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International Journal of Big Data Intelligent Technology, 2023, 4(1); doi: 10.38007/IJBDIT.2023.040102.

Intelligent Traffic System Based on PLC Fuzzy Control

Author(s)

Gongxing Yan and Xie Hui

Corresponding Author:
Gongxing Yan
Affiliation(s)

School of Intelligent Construction, Luzhou Vocational and Technical College, Luzhou 646000, Sichuan, China

Luzhou Key Laboratory of Intelligent Construction and Low-carbon Technology, Luzhou 646000, Sichuan, China

Abstract

This study designed an intelligent transportation system based on PLC fuzzy control for alleviating urban traffic jams and complex vehicles. The system includes two parts. The first part is a three-level fuzzy controller. Through monitoring the traffic of vehicles in each phase, the fuzzy control rules are used to optimize the phase sequence and adjust the green time length of the corresponding phase. Experimental results show that the traffic efficiency of the three-stage fuzzy controller is 11% higher than that of the traditional fuzzy controller. The second part is the emergency passage module, which dredges special vehicles. The MCGS configuration software is used to test the system, and results show that the system meets the control requirements and is suitable for various traffic conditions.

Keywords

Intelligent Transportation System, Fuzzy Control, Plc, Phase Sequence Optimization, MCGS

Cite This Paper

Gongxing Yan and Xie Hui. Intelligent Traffic System Based on PLC Fuzzy Control. International Journal of Big Data Intelligent Technology (2023), Vol. 4, Issue 2: 10-16. https://doi.org/10.38007/IJBDIT.2023.040102.

References

[1] Liu J.J., Zuo X.Q. Research on fuzzy control and optimization of traffic lights at intersections. Journal of System Simulation, 2020, 32(12):2401-2408.

[2] Tang M.A. Optimal control strategy of traffic signal combination under unbalanced road network. Control Engineering, 2019, 26(01):144-149.

[3] Deng L.Y., He W.T. Research on Fuzzy Control Method of Traffic Signal Light at Single Intersection. Foreign Electronic Measurement Technology, 2018, 37(04):83-86.

[4] He P.L. Research on Intelligent Transportation Based on Fuzzy Control. Xi’an: Xi'an University of Science and Technology, 2017.

[5] Li X.R. Traffic Signal Timing Optimization Based on Fuzzy Control. Xi’an: Xi'an University of Science and Technology, 2016.

[6] Wu S.J. Application of PLC in Intelligent Traffic Light Control System. Journal of Changzhou Institute of Technology, 2014, 27(06):29-33+41.

[7] Gongxing Yan, Hongzhi Wang. Autonomous Coordinated Control Strategy for Complex Process of Traffic Information Physical Fusion System Based on Big Data. IEEE Access, 2020(7):148370- 148377.

[8] Gongxing Yan, Qi Qin. The Application of Edge Computing Technology in the Collaborative Optimization of Intelligent Transportation System Based on Information Physical Fusion. IEEE Access, 2020(8):153264-153272.

[9] Gongxing Yan, Yanping Chen. The application of virtual Reality technology on intelligent traffic construction and Decision support in smart cities. wireless Communications and Mobile Computing. 2021, 579(10): 176-188.

[10] C. Li and P. Xu, “Application on traffic flow prediction of machine learning in intelligent transportation,” Neural Computing and Applications, vol. 33, no. 2, pp. 613–624, 2021.

[11] M. Saki, M. Abolhasan, and J. Lipman, “A novel approach for big data classification and transportation in rail networks,” IEEE Transactions on Intelligent Transportation Systems, vol. 21, no. 3, pp. 1239-1249, 2020.

[12] C. An and C. Wu, “Traffic big data assisted V2X communications toward smart transportation,” Wireless Networks, vol. 26,no. 3, pp. 1601–1610, 2020.

[13] S. K. Roy, S. Midya, and G. W. Weber, “Multi-objective multiitem fixed-charge solid transportation problem under twofold uncertainty,” Neural Computing and Applications, vol. 31,no. 12, pp. 8593–8613, 2019.

[14] A. Sukoco, A. S. Prihatmanto, R. Wijaya, I. Sadad, and R. Darmakusuma, “SEMUT: next generation public transportation architecture in the era IoT and big data,” Journal of Engineering and Applied Sciences, vol. 14, no. 12, pp. 4052–4059, 2019.

[15] T. B. A. Isabel, D. S. Javier, L. Ibai, M. Ilardia, M. N. Bilbao, and S. Campos-Cordobés, “Big data for transportation and mobility: recent advances, trends and challenges,” IET Intelligent Transport Systems, vol. 12, no. 8, pp. 742–755, 2018.

[16] D. Chen, M. Hu, H. Zhang, and J. Yin, “Forecast method for medium-long term air traffic flow considering periodic fluctuation factors,” Xinan Jiaotong Daxue Xuebao/Journal of South-west Jiaotong University, vol. 50, no. 3, pp. 562–568, 2015.

[17] L. Zhao, D. Wei, Y. Dong-Mei, G. Chai, and J.-h. Guo, “Short term traffic flow forecast based on combination of K nearest neighbor algorithm and support vector regression,” Journal of Highway and Transportation Research and Development,vol. 12, no. 1, pp. 89–96, 2018.

[18] T. Xie and J. C. Grossman, “Crystal graph convolutional neural networks for an accurate and interpretable prediction of material properties,” Physical Review Letters, vol. 120, no. 14,pp. 145301.1–145301.6, 2018.

[19] V. Korolev, A. Mitrofanov, A. Korotcov, and V. Tkachenko, “Graph convolutional neural networks as "general-purpose" property predictors: the universality and limits of applicability,” Journal of Chemical Information and Modeling, vol. 60,no. 1, pp. 22–28, 2020.

[20] D. Bone, C.-C. Lee, and T. Chaspari, “Signal processing and machine learning for mental health research and clinical applications [perspectives],” IEEE Signal Processing Magazine, vol. 34, no. 5, p. 196, 2017.

[21] A. Deleforge, D. Di Carlo, M. Strauss, R. Serizel, and L. Marcenaro, “Audio-based search and rescue with a drone: highlights from the IEEE signal processing cup 2019 student competition [SP competitions],” IEEE Signal Processing Magazine, vol. 36, no. 5, pp. 138–144, 2019.