International Journal of Nonlinear Analysis and Applications

Traffic control system techniques: A review

Document Type : Review articles

Authors

1 Department of Computer Science, College of Science, University of Baghdad, Iraq

2 Department of Computer Science, University of Baghdad, Baghdad, Iraq

Abstract

Controlling and managing traffic signals at road intersections is a demanding task in the transportation system to ensure vehicular traffic safety and a consistent flow of traffic. Because of the significant increase in the number of vehicles on a daily basis, reducing road congestion has become a major concern in recent years. The urban transportation system requires effective solution techniques to cope with current traffic conditions and meet the ever-increasing demand for traffic. Changes to urban infrastructure will take years, and in some cases may not be feasible. As a result, optimizing traffic signal time (TST) is one of the quickest and most cost-effective strategies to reduce congestion at intersections and improve traffic flow in the metropolitan network. To improve TST, researchers have been working on a number of ways as well as the use of technology. This paper aims to analyze recent literature published between 2014 and 2022 for various traffic signal management systems that have been developed to improve real-time traffic flow at junctions by optimizing TST and Traffic Signal Control (TSC) systems and to provide insights, research gaps, and possible directions for future work for researchers interested in the field.

Keywords

[1] A.S. Amogh, S. Pujari, S.N. Gowda and V.M. Nyamati, Traffic timer synchronization based on congestion, Proc. Int. Conf. Comput. Syst. Inf. Technol. Sustain. Sol. (CSITSS), 2016, pp. 255–260.
[2] P. Anand, Traffic signal timing control using deep learning, ACCENTS Trans. Image Process. Comput. Vision 5 (2019), no. 17, 27–32.
[3] S. Araghi, A. Khosravi and D. Creighton, A review on computational intelligence methods for controlling traffic signal timing, Expert Syst. Appl. 42 (2015), no. 3, 1538–1550.
[4] S. Araghi, A. Khosravi and D. Creighton, Intelligent cuckoo search optimized traffic signal controllers for multi intersection network, Expert Syst. Appl. 42 (2015), no. 9, 4422–4431.
[5] S. Araghi, A. Khosravi, D. Creighton and S. Nahavandi, Influence of meta-heuristic optimization on the performance of adaptive interval type2-fuzzy traffic signal controllers, Expert Syst. Appl. 71 (2017), 493–503.
[6] B. Bing and A.T. Carter, SCOOT: the world’s foremost adaptive traffic control system, Traffic Technol. Int. 95 (1995).
[7] W. Brilon and T. Wietholt, Experiences with adaptive signal control in Germany, Transp. Res. Rec. J. Transp. Res. Board 2366 (2013), no. 1, 9–16.
[8] S. Chaudhary, S. Indu and S. Chaudhury, Video-based road traffic monitoring and prediction using dynamic Bayesian networks, IET Intell. Transp. Syst. 12 (2018), no. 3, 169–176.
[9] L. Chen and C. Englund, Cooperative intersection management: a survey, IEEE Trans. Intell. Transp. Syst. 17 (2015), no. 2, 570–586.
[10] F. Clara Fang and C.V. Pham, An intelligent traffic signal control system based on fuzzy theory, CICTP 2019 (2019), 3020–3031.
[11] R.W. Denney Jr, E. Curtis and P. Olson, The national traffic signal report card, ITE J. 82 (2012), no. 6, 22–26.
[12] D. Garg, M. Chli and G. Vogiatzis, Deep reinforcement learning for autonomous traffic light control, 3rd IEEE Int. Conf. Intell. Transport. Engin. (ICITE), 2018, pp. 214–218.
[13] N.H. Gartner, Demand-responsive traffic signal control research, Transp. Res. Part A Gen. 19 (1985), no. 5–6, 369–373.
[14] M. Greguri´c, M. Vuji´c, C. Alexopoulos and M. Mileti´c, Application of deep reinforcement learning in traffic signal control: an overview and impact of open traffic data, Appl. Sci. 10 (2020), no. 11.
[15] R.A. Hadi, G. Sulong and L.E. George, Vehicle detection and tracking techniques: a concise review, Signal Image Process. Int. J. 5 (2014), 1–12.
[16] D. Heidemann, Queue length and delay distributions at traffic signals, Transp. Res. Part B Methodol. 28 (1994), no. 5, 377–389.
[17] J. Henry, J. Farges and J. Tuffal, The PRODYN real time traffic algorithm, Proc. Control Transport. Syst., 1984, pp. 305–310.
[18] S. Indu, V. Nair, S. Jain and S. Chaudhury, Video based adaptive road traffic signaling, Seventh Int. Conf. Distrib. Smart Cameras (ICDSC), IEEE, 2013, pp. 1–6.
[19] J. Jin, X. Ma and I. Kosonen, An intelligent control system for traffic lights with simulation-based evaluation, Control Engin. Practice 58 (2017), 24–33.
[20] M. Koukol, L. Zajıckova, L. Marek and P. Tucek, Fuzzy logic in traffic engineering: a review on signal control, Math. Prob. Engin. 2015 (2015), 1–14.
[21] A. Kouvelas, K. Aboudolas, M. Papageorgiou and E.B. Kosmatopoulos, A hybrid strategy for real-time traffic signal control of urban road networks, IEEE Trans. Intell. Transp. Syst. 12 (2011), no. 3, 884–894.
[22] ˙I. K¨u¸c¨uko˘glu, R. Dewil and D. Cattrysse, Hybrid simulated annealing and tabu search method for the electric travelling salesman problem with time windows and mixed charging rates, Expert Syst. Appl. 134 (2019), 279–303.
[23] Z. Li, C. Li, Y. Zhang and X. Hu, Intelligent traffic light control system based on real time traffic flows, 14th IEEE Annual Consumer Commun. Network.Conf. (CCNC), 2017, pp. 624–625.
[24] F. Lian, B. Chen, K. Zhang, L. Miao, J. Wu and S. Luan, Adaptive traffic signal control algorithms based on probe vehicle data, J. Intell. Transport. Syst. 25 (2021), no. 1, 41–57.
[25] X. Liping and L. Dangying, A novel embedded vehicle terminal for intelligent transportation, Proc. Fifth Int. Conf. Intell. Syst. Design Engin. Appl., 2014, pp. 52–54.
[26] Y. Liu, Big data technology and its analysis of application in urban intelligent transportation system, Proc. Int. Conf. Intell. Transport. Big Data Smart City (ICITBS), 2018, pp. 17–19.
[27] Z. Ma, T. Cui, W. Deng, F. Jiang and L. Zhang, Adaptive optimization of traffic signal timing via deep reinforcement learning, J. Adv. Transp. 2021 (2021), 1–14.
[28] M. Miletic, B. Kapusta and E. Ivanjko, Comparison of two approaches for preemptive traffic light control, Int. Symp. ELMAR, 2018, pp. 57–62.
[29] A.J. Miller, Settings for fixed-cycle traffic signals, OR 14 (1963), no. 4, 373.
[30] P. Mirchandani and L. Head, A real-time traffic signal control system: architecture, algorithms, and analysis, Transp. Res. Part C Emerg. Technol. 9 (2001), no. 6, 415–432.
[31] O.A. Nielsen, R. Frederiksen and N. Simonsen, Using expert system rules to establish data for intersections and turns in road networks, Int. Trans. Oper. Res. 5 (1998), no. 6, 569–581.
[32] L.F.P. Oliveira, L.T. Manera and P.D.G. Luz, Smart traffic light controller system, Sixth Int. Conf. Internet of Things: Syst. Manag. Secur. (IOTSMS), 2019, pp. 155–160.
[33] E.S. Prassas and R.P. Roess, The highway capacity manual: A conceptual and research history volume 2, Springer International Publishing, 2020.
[34] I.M. Ribeiro and M.D.L.D.O. Sim˜oes, The fully actuated traffic control problem solved by global optimization and complementarity, Engin. Optim. 48 (2016), no. 2, 199–212.
[35] R.G. Sigua, Fundamentals of traffic engineering, UP Press, 2008.
[36] A. Sims and K. Dobinson, The Sydney coordinated adaptive traffic (SCAT) system philosophy and benefits, IEEE Trans. Veh. Technol. 29 (1980), no. 2, 130–137.
[37] R. Tanwar, R. Majumdar, G.S. Sidhu and A. Srivastava, Removing traffic congestion at traffic lights using GPS technology, in Proc. 6th Int. Conf.—Cloud Syst. Big Data Engin., 2016, pp. 575–579.
[38] W. Tiedong and H. Jingjing, Applying floating car data in traffic monitoring, Proc. 2014 IEEE Int. Conf. Control Sci. Syst. Engin., 2014, pp. 96–99.
[39] A. Vogel, I. Oremovic, R. Imi´c and E. Ivanjko, Improving traffic light control by means of fuzzy logic, Int. Symp. ELMAR, 2018, pp. 51–56.
[40] F.V. Webster, Traffic signal setting, Road Res. Lab. Tech. Paper 39 (1958), 1–44.
[41] H. Wei, G. Zheng, H. Yao and Z. Li, Intellilight: A reinforcement learning approach for intelligent traffic light control, Proc. 24th ACM SIGKDD Int. Conf. Knowledge Discovery Data Min., 2018, pp. 2496–2505.
[42] M. Xu, K. An, L.H. Vu, Z. Ye, J. Feng and E. Chen, Optimizing multi-agent based urban traffic signal control system, J. Intell. Transport. Syst. 23 (2019), no. 4, 357–369.
[43] R. Zaghal, K. Thabatah and S. Salah, Towards a smart intersection using traffic load balancing algorithm, Comput. Conf., IEEE, 2017, pp. 485–491.
[44] D. Zhao, Y. Dai and Z. Zhang, Computational intelligence in urban traffic signal control: a survey, IEEE Trans. Syst. Man Cybernet. Part C (Applications and Reviews) 42 (2012), no. 4, 485–494.
International Journal of Nonlinear Analysis and Applications
Volume 13, Issue 2
July 2022
Pages 829-835
  • Receive Date: 13 March 2022
  • Revise Date: 26 April 2022
  • Accept Date: 24 May 2022