Advertisement

Intelligent traffic controller

  • Sachin KumarEmail author
  • Anupam Baliyan
  • Anurag Tiwari
  • Aniket Kumar Tripathi
  • Balram Jaiswal
Original Research
  • 7 Downloads

Abstract

This paper explores the application of dynamic traffic control timings using predefined input parameters. The method is a dynamic traffic algorithm that takes the rate of inflow, rate of outflow and queue length as input parameters to estimate the green-time that must be allocated to each road. The basic idea is to efficiently distribute the green-time based on traffic congestion in contrast to traditional methods of fixed time for traffic lights irrespective of their traffic status. The paper also compares the differences between these two methods based on some efficiency parameters using a simulator.

Keywords

Congestion Control IOT Queue length Average waiting time 

References

  1. 1.
    Lanke N, Koul S (2013) Smart traffic management system. Int J Comput Appl 75:19–22.  https://doi.org/10.5120/13123-0473 CrossRefGoogle Scholar
  2. 2.
    Davis N, Joseph H, Raina G, Jagannathan K (2017) Congestion costs incurred on Indian Roads: A case study for New DelhiGoogle Scholar
  3. 3.
    Jain V, Sharma A, Subramanian L (2012) Road traffic congestion in the developing world. In: Proceedings of the 2nd ACM Symposium on Computing for Development, DEV 2012, Atlanta, GA, United States, 3/11/12.  https://doi.org/10.1145/21606601.2160616
  4. 4.
    Pendor RB, Tasgaonkar PP (2016) An IoT framework for intelligent vehicle monitoring system. In: International conference on communication and signalGoogle Scholar
  5. 5.
    Pedraza C, Silva D, Arevalo A, Vega F (2016) RFID framework for intelligent traffic monitoring. In: 2016 8th Euro American Conference on Telematics and Information Systems (EATIS)Google Scholar
  6. 6.
    Al-Khateeb KAS, Johari JAY, Al-Khateeb WF (2008) Intelligent dynamic traffic light sequence. Department of Electrical and Computer Engineering, Faculty of Engineering, International Islamic University Malaysia, Kuala Lumpur. Malaysia. J Comput Sci 4(7):517–524 (ISSN 1549-3636 © 2008 Science Publications) CrossRefGoogle Scholar
  7. 7.
    Jain S, Jain S, Jain G (2017) Traffic congestion modelling based on origin and destination. Proc Eng 187:442–450.  https://doi.org/10.1016/j.proeng.2017.04.398CrossRefzbMATHGoogle Scholar
  8. 8.
    Younes MB, Boukerche A (2018) An efficient dynamic traffic light scheduling algorithm considering emergency vehicles for intelligent transportation systems by Maram Bani Younes and Azzedine Boukerche 20017. Wireless Netw 24:2451.  https://doi.org/10.1007/s11276-017-1482-5 CrossRefGoogle Scholar
  9. 9.
    Makino H, Tamada K, Sakai K, Kamijo S (2018) Solutions for urban traffic issues by ITS technologies in Japan. © 2018 International Association of Traffic and Safety Sciences. Production and hosting by Elsevier LtdGoogle Scholar
  10. 10.
    Ito T, Kaneyasu R (2017) Predicting traffic congestion using driver behavior by Toshio Ito—Shibaura Institute of Technology, Ryohei Kaneyasu—Fukasaku, Minuma-ku, Saitama City, Japan 2017.  https://doi.org/10.1016/j.procs.2017.08.090 CrossRefGoogle Scholar
  11. 11.
    Soylemezgiller F, Kuscu M, Kilinc D (2013) A traffic congestion avoidance algorithm with dynamic road pricing for smart cities. In: 2013 IEEE 24th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC)Google Scholar
  12. 12.
    Stefanello F et al (2017) On the minimization of traffic congestion in road networks with tolls. Ann Oper Res 249(t-2):119–139MathSciNetCrossRefGoogle Scholar
  13. 13.
    Osaba E, Lopez-Garcia P, Onieva E, Masegosa AD, Serrano L, Landaluce H (2017) Application of artificial intelligence techniques to traffic prediction and route planning: the vision of TIMON project. In: 12th ITS European Congress, Strasbourg, France, 19–22 June 2017Google Scholar
  14. 14.
    Kumar S, Gupta M, Srivastav V, Agarwal K (2007) On the efficiency and fairness of congestion control algorithms. In: Sobh T, Elleithy K, Mahmood A, Karim M (eds) Innovative algorithms and techniques in automation, industrial electronics and telecommunications. SpringerGoogle Scholar
  15. 15.
    Singh D, Kushwaha N, Kumar S (2015). Fast-AIMD: a fairness based congestion control approach for TCP networks. International Conference on Computing, Communication & Automation, pp 458–463Google Scholar
  16. 16.
    Chaudhary P, Kumar S (2017) A review of comparative analysis of TCP variants for congestion control in network. Int J Comput Appl 160:28–34.  https://doi.org/10.5120/ijca2017913087 CrossRefGoogle Scholar
  17. 17.
    Kumar S, Singh D (2014) Fairness based comparative study of AIMD congestion control techniques. Int J Innovat Adv Comput Sci IJIACS ISSN 3(4):2347–8616Google Scholar
  18. 18.
    Atta A, Abbas S, Khan MA, Ahmed G, Farooq U (2018) An adaptive approach: smart traffic congestion control system. J King Saud Univ Comput Inf Sci.  https://doi.org/10.1016/j.jksuci.2018.10.011
  19. 19.
    Yuan J, Zheng Y, Xie X (2012) Discovering regions of different functions in a city using human mobility and POIS. In: Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM, pp 186–194Google Scholar
  20. 20.
    Geisberger R, Sanders P, Schultes D, Delling D (2008) Contraction hierarchies: faster and simpler hierarchical routing in road networks. pp 319–333.  https://doi.org/10.1007/978-3-540-68552-4_24
  21. 21.
    Al Shalabi L, Shaaban Z, Kasasbeh B (2006) Data mining: a preprocessing engine. J Comput Sci 2(9):735–739CrossRefGoogle Scholar
  22. 22.
    Van Hulse J, Khoshgoftaar TM, Napolitano A (2007) Experimental perspectives on learning from imbalanced data. In: Proceedings of the 24th International Conference on Machine Learning, ACM, pp 935–942Google Scholar
  23. 23.
    Breiman L, Friedman J, Stone CJ, Olshen RA (1984) Classification and regression trees. CRC Press, Baca RatonzbMATHGoogle Scholar
  24. 24.
    Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M, Prettenhofer P, Weiss R, Dubourg V et al (2011) Scikit-learn: machine learning in python. J Mach Learn Res 12:2825–2830MathSciNetzbMATHGoogle Scholar

Copyright information

© Bharati Vidyapeeth's Institute of Computer Applications and Management 2019

Authors and Affiliations

  1. 1.Department of Computer Science & EngineeringAjay Kumar Garg Engineering CollegeGhaziabadIndia
  2. 2.Bharati Vidyapeeth’s Institute of Computer Applications and ManagementNew DelhiIndia

Personalised recommendations