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Fuzzy Control Under Time-Varying Universe and Phase Optimization in Traffic Lights (ICSSE 2020)

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Abstract

As the pace of life accelerates, people are troubled by spending too much time on commuting, and people’s waiting time for travel can be reduced by reasonable strategy of traffic control. Previous research on traffic control mainly focused on timing and ignored the study of phase. For asymmetric or small traffic flow, the common timing project based on single-loop phase structure cannot achieve optimal control. Therefore, this paper proposed a fuzzy control method of traffic light under time-varying universe and the phase structure have been optimized. For asymmetric traffic flow, the phase structure is optimized by adding additional phases to form a double-loop phase structure. In slack hour, the phase structure is optimized by merging phase. Taking an intersection in Jing-Hong city as an example, the feasibility of the method is verified. The results show that the delay of vehicles can be reduced and the traffic capacity of the intersection can be improved by this method.

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References

  1. Rustem, S., Regina, N.: Traffic safety system management. Transportat. Res. Procedia. 36, 676–681 (2018)

    Article  Google Scholar 

  2. Mohan, D.: Traffic safety: Rights and obligations. Accid. Anal. Prevent. 128, 159–163 (2019)

    Article  Google Scholar 

  3. Li, Z.H., Cao, Q., Zhao, Y.H., Tao, P.F., Zhou, R.: Krill herd algorithm for signal optimization of cooperative control with traffic supply and demand. IEEE Access. 7, 10776–10786 (2019)

    Article  Google Scholar 

  4. Dotoli, M., Fanti, M.P., Meloni, C.: A signal timing plan formulation for urban traffic control. Control. Eng. Pract. 42(3), 1297–1311 (2006)

    Article  Google Scholar 

  5. Akcelik, R.: Traffic signals: capacity and timing analysis. Transportat. Res. 5(6), 505–505 (1981)

    Google Scholar 

  6. Webster, F.V.: Traffic signal settings. Technical Report. H.M, Stationery Office (1958)

  7. Pappis, C.P., Mamdani, E.H.: A fuzzy logic controller for a traffic junction. IEEE. Transact. Syst. Man. Cybernet. 7(10), 707–717 (1977)

    Article  Google Scholar 

  8. Chen, H., Chen, S.F.: A method for real time traffic fuzzy control of a single intersection. Informat. Control. 36(3), 227–233 (1997)

    Google Scholar 

  9. Nakatsuyama, M., Nagahashi, H., Nishizuka, N.: Fuzzy logic phasecontroller for traffic junctions in the one-way arterial road. In IFACproceedings series. 2865C2870 (1985)

  10. Chiu, S., Chand, S.: Self-organizing traffic control via fuzzy logic. In: Proceedings of the 32nd IEEE conference on decision and control. 2, 1897C1902 (1993)

  11. Zhang, W.B., Wu, B.Z., Liu, W.J.: Anti-congestion fuzzy algorithm fortraffic control of a class of traffic networks. In: IEEE international conference ongranular computing. 124 (2007)

  12. Rahman, S.M., Ratrout, N.T.: Review of the fuzzy logic based approach intraffic signal control: prospects in Saudi Arabia. J. Transportat. Syst. Eng. Informat. Technol. 9, 5870 (2009)

    Google Scholar 

  13. Odeh, S.M., Mora, A.M., Moreno, M.N., Merelo, J.J.: A hybrid fuzzy genetic algorithm for an adaptive traffic signal system. Adv. Fuzzy. Syst., (2015). https://doi.org/10.1155/2015/378156

    Article  Google Scholar 

  14. Sun, W., Wu, Y.Q., Sun, Z.Y.: Command filter-based finite-time adaptive fuzzy control for uncertain nonlinear systems with prescribed performance. IEEE. Transact. Fuzzy. Syst. 10(1109), 2967295 (2020)

    Google Scholar 

  15. Sun, W., Su, S.F., Wu, Y.Q., Xia, J.W.: A novel adaptive fuzzy control for output constrained stochastic non-strict feedback nonlinear systems. IEEE. Transact. Fuzzy. Syst. 10(1109), 2969909 (2020)

    Google Scholar 

  16. Khooban, M.H., Vafamand, N., Liaghat, A., Dragicevic, T.: An optimal general type-2 fuzzy controller for Urban Traffic Network. ISA. Transact. 66, 335–343 (2017)

    Article  Google Scholar 

  17. Bi, Y.R., Lu, X.B., Sun, Z., Srinivasan, D., Sun, Z.X.: Optimal type-2 fuzzy system for arterial traffic signal control. IEEE. Transact. Intell. Transportat. Syst. 19(9), 3009–3027 (2018)

    Article  Google Scholar 

  18. Li, R.M., Jiang, C.Y., Zhu, F.H., Chen, X.L.: Traffic flow data forecasting based on interval type-2 fuzzy sets theory. IEEE/CAA J. Autom. Sinica. 3(2), 141–148 (2016)

    Article  MathSciNet  Google Scholar 

  19. Cao, X.L., Mo, H., Zhu, F.H.: Fuzzy control of timing for traffic lights based on time-varying universe. Measure. Control. Technol. 38(11), 115–120 (2019). (in Chinese)

    Google Scholar 

  20. Mo, H., Hao, X.X., Zheng, H.B., Liu, Z.Z., Wen, D.: Linguistic dynamic analysis of traffific flow based on social mediaa case study. IEEE Transact. Intell. Transport. Syst. 17(9), 2668–2676 (2016)

    Article  Google Scholar 

  21. Henrique, D., Norian, M., Furio, D.: Genetic algorithm-based traffic lights timing optimization and routes definition using Petri net model of urban traffic flow. IFAC. Proceed. Vol. 47(3), 11326–11331 (2014)

    Article  Google Scholar 

  22. Talab, H.S., Mohammadkhani, H., Haddadnia, J.: Controlling multi variable traffic light timing in an isolated intersection using a novel fuzzy algorithm. J. Intell. Fuzzy. Syst. 25(1), 103–116 (2013)

    Article  Google Scholar 

  23. Maythem, K.A., Mohd, N.K., Madzlan, N., Brahim, B.S., Marwan, A.: High accuracy traffic light controller for increasing the given green time utilization. Comput. Electri. Eng. 41, 40–51 (2015)

    Article  Google Scholar 

  24. Yin, Y.F.: Robust optimal traffic signal timing. Transportat. Res. Part. B. 42(10), 911–924 (2008)

    Article  Google Scholar 

  25. Araghi, S., Khosravi, A., Creighton, D.: A review on computational intelligence methods for controlling traffic signal timing. Exp. Syst. Appl. 42(3), 1538–1550 (2015)

    Article  Google Scholar 

  26. Zadeh, L.A.: Fuzzy sets. Informat. Control. 8(3), 338–353 (1965)

    Article  Google Scholar 

  27. Mo, H., Zhao, X.M., Wang, F.Y.: Application of interval type-2 fuzzy sets in unmanned vehicle visual guidance. Int. J. Fuzzy. Syst. 21(6), 1661–1668 (2019)

    Article  Google Scholar 

  28. Mo, H.: Linguistic dynamic orbits in the time varying universe of discourse. Acta. Automatica. Sinica. 38(10), 1585–1594 (2012)

    Article  MathSciNet  Google Scholar 

  29. Mo, H., Hao, X.X.: Linguistic dynamic analysis of traffic light timing design within the time-varying universe. Acta. Automat. Sin. 43(12), 2202–2212 (2017). (in Chinese)

    Google Scholar 

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Acknowledgements

The work was supported by National Nature Science Foundation of China (NO.61473048, 61074093).

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Correspondence to Hong Mo.

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Zhou, C., Mo, H., Chen, X. et al. Fuzzy Control Under Time-Varying Universe and Phase Optimization in Traffic Lights (ICSSE 2020). Int. J. Fuzzy Syst. 23, 692–703 (2021). https://doi.org/10.1007/s40815-020-00995-7

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  • DOI: https://doi.org/10.1007/s40815-020-00995-7

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