A Survey on Deep Reinforcement Learning Network for Traffic Light Cycle Control

Authors

  • V. Indhumathi  Department of Computer Science and Engineering, Government College of Technology,Coimbatore, Tamilnadu, India
  • Dr. K. Kumar   

DOI:

https://doi.org/10.32628/CSEIT206458

Keywords:

Traffic signal, Deep reinforcement learning, Cycle control

Abstract

A Traffic signal control is a challenging problem and to minimize the travel time of vehicles by coordinating their movements at the road intersections. In recent years traffic signal control systems have on over simplified information and rule-based methods and we have large amounts of data, more computing power and advanced methods to drive the development of intelligent transportation. An intelligent transport system to use the machine learning methods likes reinforcement learning and to explain the acknowledged transportation approaches and a list of recent literature in traffic signal control. In this survey can foster interdisciplinary research on this important topic.

References

  1. T. L. Willke, P. Tientrakool, and N. F. Maxemchuk, “A survey of intervehicle communication protocols and their applications,” IEEE Commun. Surveys Tuts., vol. 11, no. 2, pp. 3–20, 2nd Quart., 2009.
  2. F. Qu and F.-Y. Wang, “Intelligent transportation spaces: Vehicles, traffic, communications, and beyond,” IEEE Commun. Mag., vol. 48, no. 11, pp. 136–142, Nov. 2010.
  3. H. Hartenstein and K. P. Laberteaux, “A tutorial survey on vehicular ad hoc networks,” IEEE Commun. Mag., vol. 46, no. 6, pp. 164–171, Jun. 2008.
  4. T. Sukuvaara and P. Nurmi, “Wireless traffic service platform for combined vehicle-to-vehicle and vehicle-to-infrastructure communications,” IEEE Wireless Commun., vol. 16, no. 6, pp. 54–61, Dec. 2009.
  5. G. Korkmaz, E. Ekici, and F. Özgüner, “Supporting real-time traffic in multihop vehicle-to-infrastructure networks,” Transp. Res. C, Emerging Technol., vol. 18, no. 3, pp. 376–392, Jun. 2010.
  6. Y. Bi, L. X. Cai, X. Shen, and H. Zhao, “Efficient and reliable broadcast in inter-vehicle communications networks: A cross layer approach,” IEEE Trans. Veh. Technol., vol. 59, no. 5, pp. 2404–2417, Jun. 2010.
  7. T. H. Luan, X. Ling, and X. Shen, “Provisioning QoS controlled media access in vehicular to infrastructure communications,” Ad Hoc Netw., vol. 10, no. 2, pp. 231–242, Mar. 2012.
  8. R. Horowitz and P. Varaiya, “Control design of an automated highway system,” Proc. IEEE, vol. 88, no. 7, pp. 913–925, Jul. 2000.
  9. J. A. Misener and S. E. Shladover, “PATH investigations in vehicleroadside cooperation and safety: A foundation for safety and vehicleinfrastructure integration research,” in Proc. IEEE Conf. Intell. Transp. Syst., 2006, pp. 9–16.
  10. R. Rajamani, H. Tan, B. Law, and W. Zhang, “Demonstration of integrated longitudinal and lateral control for the operation of automated vehicles in platoons,” IEEE Trans. Control Syst. Technol., vol. 8, no. 4, pp. 695–708, Jul. 2000.
  11. V. Milanés, J. Godoy, J. Villagrá, and J. Pérez, “Automated on-ramp merging system for congested traffic situations,” IEEE Trans. Intell. Transp. Syst., vol. 12, no. 2, pp. 500–508, Jun. 2011.
  12. J. Little, “The synchronization of traffic signals by mixed-integer linear programming,” Oper. Res., vol. 14, no. 4, pp. 568–594, Jul./Aug. 1966.
  13. M. Papageorgiou, C. Diakaki, V. Dinopoulou, A. Kotsialos, and Y. Wang, “Review of road traffic control strategies,” Proc. IEEE, vol. 91, no. 12, pp. 2043–2067, Dec. 2003.
  14. A. G. Sims and K. W. Dobinson, “The Sydney coordinated adaptive traffic (SCAT) system—Philosophy and benefits,” IEEE Trans. Veh. Technol., vol. VT-29, no. 2, pp. 130–137, May 1980.
  15. P. B. Hunt, D. I. Robertson, R. D. Bretherton, and M. C. Royle, “The SCOOT on-line traffic signal optimisation technique,” Traffic Eng. Control, vol. 23, no. 4, pp. 190–199, Apr. 1982.
  16. P. Mirchandani and F.-Y. Wang, “RHODES to intelligent transportation systems,” IEEE Intell. Syst., vol. 20, no. 1, pp. 10–15, Jan./Feb. 2005.
  17. A. Gaur and P. Mirchandani, “Method for real-time recognition of vehicle platoons,” Transp. Res. Rec., no. 1748, pp. 8–17, 2002.
  18. Y. Jiang, S. Li, and D. E. Shamo, “A platoon-based traffic signal timing algorithm for major–minor intersection types,” Transp. Res. B, Methodol., vol. 40, no. 7, pp. 543–562, Aug. 2006.
  19. X.-F. Xie, G. J. Barlow, S. F. Smith, and Z. B. Rubinstein, “Platoon-based self-scheduling for real-time traffic signal control,” in Proc. IEEE Conf. Intell. Transp. Syst., 2011, pp. 879–884.
  20. Li, Li, Ding Wen, and Danya Yao. "A survey of traffic control with vehicular communications." IEEE Transactions on Intelligent Transportation Systems 15, no. 1 (2013): 425-432.
  21. Wei, H., Zheng, G., Gayah, V. and Li, Z., 2019. A survey on traffic signal control methods. arXiv preprint arXiv:1904.08117.

IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

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Published

2020-08-30

Issue

Volume 6, Issue 4, July-August-2020

Section

Research Articles

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How to Cite

[1]
V. Indhumathi, Dr. K. Kumar , " A Survey on Deep Reinforcement Learning Network for Traffic Light Cycle Control" International Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 6, Issue 4, pp.285-293, July-August-2020. Available at doi : https://doi.org/10.32628/CSEIT206458