(45-6) 16 * << * >> * Russian * English * Content * All Issues

A deterministic predictive traffic signal control model in intelligent transportation and geoinformation systems
V.V. Myasnikov 1,2, A.A. Agafonov 1, A.S. Yumaganov 1

1 Samara National Research University, 443086, Samara, Russia, Moskovskoye Shosse 34,
2 IPSI RAS – Branch of the FSRC "Crystallography and Photonics" RAS,
443001, Samara, Russia, Molodogvardeyskaya 151

 PDF, 958 kB

DOI: 10.18287/2412-6179-CO-1031

Pages: 917-925.

Full text of article: Russian language.

Abstract:
In this paper, we propose a traffic signal control method in intelligent transportation and geoinformation systems, based on a deterministic predictive model. The method provides adaptive control based on traffic data, including data from connected and autonomous vehicles. The proposed method is compared with the state-of-the-art traffic signal control solutions: empirical control algorithms and reinforcement learning-based control methods. An advantage of the proposed method is shown and directions of further research are outlined.

Keywords:
image segmentation, road pavement distress, synthetic dataset, generative adversarial network, convolutional neural network.

Citation:
Myasnikov VV, Agafonov AA, Yumaganov AS. A deterministic predictive traffic signal control model in intelligent transportation and geoinformation systems. Computer Optics 2021; 45(6): 917-925. DOI: 10.18287/2412-6179-CO-1031.

Acknowledgements:
The work was supported by the Russian Science Foundation under grant No.21-11-00321, https://rscf.ru/en/project/21-11-00321/.

References:
  1. The economist. The cost of traffic jams. Source: <https://www.economist.com/blogs/economist-explains/2014/11/economist-explains-1>.
  2. Schrank D, Eisele B, Lomax T. TTI’s 2012 urban mobility report. Source: <https://static.tti.tamu.edu/tti.tamu.edu/documents/umr/archive/mobility-report-2012.pdf>.
  3. Wei H, Zheng G, Gayah V, Li Z. A survey on traffic signal control methods. Source: <http://arxiv.org/abs/1904.08117>.
  4. Agafonov AA, Myasnikov VV. Numerical route reservation method in the geoinformatic task of autonomous vehicle routing. Computer Optics 2018; 42(5): 912-920. DOI: 10.18287/2412-6179-2018-42-5-912-920.
  5. Agafonov AA, Yumaganov AS, Myasnikov VV. Big data analysis in a geoinformatic problem of short-term traffic flow forecasting based on a k nearest neighbors method. Computer Optics 2018; 42(6): 1101-1111. DOI: 10.18287/2412-6179-2018-42-6-1101-1111.
  6. Hunt PB, Robertson DI, Bretherton RD, Royle MC. The SCOOT on-line traffic signal optimisation technique. Traffic Eng Control 1982; 23(4): 190-192.
  7. Hunt PB, Robertson DI, Bretherton RD, Royle MC. SCOOT, a traffic responsive method of coordinating signals. Laboratory report, Vol 1014. Transport and Road Research Laboratory; 1981.
  8. Lowrie PR. SCATS–a traffic responsive method of controlling urban traffic. Roads and Traffic Authority NSW; 1990.
  9. RF Government Resolution No 1090 of October 23, 1993 [In Russian]. Source: <https://base.garant.ru/1305770/>.
  10. Webster F. Traffic signal settings. Road Research Laboratory; 1958; 39.
  11. Varaiya P. The max-pressure controller for arbitrary networks of signalized intersections. In Book: Ukkusuri SV, Ozbay K, eds. Advances in dynamic network modeling in complex transportation systems. Vol 2. Complex networks and dynamic systems 2013: 27-66. DOI: 10.1007/978-1-4614-6243-9_2.
  12. Gershenson C. Self-organizing traffic lights. Source: <https://arxiv.org/abs/nlin/0411066>.
  13. Cools SB, Gershenson C, Hooghe BD. Self-organizing traffic lights: A realistic simulation. In Book: Prokopenko M, ed. Advances in applied self-organizing systems. 2nd ed. London: Springer-Verlag; 2013: 43-55. DOI: 10.1007/978-1-4471-5113-5_3.
  14. Genders W, Razavi S. An open-source framework for adaptive traffic signal control. Source: <https://arxiv.org/abs/1909.00395>.
  15. Li L, Wen D, Yao D. A survey of traffic control with vehicular communications. IEEE Trans Intell Transp Syst 2014; 15(1): 425-432. DOI: 10.1109/TITS.2013.2277737.
  16. Papageorgiou M, Diakaki C, Dinopoulou V, Kotsialos A, Wang Y. Review of road traffic control strategies. Proc IEEE 2003; 91(12): 2043-2067. DOI: 10.1109/JPROC.2003.819610.
  17. Roess RP, Prassas ES, McShane WR. Traffic engineering. Upper Saddle River: Prentice Hall; 2004.
  18. Gokulan BP, Srinivasan D. Distributed geometric fuzzy multiagent urban traffic signal control. IEEE Trans Intell Transp Syst 2010; 11(3): 714-727. DOI: 10.1109/TITS.2010.2050688.
  19. Teodorović D. Swarm intelligence systems for transportation engineering: Principles and applications. Transp Res Part C Emerg Technol 2008; 16(6): 651-667. DOI: 10.1016/j.trc.2008.03.002.
  20. Mikami S, Kakazu Y. Genetic reinforcement learning for cooperative traffic signal control. Proc First IEEE Conf on Evolutionary Computation 1994: 223-228. DOI: 10.1109/ICEC.1994.350012.
  21. Lee J, Abdulhai B, Shalaby A, Chung EH. Real-time optimization for adaptive traffic signal control using genetic algorithms. J Intell Transp Syst 2005; 9(3): 111-122. DOI: 10.1080/15472450500183649.
  22. Ricalde E, Banzhaf W. Evolving adaptive traffic signal controllers for a real scenario using genetic programming with an epigenetic mechanism. 16th IEEE Int Conf on Machine Learning and Applications 2017: 897-902. DOI: 10.1109/ICMLA.2017.00-41.
  23. Wei H, Zheng G, Yao H, Li Z. IntelliLight: A reinforcement learning approach for intelligent traffic light control. Proc 24th ACM SIGKDD Int Conf on Knowledge Discovery and Data Mining 2018: 2496-2505. DOI: 10.1145/3219819.3220096.
  24. Wei H, Xu N, Zhang H, Zheng G, Zang X, Chen C, Zhang W, Zhu Y, Xu K, Li Z. CoLight: Learning network-level cooperation for traffic signal control. Proc 28th ACM Int Conf on Information and Knowledge Management 2019: 1913-1922. DOI: 10.1145/3357384.3357902.
  25. Chen C, Wei H, Xu N, Zheng G, Yang M, Xiong Y, Xu K, Li Z. Toward a thousand lights: decentralized deep reinforcement learning for large-scale traffic signal control. Proc AAAI Conf on Artificial Intelligence 2020: 34(4): 3414-3421. DOI: 10.1609/aaai.v34i04.5744.
  26. Liu Y, Liu L, Chen WP. Intelligent traffic light control using distributed multi-agent Q learning. IEEE 20th Int Conf on Intelligent Transportation Systems (ITSC) 2017: 1-8. DOI: 10.1109/ITSC.2017.8317730.
  27. Li Z, Yu H, Zhang G, Dong S, Xu C-Z. Networkwide traffic signal control optimization using a multi-agent deep reinforcement learning. Transp Res Part C Emerg Technol 2021; 125. DOI: 10.1016/j.trc.2021.103059.
  28. Yau KA, Qadir J, Khoo HL. A survey on reinforcement learning models and algorithms for traffic signal control. ACM Comput Surv 2017; 50(3): 1-38. DOI: 10.1145/3068287.
  29. Mannion P, Duggan J, Howley E. An experimental review of reinforcement learning algorithms for adaptive traffic signal control. In Book: McCluskey ThL, Kotsialos A, Müller JP, Klügl F, Rana O, eds. Autonomic road transport support systems. Switzerland: Springer International Publishing; 2016: 47-66. DOI: 10.1007/978-3-319-25808-9_4.
  30. Chu T, Wang J, Li Z. Multi-agent deep reinforcement learning for large-scale traffic signal control. IEEE Trans Intell Transp Syst 2019; 21(3): 1086-1095. DOI: 10.1109/TITS.2019.2901791.
  31. Gregurić M, Vujić M, Alexopoulos C, Miletić M. Application of deep reinforcement learning in traffic signal control: An overview and impact of open traffic data. Appl Sci 2020; 10(11). DOI: 10.3390/app10114011.
  32. Qadri SSSM, Gökçe MA, Öner E. State-of-art review of traffic signal control methods: challenges and opportunities. Eur Transp Res Rev 2020; 12: 55. DOI: 10.1186/s12544-020-00439-1.
  33. Watkins CJCH. Learning from delayed rewards. Source: <http://www.cs.rhul.ac.uk/~chrisw/thesis.html>.
  34. Ronald WJ. Simple statistical gradient-following algorithms for connectionist reinforcement learning. Mach Learn 1992; 8: 229-256. DOI: 10.1007/BF00992696.
  35. Sutton RS, McAllester D, Mansour Y. Policy gradient methods for reinforcement learning with function approximation. Proc 12th Int Conf on Neural Information Processing Systems 1999; 1057-1063.
  36. Van Hasselt H. Double Q-learning. Advances in Neural Information Processing Systems 23 (NIPS 2010) 2010: 2613-2622.
  37. Mnih V, Badia AP, Mirza M, Graves A, Lillicrap T, Harley T, Silver D, Kavukcuoglu K. Asynchronous methods for deep reinforcement learning. Proc 33rd Int Conf on Machine Learning 2016: 1928-1937.
  38. Haarnoja T, Zhou A, Abbeel P, Levine S. Soft Actor-Critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor. Proc 35th Int Conf on Machine Learning 2018: 1861-1870.
  39. Martinez FJ, Toh CK, Cano JC, Calafate CT, Manzoni P. A survey and comparative study of simulators for vehicular ad hoc networks (VANETs). Wirel Commun Mob Comput 2011; 11(7): 813-828. DOI: 10.1002/wcm.859.

© 2009, IPSI RAS
151, Molodogvardeiskaya str., Samara, 443001, Russia; E-mail: journal@computeroptics.ru ; Tel: +7 (846) 242-41-24 (Executive secretary), +7 (846) 332-56-22 (Issuing editor), Fax: +7 (846) 332-56-20