Skip to main content

Advertisement

Log in

Priority-Based Adaptive Traffic Signal Control System for Smart Cities

  • Original Research
  • Published:
SN Computer Science Aims and scope Submit manuscript

Abstract

The traffic signal control system has paramount importance in managing the traffic congestion at road intersections. It allows smooth traffic flow through the road intersections. Keeping in view to increase the traffic flow through the intersections, this paper proposes an adaptive traffic signal which can be used as a prototype for smart cities. The proposed traffic signal system will act according to the context of the traffic status in the intersections. The phase selection is adaptive in nature in the proposed system and phase will be selected based on the lane priority. The lane priority is calculated based on traffic parameters: vehicle density and waiting time. Also, the green signal time of the system of each cycle is dynamic. The amount of green time a lane will get depends on the vehicle density of that lane. The proposed system has the ability to handle the emergency vehicle approaching the intersection by shifting to emergency mode from regular mode. The simulation result shows that the average percentage of traffic flow has been increased by 31.05% in the proposed adaptive traffic signal control system as compared with the fixed time traffic signal system. It also performs comparatively well in both the traffic situations: balanced vehicle density and unbalanced vehicle density across the roads in the intersection.

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
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

Data Availability

Not applicable.

References

  1. Mondal MA, Rehena Z. Intelligent traffic congestion classification system using artificial neural network. In: Companion Proceedings of the 2019 world wide web conference. Association for Computing Machinery, New York, NY, USA. 2019. pp. 110–6. https://doi.org/10.1145/3308560.3317053.

  2. Monzon A. Smart cities concept and challenges: Bases for the assessment of smart city projects. In: 2015 international conference on smart cities and green ICT systems (SMARTGREENS). 2015. pp. 1–11.

  3. Mondal MA, Rehena Z. An IoT-based congestion control framework for intelligent traffic management system. In: Chiplunkar NN, Fukao T, editors. Advances in artificial intelligence and data engineering. Singapore: Springer; 2021. p. 1287–97.

    Chapter  Google Scholar 

  4. Rawal T. Intelligent transportation system in India—a review. J Dev Manag Commun. 2015;2:299.

    Google Scholar 

  5. Tarnoff PJ, Ordonez J. Signal timing practices and procedures-state of the practice. In: Transportation Research Board. 2005.

  6. Bandra J. Traffic highway capacity design-traffic signal design. In: Traffic signal design. 2002.

  7. Zhao D, Dai Y, Zhang Z. Computational intelligence in urban traffic signal control: a survey. IEEE Trans Syst Man Cybern Part C (Appl Rev). 2012;42(4):485–94. https://doi.org/10.1109/TSMCC.2011.2161577.

  8. Papageorgiou M, Diakaki C, Dinopoulou V, Kotsialos A, Wang Y. Review of road traffic control strategies. Proc IEEE. 2003;91(12):2043–67. https://doi.org/10.1109/JPROC.2003.819610.

    Article  Google Scholar 

  9. Ribeiro IM, de Lurdes de Oliveira Simões M. The fully actuated traffic control problem solved by global optimization and complementarity. Eng Optim. 2016;48(2):199–212. https://doi.org/10.1080/0305215X.2014.995644.

    Article  MathSciNet  Google Scholar 

  10. Webster FV. Traffic signal setting. Road Research Laboratory. 1958. pp. 1–44.

  11. Miller AJ. Settings for fixed-cycle traffic signals. J Oper Res Soc. 1963;14(4):373–86. https://doi.org/10.1057/jors.1963.61.

    Article  Google Scholar 

  12. Araghi S, Khosravi A, Creighton D. Intelligent cuckoo search optimized traffic signal controllers for multi-intersection network. Expert Syst. Appl. 2015. https://doi.org/10.1016/j.eswa.2015.01.063.

  13. Jin J, Ma X, Kosonen I. An intelligent control system for traffic lights with simulation-based evaluation. Control Eng Pract. 2017;58:24–33. https://doi.org/10.1016/j.conengprac.2016.09.009.

    Article  Google Scholar 

  14. Araghi S, Khosravi A, Creighton D, Nahavandi S. Influence of meta-heuristic optimization on the performance of adaptive interval type2-fuzzy traffic signal controllers. Expert Syst Appl. 2017;71(C):493–503. https://doi.org/10.1016/j.eswa.2016.10.066.

    Article  Google Scholar 

  15. Miletić M, Kapusta B, Ivanjko E. Comparison of two approaches for preemptive traffic light control. In: 2018 international symposium ELMAR. 2018. pp. 57–62. https://doi.org/10.23919/ELMAR.2018.8534608.

  16. Wei H, Zheng G, Yao H, Li Z. Intellilight: a reinforcement learning approach for intelligent traffic light control. In: Proceedings of the 24th ACM SIGKDD international conference on knowledge discovery and data mining. KDD ’18. Association for Computing Machinery, New York, NY, USA. 2018. pp. 2496–505. https://doi.org/10.1145/3219819.3220096.

  17. Garg D, Chli M, Vogiatzis G. Deep reinforcement learning for autonomous traffic light control. In: 2018 3rd IEEE international conference on intelligent transportation engineering (ICITE). 2018. pp. 214–8. https://doi.org/10.1109/ICITE.2018.8492537.

  18. Yu D, Tian X, Xing X, Gao S. Signal timing optimization based on fuzzy compromise programming for isolated signalized intersection. Math Probl Eng. 2016;2016:1–12. https://doi.org/10.1155/2016/1682394.

    Article  MathSciNet  MATH  Google Scholar 

  19. Li Z, Schonfeld P. Hybrid simulated annealing and genetic algorithm for optimizing arterial signal timings under oversaturated traffic conditions. J Adv Transp. 2014. https://doi.org/10.1002/atr.1274.

  20. Gökçe M, Oner E, Ik G. Traffic signal optimization with particle swarm optimization for signalized roundabouts. Simulation. 2015;91:456–66. https://doi.org/10.1177/0037549715581473.

  21. Dabiri S, Abbas M. Arterial traffic signal optimization using particle swarm optimization in an integrated vissim-matlab simulation environment. 2016. pp. 766–71. https://doi.org/10.1109/ITSC.2016.7795641.

  22. Panovski D, Zaharia T. Simulation-based vehicular traffic lights optimization. In: 2016 12th international conference on signal-image technology internet-based systems (SITIS). 2016. pp. 258–65. https://doi.org/10.1109/SITIS.2016.49.

  23. Gao K, Zhang Y, Sadollah A, Su R. Optimizing urban traffic light scheduling problem using harmony search with ensemble of local search. Appl Soft Comput. 2016. https://doi.org/10.1016/j.asoc.2016.07.029.

  24. Eddelbuttel J, Cremer M. A new algorithm for optimal signal control in congested networks. J Adv Transp. 1994;28:275–97.

    Article  Google Scholar 

  25. He Q, Kamineni R, Zhang Z. Traffic signal control with partial grade separation for oversaturated conditions. Transp Res Part C Emerg Technol. 2016;71:267–83. https://doi.org/10.1016/j.trc.2016.08.001.

    Article  Google Scholar 

  26. Mehrabipour M, Hajbabaie A. A cell-based distributed-coordinated approach for network-level signal timing optimization. Comput Aided Civ Infrastruct Eng. 2017;32:599–616. https://doi.org/10.1111/mice.12272.

    Article  Google Scholar 

  27. Köhler E, Strehler M. Traffic signal optimization: combining static and dynamic models. Transp Sci. 2018. https://doi.org/10.1287/trsc.2017.0760.

  28. Yan H, He F, Lin X, Yu J, Li M, Wang Y. Network-level multiband signal coordination scheme based on vehicle trajectory data. Transp Res Part C Emerg Technol. 2019;107:266–86. https://doi.org/10.1016/j.trc.2019.08.014.

    Article  Google Scholar 

  29. Swaminathan S, Venkatesan P. Embedded traffic control system using wireless ad hoc sensors. 2014. p. 225–7.

  30. Lopes J, Bento J, Huang E, Antoniou C, Ben-Akiva M. Traffic and mobility data collection for real-time applications. In: 13th international IEEE conference on intelligent transportation systems. 2010. pp. 216–23. https://doi.org/10.1109/ITSC.2010.5625282.

  31. Sumalee A, Ho HW. Smarter and more connected: future intelligent transportation system. IATSS Res. 2018;42(2):67–71. https://doi.org/10.1016/j.iatssr.2018.05.005.

    Article  Google Scholar 

Download references

Funding

This study has received no funding.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Zeenat Rehena.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher's Note

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

This article is part of the topical collection “Social Data Science: Research Challenges and Future Directions” guest edited by Sarbani Roy, Chandreyee Chowdhury and Samiran Chattopadhyay.

Rights and permissions

Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Mondal, M.A., Rehena, Z. Priority-Based Adaptive Traffic Signal Control System for Smart Cities. SN COMPUT. SCI. 3, 417 (2022). https://doi.org/10.1007/s42979-022-01316-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s42979-022-01316-5

Keywords

Navigation