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.
Similar content being viewed by others
Data Availability
Not applicable.
References
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.
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.
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.
Rawal T. Intelligent transportation system in India—a review. J Dev Manag Commun. 2015;2:299.
Tarnoff PJ, Ordonez J. Signal timing practices and procedures-state of the practice. In: Transportation Research Board. 2005.
Bandra J. Traffic highway capacity design-traffic signal design. In: Traffic signal design. 2002.
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.
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.
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.
Webster FV. Traffic signal setting. Road Research Laboratory. 1958. pp. 1–44.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Eddelbuttel J, Cremer M. A new algorithm for optimal signal control in congested networks. J Adv Transp. 1994;28:275–97.
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.
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.
Köhler E, Strehler M. Traffic signal optimization: combining static and dynamic models. Transp Sci. 2018. https://doi.org/10.1287/trsc.2017.0760.
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.
Swaminathan S, Venkatesan P. Embedded traffic control system using wireless ad hoc sensors. 2014. p. 225–7.
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.
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.
Funding
This study has received no funding.
Author information
Authors and Affiliations
Corresponding author
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.
About this article
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
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s42979-022-01316-5