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Traffic Signal Control Methods: Current Status, Challenges, and Emerging Trends

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Proceedings of Data Analytics and Management

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 90))

Abstract

Traffic management at intersections is a challenging problem within the transport system. Various traffic signal control strategies have been used to manage the traffic at intersections in real time but are not able to deal with the congestion at road intersections. They are intended to acknowledge the continuous flow of vehicles on heavy traffic routes. However, the process of networking traffic lights of adjacent junctions is a complex matter because of many boundaries. Variable flows entering the junctions are not controlled by traditional systems. Moreover, there is no common intervention in the current traffic signal framework between the adjacent traffic light systems, the difference in the movement of cars over time, injuries, the passage of emergency vehicles, and pedestrian crossings. This paper provides a systematic literature review of the existing methods and algorithms for traffic signal control at intersections. Furthermore, this paper discusses the open challenges with the purpose of synchronizing Traffic Light for Intelligent Vehicles.

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References

  1. Nielsen OA, Frederiksen RD, Simonsen N (1998) Using expert system rules to establish data for intersections and turns in road networks. Int Trans Oper Res 569–581

    Google Scholar 

  2. Homburger WS, Hall JW, William RR, Edward CS, Michelle D, Loretta H, John JL, Matthew R, Vernon HW (2007) Fundamentals of traffic engineering; institute of transportation studies. University of California, Berkeley, CA, USA

    Google Scholar 

  3. Heidemann D (1994) Queue length and delay distributions at traffic signals. Transp Res Part B Methodol 377–389

    Google Scholar 

  4. Denney RW, Curtis E, Olson P (2012) The national traffic signal report card. ITE J

    Google Scholar 

  5. Passenger Cars in Use Worldwide 2005–2015|Statistic. Accessed on 5 Aug, 2020. [Online]. Available: https://www.statista.com/statistics/274365/passenger-cars-in-use-worldwide/

  6. Chen L, Englund C (2016) Cooperative intersection management: a survey. IEEE Trans Intell Transp Syst 17(2):570–586

    Article  Google Scholar 

  7. Zaghal R, Thabatah K, Salah S (2017) Towards a smart intersection using traffic load balancing algorithm. In: Computing conference, London, UK

    Google Scholar 

  8. Shaikh PW, El-Abd M, Khanafer M, Gao K, A review on swarm intelligence and evolutionary algorithms for solving the traffic signal control problem. IEEE Trans Intell Transp Syst. https://doi.org/10.1109/TITS.2020.3014296.

  9. Bing B, Carter A (1995) SCOOT: the world’s foremost adaptive traffic control system. In: Traffic technology international’95; UK and International Press, Surrey, UK

    Google Scholar 

  10. Sims AG, Dobinson KW (1980) The Sydney coordinated adaptive traffic (SCAT) system philosophy and benefits. IEEE Trans Veh Technol 29:130–137

    Article  Google Scholar 

  11. Gartner NH (1983) OPAC: a demand responsive strategy for traffic signal control. Transp Res Record 906:75–81

    Google Scholar 

  12. Mirchandani P, Head L (2001) A real-time traffic signal control system: architecture, algorithms, and analysis. Transp Res Part C Emerg Technol 9:415–432

    Article  Google Scholar 

  13. Henry JJ, Farges JL, Tuffal J (1984) The Prodyn real time traffic algorithm. Control Transp Syst 16:305–310

    Article  Google Scholar 

  14. Brilon W, Wietholt T (2013) Experiences with adaptive signal control in Germany. Transp Res Rec J Transp Res Board 2356:9–16

    Article  Google Scholar 

  15. Intelligent traffic system cost. https://www.itskrs.its.dot.gov/costs/about, online. Accessed on 23 Nov 2017

  16. Kouvelas A, Aboudolas K, Papageorgiou M (2011) A hybrid strategy for real-time traffic signal control of urban road networks. IEEE Trans Intell Transp Syst 12(3)

    Google Scholar 

  17. Zheng W, Lee D-H, Shi Q (2006) Short-term freeway traffic flow prediction: Bayesian combined neural network approach. J Transp Eng 132(2):114–121

    Article  Google Scholar 

  18. Sharma KK, Indu S (2019) GPS based adaptive traffic light timings and lane scheduling. In: IEEE intelligent transportation systems conference (ITSC) Auckland, NZ, Oct 2019

    Google Scholar 

  19. Indu S, Nair V, Jain S, Chaudhury S (2013) Video based adaptive road traffic signaling. In: Seventh international conference on distributed smart cameras (ICDSC)

    Google Scholar 

  20. Lin Y (2014) The design and simulation of intelligent traffic signal control system based on fuzzy logic. Found Intell Syst 965â˘A ¸S973

    Google Scholar 

  21. Tanwar R, Majumdar R, Sidhu GS, Srivastava A (2016) Removing traffic congestion at traffic lights using GPS technology. In: 6th international conference—cloud system and big data engineering (Confluence)

    Google Scholar 

  22. Liu Y (2018) Big data technology and its analysis of application in urban intelligent transportation system. In: International conference on intelligent transportation, big data and smart city (ICITBS)

    Google Scholar 

  23. Coll P, Factorovich P, Loiseau I, Gómez R (2013) A linear programming approach for adaptive synchronization of traffic signals. Int Trans Oper Res 20(5):667–679

    Article  MathSciNet  Google Scholar 

  24. Li** X, Dangying L (2014) A novel embedded vehicle terminal for intelligent transportation. In: Fifth international conference on intelligent systems design and engineering applications

    Google Scholar 

  25. Li Q, Liu B (2010) Research on traffic light control system based on wireless sensor network. Sci Technol Assoc Forum 6:71–73

    Google Scholar 

  26. Tiedong W, **g**g H (2014) Applying floating car data in traffic monitoring. In: 2014 IEEE international conference on control science and systems engineering

    Google Scholar 

  27. Dresner K, Stone P (2008) A multiagent approach to autonomous intersection management. J Artif Intell Res 31:591–653

    Article  Google Scholar 

  28. Chaudhary S, Indu S, Chaudhury S (2018) Video-based road traffic monitoring and prediction using dynamic Bayesian networks. IET Intel Transport Syst 12(3):169–176

    Article  Google Scholar 

  29. Hadi RA, Sulong G, George LE (2014) Vehicle detection and tracking techniques: a concise review. Signal Image Process Int J (SIPIJ) 5(1)

    Google Scholar 

  30. van Lint JWC, Hoogendoorn SP, van Zuylen HJ (2005) Accurate freeway travel time prediction with state-space neural networks under missing data. Transp Res C Emerg Technol, 347–369

    Google Scholar 

  31. Zhang B, Shang L, Chen D (2010) Traffic intersection signal-planning multi-object optimization based on genetic algorithm. In: Proceedings of 2nd international workshop intelligent system and applications, May 2010, pp 1–4

    Google Scholar 

  32. Dezani H, Marranghello N, Damiani F (2014) Genetic algorithm-based traffic lights timing optimization and routes definition using Petri net model of urban traffic flow. In: Proceedings of 19th World Congress of the International Federation of Automatic Control, pp 11326–11331

    Google Scholar 

  33. Dezani H, Bassi RDS, Marranghello N, Gomes L, Damiani F, Nunes da Silva I (2014) Optimizing urban traffic flow using genetic algorithm with Petri net analysis as fitness function. Neurocomputing 124:162–167

    Article  Google Scholar 

  34. Sabar NR, Kieu LM, Chung E, Tsubota T, Maciel de Almeida PE (2017) A memetic algorithm for real world multi-intersection traffic signal optimisation problems. Eng Appl Artif Intell 63:45–53

    Article  Google Scholar 

  35. Kou W, Chen X, Yu L, Gong H (2018) Multiobjective optimization model of intersection signal timing considering emissions based on field data: a case study of bei**g. J Air Waste Manage Assoc 68(8):836–848

    Article  Google Scholar 

  36. Li X, Sun J-Q (2018) Signal multiobjective optimization for urban traffic network. IEEE Trans Intell Transp Syst 19(11):3529–3537

    Article  Google Scholar 

  37. Chin YK, Yong KC, Bolong N, Yang SS, Teo KTK (2011) Multiple intersections traffic signal timing optimization with genetic algorithm. In: Proceedings of IEEE international conference on control system, computing and engineering, Nov 2011, pp 454–459

    Google Scholar 

  38. Li X, Sun J-Q (2019) Multi-objective optimal predictive control of signals in urban traffic network. J Intell Transp Syst 23(4):370–388

    Article  Google Scholar 

  39. Ma C, Liu P (2019) Intersection signal timing optimization considering the travel safety of the elderly. Adv Mech Eng 11(12):1–8

    Google Scholar 

  40. Dong C, Huang S, Liu X (2010) Urban area traffic signal timing optimization based on sa-PSO. In: Proceedings of international conference on artificial intelligent and computer intelligent, Oct 2010, pp 80–84

    Google Scholar 

  41. García-Nieto J, Alba E, Carolina Olivera A (2012) Swarm intelligence for traffic light scheduling: application to real urban areas. Eng Appl Artif Intell 25(2), 274–283

    Google Scholar 

  42. Yong Z, Peng W (2015) Optimal control for region of the city traffic signal based on selective particle swarm optimization algorithm. In: Proceedings of international conference on intelligent transportation, Big Data Smart City, Dec 2015, pp 544–550

    Google Scholar 

  43. Geda Pasek Suta Wijaya I, Uchimura K, Koutaki G (2015) Traffic light signal parameters optimization using particle swarm optimization, case study of OOE Toroku road network optimization. In: Proceedings of international seminar intelligent technology and its application, pp 11–16

    Google Scholar 

  44. Baskan O, Haldenbilen S (2011) Ant colony optimization approach for optimizing traffic signal timings. In: Proceedings of ant colony optimization methods application, pp 205–220

    Google Scholar 

  45. Renfrew D, Yu X-H (2012) Traffic signal optimization using ant colony algorithm. In: Proceedings of international joint conference on neural network (IJCNN), June 2012, pp 1–7

    Google Scholar 

  46. Dell’Orco M, Baskan O, Marinelli M (2013) A harmony search algorithm approach for optimizing traffic signal timings. Traffic Transp 25(4):349–358

    Google Scholar 

  47. Gao K, Zhang Y, Sadollah A, Su R (2016) Optimizing urban traffic light scheduling problem using harmony search with ensemble of local search. Appl Soft Comput 48:359–372

    Article  Google Scholar 

  48. Yousef KM, Al-karaki JN, Shatnawi AM (2010) Intelligent traffic light flow control system using wireless sensors networks. J Inf Sci Eng 26(3):753–768

    Google Scholar 

  49. Zhang R, Schmutz F, Gerard K, Pomini A, Basseto L, Hassen SB, Ishikawa A, Ozgunes I, Tonguz OK (2018) Virtual traffic lights: system design and implementation. In: Vehicular technology conference (VTC-Spring), 2018 IEEE 87th IEEE

    Google Scholar 

  50. Tonguz OK (2018) Red light, green light—no light. IEEE Spectr Mag 55(10):24–29

    Article  Google Scholar 

  51. Vdot Launches Smarter Roads. Accessed on 23 Nov 2017. [Online]. Available: http://www.virginiadot.org/newsroom/statewide/2017/vdot_launches_smarterroads118667.asp

  52. Virginia Connected Corridors. Accessed on 23 Nov 2017. [Online]. Available: https://www.vtti.vt.edu/facilities/vcc.html

  53. Tonguz OK, Zhang R (2020) Harnessing vehicular broadcast communications: DSRC-actuated traffic control. IEEE Trans Intell Transp Syst 21(2)

    Google Scholar 

  54. Milanes V, Perez J, Onieya E, Gonzalez C (2010) Controller for urban intersections based on wireless communications and fuzzy logic. IEEE Trans Intell Transp Syst 11(1):243–248

    Article  Google Scholar 

  55. Bi Y, Lu X, Srinivasan D, Sun Z (2018) Optimal type-2 fuzzy system for arterial traffic signal control. IEEE Trans Intell Transp Syst, 1–19

    Google Scholar 

  56. Moghaddam MJ, Hosseini M, Safabakhsh R (2015) Traffic light control based on fuzzy Q-leaming. In: 2015 the international symposium on artificial intelligence and signal processing (AISP) Mashhad, Iran, pp 124–128

    Google Scholar 

  57. Iyer V, Jadhav R, Mavchi U, Abraham J (2016) Intelligent traffic signal synchronization using fuzzy logic and Q-learning. In: 2016 international conference on computing, analytics and security trends (CAST), Pune, India, 2016, pp 156–161

    Google Scholar 

  58. Askerzada IN, Mahmood M (2010) Control the extension time of traffic light in single junction by using fuzzy logic. Int J Electr Comput Sci 10(2)

    Google Scholar 

  59. Protri S, Wuttidittachotti P, Thajchayapong S (2015) Traffic signal control using fuzzy logic. In: International conference on electrical engineering/electronics, computer, telecommunication and information technology (ECTI-CON), Hua Hin, Thailand, pp 1–6, Aug. 2015

    Google Scholar 

  60. Firdous M, Din Iqbal FU, Ghafoor N, Qureshi NK, Naseer N (2019) Traffic light control system for four-way intersection and T-crossing using fuzzy logic. In: 2019 IEEE international conference on artificial intelligence and computer applications (ICAICA), Dalian, China, pp 178–182

    Google Scholar 

  61. Hawi R, Okeyo G, Kimwele M (2017) Smart traffic light control using fuzzy logic and wireless sensor network. In: 2017 computing conference, London, UK, pp 450–460

    Google Scholar 

  62. Wei H, Zheng G, Gayah V, Li Z (2019) A survey on traffic signal control methods. ar**v preprint ar**v:1904.08117

  63. Li L, Lv Y, Wang F (2016) Traffic signal timing via deep reinforcement learning. IEEE/CAA J Autom Sin 3(3)

    Google Scholar 

  64. Mousavi SS, Schukat M, Howley E (2017) Traffic light control using deep policy gradient and value-function-based reinforcement learning. IET Intell Transp Syst 11(7):417–423

    Article  Google Scholar 

  65. Zhang R, Zhou X, Tonguz OK (2020) Using AI for mitigating the impact of network delay in cloud-based intelligent traffic signal control. Proc J. ar**v preprint ar**v:2002.08303

  66. Zheng G, **ong Y, Zang X, Fang J, Fie H, Zheng H, Li Y, Xu K, Li Z (2019) Learning phase competition for traffic signal control. In: CIKM ‘19: proceedings of the 28th ACM international conference on information and knowledge management, pp 1963–1972, Nov 2019

    Google Scholar 

  67. Yau K-LA, Qadir J, Khoo HL, Ling MH, Komisarczuk P (2017) A survey on reinforcement learning models and algorithms for traffic signal control. ACM Comput Surv 50(3):1–38

    Article  Google Scholar 

  68. Wang X, Ke L, Qiao Z, Chai X (2020) Large-scale traffic signal control using a novel multiagent reinforcement learning. IEEE Trans Cybern, 1–14

    Google Scholar 

  69. Wei H, Zheng G, Gayah V, Li Z (2021) Recent advances in reinforcement learning for traffic signal control: a survey of models and evaluation. ACM SIGKDD Explor Newslett 22(2)

    Google Scholar 

  70. Joo H, Ahmed SH, Lim Y (2020) Traffic signal control for smart cities using reinforcement learning. Comput Commun 154:324–330

    Article  Google Scholar 

  71. Zhang R, Leteurtre R, Striner B, Alanazi A, Alghafis A, Tonguz OK (2019) Partially detected intelligent traffic signal control: environmental adaptation. In: 2019 18th IEEE international conference on machine learning and applications (ICMLA), Boca Raton, FL, USA, 2019, pp 1956–1960

    Google Scholar 

  72. Zhang R, Ishikawa A, Wang W, Striner B, Tonguz OK (2021) Using reinforcement learning with partial vehicle detection for intelligent traffic signal control. IEEE Trans Intell Transp Syst 22(1):404–415

    Article  Google Scholar 

  73. Liang XJ, Guler SI, Gayah VV (2020) An equitable traffic signal control scheme at isolated signalized intersections using connected vehicle technology. Transp Res Part C Emerg Technol 110(2020):81–97

    Article  Google Scholar 

  74. Hu X, Lu J, Wang W, Zhirui Y (2015) Traffic signal synchronization in the saturated high-density grid road network. In: Computational intelligence and neuroscience, pp 1–11

    Google Scholar 

  75. Iyer V, Jadhav R, Mavchi U, Abraham J (2016) Intelligent traffic signal synchronization using fuzzy logic and Q-learning. In: 2016 international conference on computing, analytics and security trends (CAST), Pune, 2016, pp 156–161

    Google Scholar 

  76. Amogh AS, Pujari S, Gowda SN, Nyamati VM (2016) Traffic timer synchronization based on congestion. In: 2016 international conference on computation system and information technology for sustainable solutions (CSITSS), Bengaluru, India, 2016, pp 255–260

    Google Scholar 

  77. Khan A, Ullah F, Kaleem F, Rahman S, Cho Y (2017) Sensor-based self-organized traffic control at intersections. In: International conference on information and communication technology convergence (ICTC), Oct 2017

    Google Scholar 

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Authors and Affiliations

  1. Department of Electronics and Communication Engineering, Delhi Technological University, Delhi, 110042, India

    Ishu Tomar, S. Indu & Neeta Pandey

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  1. Ishu Tomar
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  2. S. Indu
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  3. Neeta Pandey
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Editors and Affiliations

  1. Maharaja Agrasen Institute of Technology, New Delhi, India

    Deepak Gupta

  2. Jan Wyzykowski University, Polkowice, Poland

    Zdzislaw Polkowski

  3. Maharaja Agrasen Institute of Technology, New Delhi, India

    Ashish Khanna

  4. Rajnagar Mahavidyalaya, Birbhum, West Bengal, India

    Siddhartha Bhattacharyya

  5. Tijuana Institute of Technology, Tijuana, Mexico

    Oscar Castillo

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Tomar, I., Indu, S., Pandey, N. (2022). Traffic Signal Control Methods: Current Status, Challenges, and Emerging Trends. In: Gupta, D., Polkowski, Z., Khanna, A., Bhattacharyya, S., Castillo, O. (eds) Proceedings of Data Analytics and Management . Lecture Notes on Data Engineering and Communications Technologies, vol 90. Springer, Singapore. https://doi.org/10.1007/978-981-16-6289-8_14

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