Skip to main content
SpringerLink
Log in

Traffic Congestion Identification and Reduction

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

Historical data is increasingly becoming more purposeful in the field of intelligent traffic systems. Traffic congestion is one of the major challenges in most cities around the world. It increases fuel wastage, monetary losses, and life-endangering. Historical trajectory data may be a suitable solution to reduce congestion on the road in VANET. This approach is a combination of data mining historical trajectory data to detect and predict traffic congestion and VANET in reducing the detected congestion events. The trajectories are first preprocessed before they are clustered using traj clustering algorithm. Congestion detection is then done for each cluster based on a certain speed threshold and also time duration of that particular event. The experiments are done on a data set which recorded traffic trajectories for one day and the same experiment is iterated on data sets for the day two and three respectively day and the detected congestion incidents are recorded in terms of their coordinates and time period in which they occurred. After detecting the congestion, we have also proposed a traffic congestion reduction solution. Results and simulations show that our proposed mechanism is suitable in VANET.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

References

  1. Amelia, A., & Saptawati, P. (2014). Detection of potential traffic jam based on traffic characteristic data analysis. In International conference on data and software engineering (ICODSE) (pp. 1–5).

  2. Xu, L., Yue, Y., & Li, Q. (2013). Identifying urban traffic congestion pattern from historical floating car data. Procedia—Social and Behavioral Sciences, 96, 2084–2095.

    Article  Google Scholar 

  3. Ong, R., Pinelli, F., Trasarti, R., Nanni, M., Renso, C., Rinzivillo, S., & Giannotti, F. (2011) Traffic jams detection using flock mining. In European conference (ECML PKDD-11) (pp. 650–653).

  4. Benkert, M., Gudmundsson, J., Hübner, F., & Wolle, T. (2006) Reporting flock patterns. In 14th annual European symposium (pp. 111–125).

  5. Wang, Z., Min, L., Yuan, X., Zhang, J., & Van de Wetering, H. (2013). Visual traffic jam analysis based on trajectory data. IEEE Transactions on Visualization and Computer Graphics, 19(12), 2159–2168.

    Article  Google Scholar 

  6. Gupta, A., Netaji S., Choudhary, S., & Paul, S. (2013). DTC: A framework to detect traffic congestion by mining versatile GPS data. In 1st international conference proceedings of emerging trends and applications in computer science (ICETACS) (pp. 97–103).

  7. Florido, E., Castaño, O., Troncoso, A., & Martínez-Álvarez, F. (2015). Data mining for predicting traffic congestion and its application to Spanish data. In 10th international conference on soft computing models in industrial and environmental applications volume 368 of the series advances in intelligent systems and computing (pp. 341–351).

  8. Yang, Y., Cui, Z., Zhang, J. W. G., & Xian, X. (2012). Fuzzy C-means clustering and opposition-based reinforcement learning for traffic congestion identification. Journal of Information & Computational Science, 9(9), 2441–2450.

    Google Scholar 

  9. Jain, V., Sharma, A., & Subramanian, L. (2012). Road traffic congestion in the developing world. In Proceeding ACM DEV ‘12 Proceedings of the 2nd ACM symposium on computing for development (pp. 1–5).

  10. Ramon, B., Javier, G., & Joaquin, S, S. (2010). Road traffic congestion detection through cooperative vehicle-to-vehicle communications. In 35th IEEE conference on local computer networks (pp. 606–612).

  11. Milojevic, M., & Rakocevic, V. (2013). Distributed vehicular traffic congestion detection algorithm for urban environments. In Vehicular networking conference (VNC) (pp. 182–185).

  12. Roy, S., Bandyopadhyay, S., Das, M., Batabyal, S., & Pal, S. (2010). Real time traffic congestion detection and management using active RFID and GSM technology. In Proceedings of the 10th international conference on intelligent transport systems telecommunication.

  13. Sylvestre, M, P., & Vatniky, D. (2014). Using traj package to identify clusters of longitudinal trajectories. https://cran.r-project.org/web/packages/traj/vignettes/trajVignette. Accessed 29 March 2020.

  14. Leffondré, K., Abrahamowicz, M., Regeasse, A., Hawker, G. A., Badley, E. M., McCusker, J., et al. (2004). Statistical measures were proposed for identifying longitudinal patterns of change in quantitative health indicators. Journal of Clinical Epidemiology, 57(10), 1049–1062.

    Article  Google Scholar 

  15. Sylvestre, M. P., McCusker, J., Cole, M., Regeasse, A., Belzile, E., & Abrahamowicz, M. (2006). Classification of patterns of delirium severity scores over time in an elderly population. International Psychogeriatrics, 18(4), 667–680.

    Article  Google Scholar 

  16. Long, L., Baldessari, R., Salvador, P., Festag A., & Zhang, W. (2011). Performance evaluation of beacon congestion control algorithms for VANETs. In Proceedings of the global telecommunications conference (GLOBECOM 2011) (pp. 1–6).

  17. Yusof, D. M., & Abu, B. K. (2011). A review of congestion control algorithm for event-driven safety messages in vehicular networks. IJCSI International Journal of Computer Science Issues, 8(5), 49–53.

    Google Scholar 

  18. Yuan, J., Zheng, Y., Xie, X., & Sun, G. (2011). Driving with knowledge from the physical world. In The 17th ACM SIGKDD international conference on knowledge discovery and data mining (pp. 316–324).

  19. Yuan, J., Zheng, Y., Zhang, C., Xie, W., Xie, X., Sun, G., & Huang, Y. (2010). T-drive: Driving directions based on taxi trajectories. In Proceedings of the 18th SIGSPATIAL international conference on advances in geographic information systems, GIS ‘10 (pp. 99–108).

  20. Chan-Tong, L., Hanyang, G., & Benjamin, N. (2018). A real-time traffic congestion detection system using on-line images. In Proceedings of the 17th international conference on communication technology (ICCT) (pp. 1548–1552).

  21. Pranamesh, C., Yaw, O., Subhadipto, P., Vesa, A., Anuj, S., & Soumik, S. (2018). Traffic congestion detection from camera images using deep convolution neural networks. Journal of the Transportation Research Board, 2672(45), 222–231.

    Article  Google Scholar 

  22. Maja, K., & Jukka, M. (2019). Fuzzy inference approach in traffic congestion detection. Journal of Annals of GIS., 25(4), 329–336.

    Article  Google Scholar 

  23. Manjoro, W. S., Dhakar, M., & Chaurasia, B. K. (2016). Traffic congestion detection using data mining in VANET. In IEEE students’ conference on electrical, electronics and computer science (SCEECS), ISBN No: 978-1-4673-7918-2 (pp. 1–6).

  24. Xu, Y., Shixin, L., Keyan, G., Tingting, Q., & Xiaoya, C. (2019). Application of data science technologies in intelligent prediction of traffic congestion. The Journal of Advanced Transportation, 2019, 1–14.

    Article  Google Scholar 

  25. Spiridon, V., Vasileios, K., Georgios, T., Athanasios, T., & Konstantinos, O. (2020). A low-cost vehicular traffic monitoring system using fog computing. Journal of Smart Cities, 3(1), 138–156.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of IT, Indian Institute of Information Technology, Lucknow, India

    Brijesh Kumar Chaurasia

  2. Department of CSE, ITM University Gwalior, Gwalior, MP, India

    Wellington S. Manjoro

  3. Department of CSE, MGCGV, Chitrakoot, Satna, MP, India

    Mradul Dhakar

Authors
  1. Brijesh Kumar Chaurasia
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Wellington S. Manjoro
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mradul Dhakar
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Brijesh Kumar Chaurasia.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chaurasia, B.K., Manjoro, W.S. & Dhakar, M. Traffic Congestion Identification and Reduction. Wireless Pers Commun 114, 1267–1286 (2020). https://doi.org/10.1007/s11277-020-07420-0

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-020-07420-0

Keywords

Search

Navigation