has made a significant attempt for detecting on-road violations, there are still challenges such as poor performance under real-world circumstances and real-time detection. In this paper, we propose a novel method which utilizes the powerful deep convolutional neural networks for vehicle recognition task to detect traffic events on the separate lane. Experimental results on real-world dataset videos as well as live stream in real-time from digital cameras demonstrate the feasibility and effectiveness of the proposed method for identifying incidents under various conditions of urban roads and highways.


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

Buying options

USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
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

Learn about institutional subscriptions


  1. Khattak, A., Wang, X., Zhang, H.: Incident management integration tool. IET Intell. Transp. Syst. 6(2), 204–214 (2012)

    Article  Google Scholar 

  2. Wang, J., Li, X., Stephen, S., et al.: A hybrid approach for automatic incident detection. IEEE Trans. Intell. Transp. Syst. 14(3), 1176–1185 (2013)

    Article  Google Scholar 

  3. Ahmed, F., Hawas, Y.E.: A fuzzy logic model for real-time incident detection in urban road network. In: Proceedings of International Conference on Agents and Artificial Intelligence, Barcelona, Spain, pp. 465–472, February 2013

    Google Scholar 

  4. Dipti, S., Xin, J., Ruey, L.C.: Evaluation of adaptive neural network models for freeway incident detection. IEEE Trans. Intell. Transp. Syst. 5(1), 1–11 (2004)

    Article  Google Scholar 

  5. Xiao, J., Liu, Y.: Traffic incident detection by multiple kernel support vector machine ensemble. In: Proceedings of the IEEE International Conference on Intelligent (2012)

    Google Scholar 

  6. Ren, J., et al.: Detecting and positioning of traffic incidents via video-based analysis of traffic states in a road segment. IET Int. Trans. Syst. (ITS) 10(6), 428–437 (2016). Transportation Systems (ITS), Anchorage, USA, September 2012

    Article  Google Scholar 

  7. Tian, Q.F., Chen, Y.Z., Zhang, L.G.: Incident detection in urban freeway traffic based on adaptive algorithm. J. Trans. Eng. Inf. 8(4), 99–103, 125 (2010)

    Google Scholar 

  8. Chakraborty, P., et al.: Outlier Mining Based Traffic Incident Detection Using Big Data Analytics. No. 17-05869 (2017)

    Google Scholar 

  9. Asakura, Y., et al.: Incident detection methods using probe vehicles with on-board GPS equipment. Transp. Res. Part C: Emerg. Technol. 81, 330–341 (2016)

    Article  Google Scholar 

  10. Gu, Y., Qian, Z.S., Chen, F.: From Twitter to detector: real-time traffic incident detection using social media data. Transp. Res. Part C: Emerg. Technol. 67, 321–342 (2016)

    Article  Google Scholar 

  11. Steenbruggen, J., Tranos, E., Rietveld, P.: Traffic incidents in motorways: an empirical proposal for incident detection using data from mobile phone operators. J. Transp. Geogr. 54, 81–90 (2016)

    Article  Google Scholar 

  12. Jia, Y., et al.: Caffe: convolutional architecture for fast feature embedding. In: Proceedings of the 22nd ACM International Conference on Multimedia. ACM (2014)

    Google Scholar 

  13. Szegedy, C., et al.: Going deeper with convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2015)

    Google Scholar 

  14. Bosch, A., Zisserman, A., Munoz, X.: Representing shape with a spatial pyramid kernel. In: Proceedings of the 6th ACM International Conference on Image and Video Retrieval. ACM (2007)

    Google Scholar 

  15. Pham, C., et al.: FoodBoard: surface contact imaging for food recognition. In: Proceedings of the 2013 ACM International Joint Conference on Pervasive and Ubiquitous Computing. ACM (2013)

    Google Scholar 

  16. Vu, H.N., Tran, T.A., Na, I.S., Kim, S.H.: Automatic extraction of text regions from document images by multilevel thresholding and k-means clustering. In: 2015 IEEE/ACIS 14th International Conference on Computer and Information Science (ICIS), pp. 329–334. IEEE, June 2015

    Google Scholar 

  17. Zivkovic, Z.: Improved adaptive Gaussian mixture model for background subtraction. In: Proceedings of the 17th International Conference on Pattern Recognition, ICPR 2004, vol. 2. IEEE (2004)

    Google Scholar 

Download references

Author information

Authors and Affiliations


Corresponding author

Correspondence to Nam Vu .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2018 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Vu, N., Pham, C. (2018). Traffic Incident Recognition Using Empirical Deep Convolutional Neural Networks Model. In: Cong Vinh, P., Ha Huy Cuong, N., Vassev, E. (eds) Context-Aware Systems and Applications, and Nature of Computation and Communication. ICTCC ICCASA 2017 2017. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 217. Springer, Cham.

Download citation

  • DOI:

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-77817-4

  • Online ISBN: 978-3-319-77818-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics