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Combined Intelligent Control of a Signalized Intersection of Multilane Urban Highways

  • Anatoliy A. Solovyev
  • Andrey M. ValuevEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1126)

Abstract

The paper treats the problem of intelligent traffic control at signalized intersections of urban highways and evolves the approach based on joint choice of the phase separation of passage directions and the durations of the traffic light cycle phases. The method is proposed for generation of the entire set of traffic separation schemes (TSSs) with a limited conflict level, the latter depending on the number of active conflict points of merging on traffic light cycle phases. The linear programming problem for determination of the most efficient parameters of the traffic light cycle for a given TSS is formulated; its solution is recommended to be used for the choice of preferable safe TSS for the established traffic demand. Besides, a principal way of corrections of the phase durations of the current cycle depending on composition of vehicle queues on intersection entries is proposed; simulation techniques for this objective are discussed. Aspects of “education” of drivers that provides the efficiency of the combined intelligent control of signalized intersections are discussed.

Keywords

Traffic flow Safety Intelligent control system Signalized intersection Conflict points Traffic organization Traffic light cycle Traffic separation scheme Optimization 

References

  1. 1.
    Hunt, P.B., Robertson, D.I., Bretherton, R.D.: The SCOOT on-line traffic signal optimisation technique (Glasgow). Traffic Eng. Control 23(4), 190–192 (1982)Google Scholar
  2. 2.
    Ming, W., Qin, Y., Jie, X.: The application of SCOOT in modern traffic network. Manag. Eng. 18, 93–98 (2015)Google Scholar
  3. 3.
    Zhou, B., Cao, J., Zeng, X., Wu, H.: Adaptive traffic light control in wireless sensor network-based intelligent transportation system. In: IEEE Vehicular Technology Conference (2010).  https://doi.org/10.1109/vetecf.2010.5594435. Article No. 5594435
  4. 4.
    Zhou, C., Weng, Z., Chen, X., Zhizhe, S.: Integrated traffic information service system for public travel based on smart phones applications: a case in China. Int. J. Intell. Syst. Appl. (IJISA) 5(12), 72–80 (2013)Google Scholar
  5. 5.
    Efiong, J.E.: Mobile device-based cargo gridlocks management framework for urban areas in Nigeria. Int. J. Educ. Manag. Eng. (IJEME) 7(6), 14–23 (2017)CrossRefGoogle Scholar
  6. 6.
    Goyal, K., Kaur, D.: A novel vehicle classification model for urban traffic surveillance using the deep neural network model. Int. J. Educ. Manag. Eng. (IJEME) 6(1), 18–31 (2016)CrossRefGoogle Scholar
  7. 7.
    Dennouni, N., Peter, Y., Lancieri, L., Slama, Z.: Towards an incremental recommendation of POIs for mobile tourists without profiles. Int. J. Intell. Syst. Appl. 10(10), 42–52 (2018)Google Scholar
  8. 8.
    Rida, N., Ouadoud, M., Hasbi, A., Chebli, S.: Adaptive traffic light control system using wireless sensors networks. In: 2018 IEEE 5th International Congress on Information Science and Technology (CiSt), pp. 552–556. IEEE (2018)Google Scholar
  9. 9.
    Natafgi, M.B., Osman, M., Haidar, A.S., Hamandi, L.: Smart traffic light system using machine learning. In: 2018 IEEE International Multidisciplinary Conference on Engineering Technology (IMCET), pp. 1–6 (November 2018)Google Scholar
  10. 10.
    El-Tantawy, S., Abdulhai, B., Abdelgawad, H.: Multiagent reinforcement learning for integrated network of adaptive traffic signal controllers (MARLIN-ATSC): methodology and large-scale application on downtown Toronto. IEEE Trans. Intell. Transp. Syst. 14(3), 1140–1150 (2013). Article No. 6502719CrossRefGoogle Scholar
  11. 11.
    Adebiyi, R.F.O., Abubilal, K.A., Tekanyi, A.M.S., Adebiyi, B.H.: Management of vehicular traffic system using artificial bee colony algorithm. Int. J. Image Graph. Sig. Process. (IJIGSP) 9(11), 18–28 (2017)CrossRefGoogle Scholar
  12. 12.
    Tarko, A.P.: Use of crash surrogates and exceedance statistics to estimate road safety. Accid. Anal. Prev. 45(1), 230–240 (2012)CrossRefGoogle Scholar
  13. 13.
    Rifaat, S.M., Tay, R., De Barros, A.: Effect of street pattern on the severity of crashes involving vulnerable road users. Accid. Anal. Prev. 43(1), 276–283 (2011)CrossRefGoogle Scholar
  14. 14.
    Solovyev, A.A., Valuev, A.M.: Optimization of the structure and parameters of the light cycle aimed at improving traffic safety at an intersection. In: Tsvirkun, A. (ed.) Proceedings of 2018 Eleventh International Conference “Management of Large-Scale System Development” (MLSD), Russia, Moscow, V.A. Trapeznikov Institute of Control Sciences, October 1–3, 2018. IEEE Xplore Digital Library, pp. 1–5 (2018).  https://doi.org/10.1109/MLSD.2018.8551900
  15. 15.
    Solovyev, A.A., Valuev, A.M.: Structural and parametric control of a signalized intersection with real-time “education” of drivers. In: Hu, Z., Petoukhov, S., He, M. (eds.) AIMEE2018: Advances in Artificial Systems for Medicine and Education II. Advances in Intelligent Systems and Computing, vol. 902, pp. 517–526. Springer, Cham (2020)Google Scholar
  16. 16.
    Glukharev, K.K., Ulyukov, N.M., Valuev, A.M., Kalinin, I.N.: On traffic flow on the arterial network model. In: Kozlov, V.V., et al. (eds.) Traffic and Granular Flow 2011, pp. 399–412. Springer, Berlin (2013)CrossRefGoogle Scholar
  17. 17.
    Solovyev, A.A., Valuev, A.M.: Organization of traffic flows simulation aimed at establishment of integral characteristics of their dynamics. Adv. Syst. Sci. Appl. 18(2), 1–10 (2018)Google Scholar
  18. 18.
    Valuev, A.M.: Modeling of the transport flow through crossroads with merging and divergence points. In: Tsvirkun, A. (ed.) Proceedings of 2018 Eleventh International Conference “Management of Large-Scale System Development” (MLSD), Russia, Moscow, V.A. Trapeznikov Institute of Control Sciences, October 1–3, 2018. IEEE Xplore Digital Library, pp. 1–3 (2018).  https://doi.org/10.1109/MLSD.2018.8551915

Copyright information

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Mechanical Engineering Research Institute of the Russian Academy of SciencesMoscowRussia

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