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An Analysis on Scheduling of Traffic Light at Urban Traffic Intersection Using Fuzzy Control Algorithm

Received: 7 September 2021    Accepted: 4 October 2021    Published: 19 October 2021
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Abstract

In modern society, private cars have become the first choice for many families because of their convenience and versatility. The volume of vehicles on the road is the basis of traffic accident and traffic congestion. In urban sector the traffic congestion is normally high due to the green light time interval at four road intersections. The traffic light control and time setting are basically timer control operation at current traffic control management system, this shows that, the current system is not intelligent so that there is still heavy traffic congestion. It is vital to implement routinely adjusted schedule as per the real-time position of vehicles at urban cross road intersection. Now, there are various detector systems for traffic monitoring, like Inductive Loop microwave radar, laser, infrared, ultrasonic, magnetometer and video image processing. But they have relevant weakness, such as high cost and complex technology. As a more and more widely used technology, image processing plays an important role in the management and control of intelligent transportation system. Image processing systems are based on motion detection of vehicles, wherein computer vision algorithms extract vehicles from traffic video data for traffic density estimations. This paper is an analysis on scheduling of traffic light of traffic management system using Fuzzy Control Algorithm. With the increase of the number of vehicles and population, it will also improve the traffic jam and the mood of people because of the cause of jam. Rather than previous technology, it will be low cost and simple, which can be adopted in every place as far as possible. MATLAB tool was used to figure out the variables impact on scheduling of traffic light at urban traffic intersection. The vehicle number, vehicle speed, lane length and vehicle type variables are identified and tested against vehicle driving for conclusion on traffic management performance. From findings the results were identified as the vehicle number, vehicle speed, and vehicle type have significant positive relationship with vehicle driving. However, the lane length did not significantly affect the vehicle driving. This indicates that the lane length is less important in scheduling of traffic light at urban traffic intersection.

Published in International Journal of Transportation Engineering and Technology (Volume 7, Issue 3)
DOI 10.11648/j.ijtet.20210703.14
Page(s) 85-91
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Fuzzy Control Algorithm, Vehicle Number, Vehicle Speed, Lane Length, Vehicle Type, Vehicle Driving, Scheduling

References
[1] N. Kumar, N. Anand Singh, R. Pal, and M. Kumar Sharma, “Automated traffic management system using image Processing”. International Journal of Advanced Research in Electronics And Communication Engineering (IJARECE), 2017. 6 (4).
[2] H. Zhao, G. Han, and X. Niu, “The signal control optimization of road intersections with slow traffic based on improved PSO”. Mob. Netw. Appl. 2019, 1–9. %2Fs11036-019-01225-7.
[3] M Younes,., and A Boukerche, “An efficient dynamic traffic light scheduling algorithm considering emergency vehicles for intelligent transportation systems”. Wireless Networks, 2017. 24 (7), 2451-2463. doi: 10.1007/s11276-017-1482-5.
[4] C. Jin, W. Wang, and R. Jiang, “Four-phase or two-phase signal plan? A study on four-leg intersection by cellular automaton simulations”. International Journal of Modern Physics C, 2016. 27 (03), 1650032. doi: 10.1142/s0129183116500327.
[5] N. Kumar, S. Rahman, and N. Dhakad, “Fuzzy Inference Enabled Deep Reinforcement Learning-Based Traffic Light Control for Intelligent Transportation System”. IEEE Transactions on Intelligent Transportation Systems, 2020. 1-10. doi: 10.1109/tits.2020.2984033.
[6] G. Weiwei, “Research on scheduling algorithm of intelligent traffic light”. Microcomputer Application, 2017), (TP301.6; U491.51).
[7] C. Urrea, J. Kern, and J. Alvarado “Design and Evaluation of a New Fuzzy Control Algorithm Applied to a Manipulator Robot”, Applied Sciences, MDPI, 2020 10, 7482; doi: 10.3390/app10217482.
[8] J. Jiang, Z. Wang, and F. Chen, “Urban Traffic Signals Timing at Four-Phase Signalized Intersection Based on Optimized Two-Stage Fuzzy Control Scheme”, Hidawi, 2021, Volume 2021, Article ID 6693562, https://doi.org/10.1155/2021/6693562.
[9] M. Razavi, M. Hamidkhani, and R. Sadeghi “Smart Traffic Light Scheduling in Smart City Using Image and Video Processing”. Conference: 2019 3rd International Conference on Internet of Things and Applications (IoT). 2019.
[10] S. Zhu, Y. Zhao, Y. Zhang, Q. Li, W. Wang, and S. Yang, “Short- term traffic flow prediction with wavelet and multi-dimensional taylor network model,” IEEE Transactions on Intelligent Transportation Systems, 2020, vol. 99, pp. 1–6.
[11] B. L. Ye, W. Wu, K. Ruan, L. Li, T. Chen, H. Gao, and Y Chen, “A survey of model predictive control methods for traffic signal control”. IEEE/CAA J. Autom. Sin. 2019, 6, 623–640.
[12] J. Lin, J. Zhou, M. Lu, H. Wang, and A. Yi, “Design of robust adaptive fuzzy controller for a class of single-input single-output (siso) uncertain nonlinear systems,” Mathematical Problems in Engineering, 2020. vol. 2020, no. 9, Article ID 6178678, 11 pages.
[13] J. C. Chedjou, and K. Kyamakya, A review of traffic light control systems and introduction of a control concept based on coupled nonlinear oscillators. Recent Adv. Nonlinear Dyn. Synchronization 2018, 109, 113–149.
[14] T. Jiang, Z. Wang, and F. Chen, “Urban Traffic Signals Timing at Four-Phase Signalized Intersection Based on Optimized Two-Stage Fuzzy Control Scheme. Math”. Probl. Eng. 2021.
[15] S. Jafari, Z. Shahbazi, Y. C. Byun, “Improving the Performance of Single-Intersection Urban Traffic Networks Based on a Model Predictive Controller”. Sustainability 2021, 13, 5630. https://doi.org/10.3390/su13105630.
Cite This Article
  • APA Style

    Ma Wen, Burra Venkata Durga Kumar. (2021). An Analysis on Scheduling of Traffic Light at Urban Traffic Intersection Using Fuzzy Control Algorithm. International Journal of Transportation Engineering and Technology, 7(3), 85-91. https://doi.org/10.11648/j.ijtet.20210703.14

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    ACS Style

    Ma Wen; Burra Venkata Durga Kumar. An Analysis on Scheduling of Traffic Light at Urban Traffic Intersection Using Fuzzy Control Algorithm. Int. J. Transp. Eng. Technol. 2021, 7(3), 85-91. doi: 10.11648/j.ijtet.20210703.14

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    AMA Style

    Ma Wen, Burra Venkata Durga Kumar. An Analysis on Scheduling of Traffic Light at Urban Traffic Intersection Using Fuzzy Control Algorithm. Int J Transp Eng Technol. 2021;7(3):85-91. doi: 10.11648/j.ijtet.20210703.14

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  • @article{10.11648/j.ijtet.20210703.14,
      author = {Ma Wen and Burra Venkata Durga Kumar},
      title = {An Analysis on Scheduling of Traffic Light at Urban Traffic Intersection Using Fuzzy Control Algorithm},
      journal = {International Journal of Transportation Engineering and Technology},
      volume = {7},
      number = {3},
      pages = {85-91},
      doi = {10.11648/j.ijtet.20210703.14},
      url = {https://doi.org/10.11648/j.ijtet.20210703.14},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijtet.20210703.14},
      abstract = {In modern society, private cars have become the first choice for many families because of their convenience and versatility. The volume of vehicles on the road is the basis of traffic accident and traffic congestion. In urban sector the traffic congestion is normally high due to the green light time interval at four road intersections. The traffic light control and time setting are basically timer control operation at current traffic control management system, this shows that, the current system is not intelligent so that there is still heavy traffic congestion. It is vital to implement routinely adjusted schedule as per the real-time position of vehicles at urban cross road intersection. Now, there are various detector systems for traffic monitoring, like Inductive Loop microwave radar, laser, infrared, ultrasonic, magnetometer and video image processing. But they have relevant weakness, such as high cost and complex technology. As a more and more widely used technology, image processing plays an important role in the management and control of intelligent transportation system. Image processing systems are based on motion detection of vehicles, wherein computer vision algorithms extract vehicles from traffic video data for traffic density estimations. This paper is an analysis on scheduling of traffic light of traffic management system using Fuzzy Control Algorithm. With the increase of the number of vehicles and population, it will also improve the traffic jam and the mood of people because of the cause of jam. Rather than previous technology, it will be low cost and simple, which can be adopted in every place as far as possible. MATLAB tool was used to figure out the variables impact on scheduling of traffic light at urban traffic intersection. The vehicle number, vehicle speed, lane length and vehicle type variables are identified and tested against vehicle driving for conclusion on traffic management performance. From findings the results were identified as the vehicle number, vehicle speed, and vehicle type have significant positive relationship with vehicle driving. However, the lane length did not significantly affect the vehicle driving. This indicates that the lane length is less important in scheduling of traffic light at urban traffic intersection.},
     year = {2021}
    }
    

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  • TY  - JOUR
    T1  - An Analysis on Scheduling of Traffic Light at Urban Traffic Intersection Using Fuzzy Control Algorithm
    AU  - Ma Wen
    AU  - Burra Venkata Durga Kumar
    Y1  - 2021/10/19
    PY  - 2021
    N1  - https://doi.org/10.11648/j.ijtet.20210703.14
    DO  - 10.11648/j.ijtet.20210703.14
    T2  - International Journal of Transportation Engineering and Technology
    JF  - International Journal of Transportation Engineering and Technology
    JO  - International Journal of Transportation Engineering and Technology
    SP  - 85
    EP  - 91
    PB  - Science Publishing Group
    SN  - 2575-1751
    UR  - https://doi.org/10.11648/j.ijtet.20210703.14
    AB  - In modern society, private cars have become the first choice for many families because of their convenience and versatility. The volume of vehicles on the road is the basis of traffic accident and traffic congestion. In urban sector the traffic congestion is normally high due to the green light time interval at four road intersections. The traffic light control and time setting are basically timer control operation at current traffic control management system, this shows that, the current system is not intelligent so that there is still heavy traffic congestion. It is vital to implement routinely adjusted schedule as per the real-time position of vehicles at urban cross road intersection. Now, there are various detector systems for traffic monitoring, like Inductive Loop microwave radar, laser, infrared, ultrasonic, magnetometer and video image processing. But they have relevant weakness, such as high cost and complex technology. As a more and more widely used technology, image processing plays an important role in the management and control of intelligent transportation system. Image processing systems are based on motion detection of vehicles, wherein computer vision algorithms extract vehicles from traffic video data for traffic density estimations. This paper is an analysis on scheduling of traffic light of traffic management system using Fuzzy Control Algorithm. With the increase of the number of vehicles and population, it will also improve the traffic jam and the mood of people because of the cause of jam. Rather than previous technology, it will be low cost and simple, which can be adopted in every place as far as possible. MATLAB tool was used to figure out the variables impact on scheduling of traffic light at urban traffic intersection. The vehicle number, vehicle speed, lane length and vehicle type variables are identified and tested against vehicle driving for conclusion on traffic management performance. From findings the results were identified as the vehicle number, vehicle speed, and vehicle type have significant positive relationship with vehicle driving. However, the lane length did not significantly affect the vehicle driving. This indicates that the lane length is less important in scheduling of traffic light at urban traffic intersection.
    VL  - 7
    IS  - 3
    ER  - 

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Author Information
  • School of Information Science and Technology, Xiamen University Malaysia, Sepang District, Malaysia

  • School of Information Science and Technology, Xiamen University Malaysia, Sepang District, Malaysia

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