Skip to main content
SpringerLink
Log in

Traffic Congestion Detection: Solutions, Open Issues and Challenges

  • Conference paper
  • First Online:
Distributed Computing for Emerging Smart Networks (DiCES-N 2020)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1348))

Abstract

In recent years, cities are experiencing a deep negative impact due to traffic congestion. As the number of vehicles is increasing rapidly, traffic congestion becomes unsustainable in urban domains specially in peak hours leading to time and fuel waste, accidents, additional costs for the economy, environmental problems and consequently impaired quality of life. Tremendous effort is done to provide solutions dealing with traffic congestion detection and monitoring. Based on used technologies, existing solutions can be classified into three categories: sensor technology, Vehicular Adhoc Networks and visual traffic surveillance. In this survey, we review the main recent works proposed in each category and discuss their strengths and weaknesses. We give an insight of future directions for handling congestion using the presented methods.

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

Access this chapter

Log in via an institution
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

References

  1. Afrin, T., Yodo, N.: A survey of road traffic congestion measures towards a sustainable and resilient transportation system. Sustainability 12(11), 4660 (2020)

    Article  Google Scholar 

  2. Sutandi, A.C.: Its impact on traffic congestion and environmental quality in large cities in developing countries. In Proceedings of the Eastern Asia Society for Transportation Studies, vol. 7 (The 8th International Conference of Eastern Asia Society for Transportation Studies, 2009), pp. 180–180. Eastern Asia Society for Transportation Studies (2009)

    Google Scholar 

  3. de Souza, A.M., Yokoyama, R.S., Maia, G., Loureiro, A., Villas, L.: Real-time path planning to prevent traffic jam through an intelligent transportation system. In: 2016 IEEE Symposium on Computers and Communication (ISCC), pp. 726–731. IEEE (2016)

    Google Scholar 

  4. Chetouane, A., Mabrouk, S., Jemili, I., Mosbah, M.: A comparative study of vehicle detection methods in a video sequence. In: Jemili, I., Mosbah, M. (eds.) DiCES-N 2019. CCIS, vol. 1130, pp. 37–53. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-40131-3_3

    Chapter  Google Scholar 

  5. An, S.-H., Lee, B.-H., Shin, D.-R.: A survey of intelligent transportation systems. In 2011 Third International Conference on Computational Intelligence, Communication Systems and Networks, pp. 332–337. IEEE (2011)

    Google Scholar 

  6. Dubey, P.P., Borkar, P.: Review on techniques for traffic jam detection and congestion avoidance. In: 2015 2nd International Conference on Electronics and Communication Systems (ICECS), pp. 434–440. IEEE (2015)

    Google Scholar 

  7. Bhandari, A., Patel, V., Patel, M: A survey on traffic congestion detection and rerouting strategies. In 2018 2nd International Conference on Trends in Electronics and Informatics (ICOEI), pp. 42–44. IEEE (2018)

    Google Scholar 

  8. Chetouane, A., Mabrouk, S., Jemili, I., Mosbah, M.: Vision-based vehicle detection for road traffic congestion classification. Concurr. Comput. Pract. Exp. 32, e5983 (2020)

    Google Scholar 

  9. El-Sersy, H., El-Sayed, A.: Survey of traffic congestion detection using vanet. Commun. Appl. Electron. 1(4), 14–20 (2015)

    Article  Google Scholar 

  10. Paranjothi, A., Khan, M.S., Zeadally, S.: A survey on congestion detection and control in connected vehicles. Ad Hoc Netw. 108, 102277 (2020)

    Article  Google Scholar 

  11. Zhang, Y., Sun, L., Song, H., Cao, X.: Ubiquitous WSN for healthcare: recent advances and future prospects. IEEE Internet Things J. 1(4), 311–318 (2014)

    Article  Google Scholar 

  12. Negra, R., Jemili, I., Belghith, A.: Wireless body area networks: applications and technologies. Procedia Comput. Sci. 83, 1274–1281 (2016)

    Article  Google Scholar 

  13. Negra, R., Jemili, I., Belghith, A., Mosbah, M.: MTM-MAC: medical traffic management MAC protocol for handling healthcare applications in WBANs. In: Palattella, M.R., Scanzio, S., Coleri Ergen, S. (eds.) ADHOC-NOW 2019. LNCS, vol. 11803, pp. 483–497. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-31831-4_33

    Chapter  Google Scholar 

  14. Ksouri, C., Jemili, I., Mosbah, M., Belghith, A.: Data gathering for internet of vehicles safety. In 2018 14th International Wireless Communications & Mobile Computing Conference (IWCMC), pp. 904–909. IEEE (2018)

    Google Scholar 

  15. Guerrero-Ibáñez, J., Zeadally, S., Contreras-Castillo, J.: Sensor technologies for intelligent transportation systems. Sensors 18(4), 1212 (2018)

    Article  Google Scholar 

  16. D’Andrea, E., Marcelloni, F.: Detection of traffic congestion and incidents from GPS trace analysis. Expert Syst. Appl. 73, 43–56 (2017)

    Article  Google Scholar 

  17. Kamran, S., Haas, O.: A multilevel traffic incidents detection approach: identifying traffic patterns and vehicle behaviours using real-time GPS data. In 2007 IEEE Intelligent Vehicles Symposium, pp. 912–917. IEEE (2007)

    Google Scholar 

  18. Yoon, J., Noble, B., Liu, M.: Surface street traffic estimation. In: Proceedings of the 5th International Conference on Mobile Systems, Applications and Services, pp. 220–232. ACM (2007)

    Google Scholar 

  19. Gupta, A., Choudhary, S., Paul, S.: DTC: a framework to detect traffic congestion by mining versatile GPS data. In 2013 1st International Conference on Emerging Trends and Applications in Computer Science, pp. 97–103. IEEE (2013)

    Google Scholar 

  20. Mandal, A., Sadhukhan, P., Gaji, F., Sharma, P.: Measuring real-time road traffic queue length: a reliable approach using ultrasonic sensor. In: Kundu, S., Acharya, U.S., De, C.K., Mukherjee, S. (eds.) Proceedings of the 2nd International Conference on Communication, Devices and Computing. LNEE, vol. 602, pp. 391–398. Springer, Singapore (2020). https://doi.org/10.1007/978-981-15-0829-5_38

    Chapter  Google Scholar 

  21. Prasetyo, M.A., Latuconsina, R., Purboyo, T.W.: A proposed design of traffic congestion prediction using ultrasonic sensors. Int. J. Appl. Eng. Res. 13(1), 434–441 (2018)

    Google Scholar 

  22. Mshelia, D.E., Alkali, A.H., Dada, E.G., Ismail, K.: Design and development of a traffic density detection and signal adjustment system (2019)

    Google Scholar 

  23. Barbagli, B., Manes, G., Facchini, R., Manes, A.: Acoustic sensor network for vehicle traffic monitoring. In: Proceedings of the 1st International Conference on Advances in Vehicular Systems, Technologies and Applications, pp. 24–29 (2012)

    Google Scholar 

  24. Yu, X., Prevedouros, P.D.: Performance and challenges in utilizing non-intrusive sensors for traffic data collection (2013)

    Google Scholar 

  25. Rekha, R., Karthika, R.: Fuzzy based traffic congestion detection & pattern analysis using inductive loop sensor. Int. J. Sci. Eng. Res. 4(6), 1149–1152 (2013)

    Google Scholar 

  26. Gholve, M.H., Chougule, S.: Traffic congestion detection for highways using wireless sensors. Int. J. Electron. Commun. Eng. 6(3), 259–265 (2013)

    Google Scholar 

  27. Nam, D., Lavanya, R., Yang, I., Jeon, W.H., Jayakrishnan, R: Traffic density estimation using radar sensor data from probe vehicles (2017)

    Google Scholar 

  28. Ghazal, B., ElKhatib, K., Chahine, K., Kherfan, M.: Smart traffic light control system. In 2016 Third International Conference on Electrical, Electronics, Computer Engineering and Their Applications (EECEA), pp. 140–145. IEEE (2016)

    Google Scholar 

  29. Atta, A., Abbas, S., Khan, M.A., Ahmed, G., Farooq, U.: An adaptive approach: smart traffic congestion control system. J. King Saud Univ. Comput. Inf. Sci. 32, 1012–1019 (2018)

    Google Scholar 

  30. Roy, S.: Real time traffic congestion detection and management using active RFID and GSM technology (2010)

    Google Scholar 

  31. Cherkaoui, B., Beni-Hssane, A., El Fissaoui, M., Erritali, M.: Road traffic congestion detection in VANET networks. Procedia Comput. Sci. 151, 1158–1163 (2019)

    Article  Google Scholar 

  32. Dey, K.C., Rayamajhi, A., Chowdhury, M., Bhavsar, P., Martin, J.: Vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication in a heterogeneous wireless network-performance evaluation. Transp. Res. Part C Emerg. Technol. 68, 168–184 (2016)

    Google Scholar 

  33. Tomar, R., Prateek, M., Sastry, G.H.: Vehicular adhoc network (VANET)-an introduction (2016)

    Google Scholar 

  34. Al-Fuqaha, A., Kwigizile, V., Oh, J., et al.: Vehicle-to-device (V2D) communications: readiness of the technology and potential applications for people with disability. Technical report, Western Michigan University (2018)

    Google Scholar 

  35. Wang, X., Mao, S., Gong, M.X.: An overview of 3GPP cellular vehicle-to-everything standards. GetMobile Mob. Comput. Commun. 21(3), 19–25 (2017)

    Article  Google Scholar 

  36. Lo, C.-C., Kuo, Y.-H.: Traffic-aware routing protocol with cooperative coverage-oriented information collection method for VANET. IET Commun. 11(3), 444–450 (2017)

    Article  Google Scholar 

  37. Chi, J., Do, S., Park, S.: Traffic flow-based roadside unit allocation strategy for VANET. In 2016 International Conference on Big Data and Smart Computing (BigComp), pp. 245–250. IEEE (2016)

    Google Scholar 

  38. Bauza, R., Gozálvez, J.: Traffic congestion detection in large-scale scenarios using vehicle-to-vehicle communications. J. Netw. Comput. Appl. 36(5), 1295–1307 (2013)

    Article  Google Scholar 

  39. Araújo, G.B., Queiroz, M.M., de LP Duarte-Figueiredo, F., Tostes, A.I.J., Loureiro, A.A.F.: CARTIM: a proposal toward identification and minimization of vehicular traffic congestion for VANET. In 2014 IEEE Symposium on Computers and Communications (ISCC), pp. 1–6. IEEE (2014)

    Google Scholar 

  40. Wei, L., Hong-ying, D.: Real-time road congestion detection based on image texture analysis. Procedia Eng. 137, 196–201 (2016)

    Article  Google Scholar 

  41. Kurniawan, J., Syahra, S.G.S., Dewa, C.K., et al.: Traffic congestion detection: learning from CCTV monitoring images using convolutional neural network. Procedia Comput. Sci. 144, 291–297 (2018)

    Article  Google Scholar 

  42. Nagaraj, U., Rathod, J., Patil, P., Thakur, S.V., Sharma, U., Nagaraj, P.: Traffic jam detection using image processing (2013)

    Google Scholar 

  43. Li, L., Chen, L., Huang, X., Huang, J.: A traffic congestion estimation approach from video using time-spatial imagery. In 2008 First International Conference on Intelligent Networks and Intelligent Systems, pp. 465–469. IEEE (2008)

    Google Scholar 

  44. Andrews Sobral, L.O., Schnitman, L., De Souza, F.: Highway traffic congestion classification using holistic properties. In: 10th IASTED International Conference on Signal Processing, Pattern Recognition and Applications (2013)

    Google Scholar 

  45. Xun, F.-F., Yang, X.-H., Xie, Y., Wang, L.-Y.: Congestion detection of urban intersections based on surveillance video. In: 2018 18th International Symposium on Communications and Information Technologies (ISCIT), pp. 495–498. IEEE (2018)

    Google Scholar 

  46. Kadhim, H.A., Araheemah, W.A.: A comparative between corner-detectors (Harris, Shi-Tomasi & FAST) in images noisy using non-local means filter. J. Al-Qadisiyah Comput. Sci. Math. 11(3), 86 (2019)

    Google Scholar 

  47. Bruhn, A., Weickert, J., Schnörr, C.: Lucas/kanade meets horn/schunck: combining local and global optic flow methods. Int. J. Comput. Vis. 61(3), 211–231 (2005). https://doi.org/10.1023/B:VISI.0000045324.43199.43

    Article  Google Scholar 

  48. Ke, X., Shi, L., Guo, W., Chen, D.: Multi-dimensional traffic congestion detection based on fusion of visual features and convolutional neural network. IEEE Trans. Intell. Transpo. Syst. 20(6), 2157–2170 (2018)

    Article  Google Scholar 

  49. Pamula, T.: Road traffic conditions classification based on multilevel filtering of image content using convolutional neural networks. IEEE Intell. Transp. Syst. Mag. 10(3), 11–21 (2018)

    Article  Google Scholar 

  50. Bernas, M., Płaczek, B., Korski, W., Loska, P., Smyła, J., Szymała, P.: A survey and comparison of low-cost sensing technologies for road traffic monitoring. Sensors 18(10), 3243 (2018)

    Article  Google Scholar 

  51. Hasrouny, H., Samhat, A.E., Bassil, C., Laouiti, A.: VANet security challenges and solutions: a survey. Veh. Commun. 7, 7–20 (2017)

    Google Scholar 

  52. Silva, C.M., Masini, B.M., Ferrari, G., Thibault, I.: A survey on infrastructure-based vehicular networks. Mob. Inf. Syst. 2017, 6123868 (2017)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Faculty of Sciences of Bizerte, University of Carthage, Tunis, Tunisia

    Ameni Chetouane & Sabra Mabrouk

  2. University of Bordeaux, CNRS, Bordeaux INP, LaBRI, UMR 5800, 33400, Bordeaux, Talence, France

    Mohamed Mosbah

Authors
  1. Ameni Chetouane
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sabra Mabrouk
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mohamed Mosbah
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ameni Chetouane .

Editor information

Editors and Affiliations

  1. University of Carthage, Zarzouna Bizerte, Tunisia

    Imen Jemili

  2. Bordeaux INP, France Talence, France

    Mohamed Mosbah

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Chetouane, A., Mabrouk, S., Mosbah, M. (2020). Traffic Congestion Detection: Solutions, Open Issues and Challenges. In: Jemili, I., Mosbah, M. (eds) Distributed Computing for Emerging Smart Networks. DiCES-N 2020. Communications in Computer and Information Science, vol 1348. Springer, Cham. https://doi.org/10.1007/978-3-030-65810-6_1

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-65810-6_1

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-65809-0

  • Online ISBN: 978-3-030-65810-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation