Skip to main content
Log in

A Novel Self-Organizing Approach to Automatic Traffic Light Management System for Road Traffic Network

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

Traffic network is basically a “network of networks” consisting of mainly two types of networks: road network and a travel network. Due to drastic increase in population of vehicles, traffic congestion in metro cities of India is a severe problem. To resolve this use, we have proposed an approach to dynamic and automatic road traffic light management system. The approach is based on arithmetic mean theorem. The input to the control interface of traffic light of proposed approach are six different parameters : velocity of traffic units (v), queue length (l), inter-arrival time between vehicles (t), centrality measures value(c) and predicted value (prdt v) of traffic congestion by historical database and output is level of congestion. On the basis of congestion level, we build inference rules. The proposed approach automatically updates duration of green and red light as per the level of congestion at a particular junction. Proposed approach follows forward reasoning of If–Then rules. A study of this approach is done in this paper on various traffic situations of Delhi, India depending on queue length of vehicles.

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

Access this article

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

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

Notes

  1. https://timesofindia.indiatimes.com/auto/miscellaneous/vehicle-numbers-cross-one-crore-mark-in-delhi/articleshow/58983958.cms.

  2. http://www.firstpost.com/data-business/these-five-cities-have-the-most-vehicles-in-india-mumbai-isnt-one-of-them-1963743.html.

References

  1. Sahay, U., Sharma, A., & Singh, B. (2019). Scope of ITS in exaggerating existing traffic systems in India. Recent Trends in Sensor Research and Technology,4(2), 14–18.

    Google Scholar 

  2. Pandey, M. (2012, July 23). Growing congestion on Delhi roads likely to reduce speeds to 5 kmph. An Article in Mail today.

  3. Planning Department of Delhi. (2000, March). Economic survey of Delhi. Chapter 12.

  4. Wen, W. (2008). A dynamic and automatic traffic light control expert system for solving the road congestion problem. Expert Systems with Applications,34, 2370–2381.

    Article  Google Scholar 

  5. US Department of transportation. (2007). Congestion Mitigation. Retrieved December 2, 2017 from http://www.fhwa.dot.gov/congestionmitigation/congestionmitigation.htm.

  6. Liao, S. H. (2002). Problem solving and knowledge inertia. Expert Systems with Applications,22, 21–31.

    Article  Google Scholar 

  7. Liu, B. (1997). Routing finding by using knowledge about the road network. IEEE Transactions on System, Man, and Cybernetics-Part A: Systems and Humans,27(4), 425–430.

    Google Scholar 

  8. Maniccam, S. (2006). Adaptive decentralized congestion avoidance in two dimensional traffic. Physica A,343, 512–526.

    Article  Google Scholar 

  9. Sheu, J. B. (2006). A composite traffic flow modeling approach for incident-responsive network traffic assignment. Physica A,367, 461–478.

    Article  Google Scholar 

  10. Xia, L., & Shao, Y. (2005). Modelling of traffic flow and air pollution emission with application to Hong Kong Island. Environmental Modelling and Software,20, 1175–1188.

    Article  MathSciNet  Google Scholar 

  11. Yang, X., & Recker, W. (2005). Simulation studies of information propagation in a self-organizing distributed traffic information system. Transportation Research Part C,13, 370–390.

    Article  Google Scholar 

  12. Wangermann, J. P., & Stengel, R. F. (1998). Principled negotiation between intelligent agents: a model for air traffic management. Artificial Intelligent in Engineering,12, 177–187.

    Article  Google Scholar 

  13. Zadeh, L. A. (1965). Fuzzy sets. Information and Control,8(3), 338–353.

    Article  MathSciNet  Google Scholar 

  14. Pappis, C., & Mamdani, E. (1997). A fuzzy logic controller for a traffic junction. IEEE Transactions Systems, Man, and Cybernetics,7(10), 707–717.

    Article  Google Scholar 

  15. Nakatsuyama, M., Nagahashi, H., & Nishizuka, N. (1984). Fuzzy logic phase controller for traffic junctions in the one-way arterial road. In: Proceedings of the, IFAC 9th triennial world congress, Budapest, Hungary (pp. 2865–2870).

  16. Chen, L. L., May, A. D., & Auslander, D. M. (1990). Freeway ramp control using fuzzy set theory for inexact reasoning. Transportation Research Part A,24(1), 15–25.

    Article  Google Scholar 

  17. Chiu, S. (1992). Adaptive traffic signal control using fuzzy logic. In: Proceedings of the intelligent vehicles ‘92 symposium. Rockwell International Science Center.

  18. Kelsey, R. L., & Bisset, K. R. (1993). Simulation of traffic flow and control using fuzzy and conventional methods. In M. Jamshidi (Ed.), Fuzzy Logic and Control: Software and Hardware Applications (pp. 262–278). Englewood Cliffs, NJ: Prentice-Hall.

    Google Scholar 

  19. Favilla, J., Machion, A., Gomide, F.(1993). Fuzzy traffic control: Adaptive strategies. In: Proceedings of the second IEEE international conference on fuzzy systems, San Francisco, California (Vol. 1, pp. 506–511).

  20. Chang, Y. H., & Shyu, T. H. (1993). Traffic signal installation by the expert system using fuzzy set theory for inexact reasoning. Transportation Planning and Technology,17(2), 191–202.

    Article  Google Scholar 

  21. Lee, J. H., Lee, K. M., & Lee-Kwang, H. (1995). Fuzzy controller for intersection group. In: Proceedings of the international IEEE/IAS conference on industrial automation and control: Emerging technologies, Taipei, Taiwan (pp. 376–382).

  22. Beauchamp-Baez, G., Rodriguez-Morales, E., & Muniz-Marrero, E. (1997). A fuzzy logic based phase controller for traffic control. In: Proceedings of the Sixth IEEE international conference on fuzzy systems (pp. 1533–1539).

  23. Niittymaki, J., & Kikuchi, S. (1998). Application of fuzzy logic to the control of a pedestrian crossing signal. Journal of Transportation Research,1651, 30–38.

    Google Scholar 

  24. Trabia, M. B., Kaseko, M. S., & Ande, M. (1999). A two-stage fuzzy logic controller for traffic signals. Transportation Research Part C,7(6), 353–367.

    Article  Google Scholar 

  25. Niittymaki, J., & Kononen, V. (2000). Traffic signal controller based on fuzzy logic. In: Proceedings of the 2000 IEEE international conference on systems, man, and cybernetics, Nashville, Tennessee (Vol. 5, pp. 3578–3581).

  26. Wei, W., Zhang, Y., Mbede, J. B., et al. (2001). Traffic signal control using fuzzy logic and MOGA. In: Proceedings of the 2001 IEEE international conference on Systems, man, and cybernetics, Tucson, Arizona (pp. 1335–1340).

  27. Niittymaki, J. (2001). General fuzzy rule base for isolated traffic signal control—rule formulation. Transportation Planning and Technology,24(3), 227–247.

    Article  Google Scholar 

  28. Bingham, E. (2001). Reinforcement learning in neurofuzzy traffic signal control. European Journal of Operational Research,131(2), 232–241.

    Article  Google Scholar 

  29. Chou, C. H., & Teng, J. C. (2002). A fuzzy logic controller for traffic junction signals. Information Sciences,143(1), 73–97.

    Article  Google Scholar 

  30. Chen, L., & Englund, C. (2018). Every second counts: integrating edge computing and service oriented architecture for automatic emergency management. Journal of Advanced Transportation,2018, 13.

    Google Scholar 

  31. Karatsoli, M., Margreiter, M., & Spangler, M. (2017). Bluetooth-based travel times for automatic incident detection–A systematic description of the characteristics for traffic management purposes. Transportation Research Procedia,24, 204–211.

    Article  Google Scholar 

  32. Kamesh, D. B. K., Sumadhuri, D. S. K., Sahithi, M. S. V., & Sastry, J. K. R. (2017). An efficient architectural model for building cognitive expert system related to traffic management in smart cities. Journal of Engineering and Applied Sciences,12(9), 2437–2445.

    Google Scholar 

  33. Plotnikov, A., Kravchenko, P., & Kotikov, J. (2017). Classification investigations of traffic management schemes having conflict loading at the signal-controlled road junctions. Transportation Research Procedia,20, 511–515.

    Article  Google Scholar 

  34. Soni, N. B., & Saraswat, J. (2017, December). A review of IoT devices for traffic management system. In 2017 international conference on intelligent sustainable systems (ICISS) (pp. 1052–1055). IEEE.

  35. Murat, Y. S., & Gedizlioglu, E. (2002). A new approach for fuzzy traffic signal control. In: Proceedings of the 13th mini-EURO conference on artificial intelligence in transportation systems and science, Bari, Italy.

  36. Zhang, L., Li, H., & Prevedouros, P. D.(2005). Signal control for oversaturated intersections using fuzzy logic. In: Proceedings of the of 84th transportation research board Ann. Mtg., Washington, D.C.

  37. Murat, Y. S., & Gedizlioglu, E. (2005). A fuzzy logic multi-phased signal control model for isolated junctions. Transportation Research Part C: Emerging Technologies,13(1), 19–36.

    Article  Google Scholar 

  38. Cho, Y. I., & Kang, S. (2007). Weighted CRI method for fuzzy controller. In: Proceedings of the third international conference on intelligent computing, ICIC 2007, Qingdao, China (pp. 670–679).

  39. Hu, Y., Thomas, P., & Stonier, R. J.(2007). Traffic signal control using fuzzy logic and evolutionary algorithms. In: Proceedings of the IEEE congress on evolutionary computation, CEC 2007, Singapore (pp. 1785–1792).

  40. Zeng, R., Li, G., & Lin, L. (2007). Adaptive traffic signals control by using fuzzy logic. In: Proceedings of the second international conference on innovative computing, information and control, Kumamoto, Japan.

  41. Zhang, W. B., Wu, B. Z., & Liu, W. J. (2007). Anti-congestion fuzzy algorithm for traffic control of a class of traffic networks. In: Proceedings of the IEEE international conference on granular computing, GRC 2007, San Jose, California (pp. 124–128).

  42. Jain, A., Tayal, D. K., Khari, M., & Vij, S. (2016). A novel method for test path prioritization using centrality measures. International Journal of Open Source Software and Processes (IJOSSP),7(4), 19–38.

    Article  Google Scholar 

  43. Jain, A., & Lobiyal, D. K. (2016). Fuzzy Hindi WordNet and word sense disambiguation using fuzzy graph connectivity measures. ACM Transactions on Asian and Low-Resource Language Information Processing,15(2), 8.

    Google Scholar 

  44. Quek, C., Pasquier, M., & LimA, B. (2009). Novel self-organizing fuzzy rule-based system for modelling traffic flow behavior. Expert Systems with Applications,36, 12167–12178.

    Article  Google Scholar 

  45. Paul, J., Malhotra, B., Dale, S., & Qiang, M. (2013). RFID based vehicular networks for smart cities. In 2013 IEEE 29th international conference on data engineering workshops (ICDEW) (pp. 120–127). IEEE.

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Amita Jain.

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

Jain, A., Yadav, S., Vij, S. et al. A Novel Self-Organizing Approach to Automatic Traffic Light Management System for Road Traffic Network. Wireless Pers Commun 110, 1303–1321 (2020). https://doi.org/10.1007/s11277-019-06787-z

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-019-06787-z

Keywords

Navigation