Adaptive Traffic Control Systems: Enhancing Urban Mobility through Real-Time Traffic Management

Year : 2024 | Volume : 02 | Issue : 02 | Page : 37 45
    By

    Shubham Sampat Gunjal,

  • Kamlesh Arun Satpute,

  • Shubham Shivaji Lanke,

  • N. R. Dhumale,

  1. Student, Department of Electronics & Communication, Sinhgad College of Engineering, Pune, Maharashtra, India.
  2. Student, Department of Electronics & Communication, Sinhgad College of Engineering, Pune, Maharashtra, India.
  3. Student, Department of Electronics & Communication, Sinhgad College of Engineering, Pune, Maharashtra, India.
  4. Assistant Professor, Department of Electronics & Communication, Sinhgad College of Engineering, Pune, Maharashtra, India.

Abstract

Traffic congestion is a ubiquitous challenge in urban areas, necessitating innovative solutions to improve transportation efficiency and alleviate gridlock. Traditional traffic signal control methods often prove inadequate in dynamically adapting to fluctuating traffic conditions, leading to increased travel times, fuel consumption, and emissions. In response, adaptive traffic control systems have emerged as a promising approach to mitigate congestion and enhance traffic flow in urban environments. These devices dynamically modify signal timings in response to current demand for transportation and environmental conditions by using real-time data from monitoring systems, sensors, and other sources. By continuously optimizing signal phasing and timing plans, adaptive control strategies aim to maximize intersection throughput, minimize delays, and enhance overall traffic safety. This study provides a comprehensive review of adaptive traffic control systems, exploring their underlying principles, key components, and applications in urban transportation. Through a synthesis of existing literature, empirical analysis, and case studies, we identify the strengths and limitations of adaptive control strategies, examine emerging trends and technologies in the field, and discuss the challenges and opportunities associated with the implementation of adaptive traffic management solutions. Our findings highlight the potential of adaptive traffic control systems to transform traffic management practices, improve mobility outcomes, and create more sustainable and resilient transportation networks in the future.

Keywords: Traffic control, signal, wireless sensors networks, microcontroller, congestion, wireless sensor networks

[This article belongs to International Journal of Electrical and Communication Engineering Technology ]

How to cite this article:
Shubham Sampat Gunjal, Kamlesh Arun Satpute, Shubham Shivaji Lanke, N. R. Dhumale. Adaptive Traffic Control Systems: Enhancing Urban Mobility through Real-Time Traffic Management. International Journal of Electrical and Communication Engineering Technology. 2024; 02(02):37-45.
How to cite this URL:
Shubham Sampat Gunjal, Kamlesh Arun Satpute, Shubham Shivaji Lanke, N. R. Dhumale. Adaptive Traffic Control Systems: Enhancing Urban Mobility through Real-Time Traffic Management. International Journal of Electrical and Communication Engineering Technology. 2024; 02(02):37-45. Available from: https://journals.stmjournals.com/ijecet/article=2024/view=185133


References

  1. Dinesh Rotake, Swapnili Karmore. Intelligent Traffic Signal Control System Using Embedded System. Innov Syst Des Eng. 2012; 3(5): 11–20.
  2. Tubaishat M, Shang Y, Shi H. Adaptive Traffic Light Control with Wireless Sensor Networks. 2007 4th IEEE Consumer Communications and Networking Conference, Las Vegas, NV, USA. 2007;187–191. doi: 10.1109 CCNC.2007.44.
  3. Yousef Khalil M, Al-Karaki Jamal N, Shatnawi Ali M. Intelligent Traffic Light Flow Control System Using Wireless Sensors Networks. J Inf Sci Eng. 2010; 26: 753–768.
  4. Nang Hom Kham, Chaw Myat Nwe. Implementation of Modern Traffic Light Control System. Int J Sci Res Publ. 2014 Jun; 4(6): 1–6.
  5. Nouha Rida, Aberrahim Hasbi. Traffic Lights Control using Wireless Sensors Networks. In Proceedings of the 3rd International Conference on Smart City Applications (SCA ’18). Association for Computing Machinery, New York, NY, USA. 2018; Article 14, 1–6. https: doi.org 10.1145 3286606.3286791
  6. Varaprasad G. High Stable Power Aware Multicast Algorithm for Mobile Ad Hoc Networks. IEEE Sens J. 2013 May; 13(5): 1442–1446. doi: 10.1109 JSEN.2012.2236552.
  7. Wieselthier JE, Nguyen GD, Ephremides A. Algorithms for energy-efficient multicasting in staticad hoc wireless networks. J Mobile Netw. 2001; 6(3): 251–263.
  8. Zorzi M, Rao RR. Error control and energy consumption in communications for nomadic computing. IEEE Trans Comput. 1997 Mar; 46(3): 279–289. doi: 10.1109 12.580424.
  9. Cai Bai-gen, ShangGuan Wei, Wang Jian, Chen Rui. The Research and Realization of Vehicle Detection System Based on Wireless Magneto-resistive Sensor. 2nd International Conference on Intelligent Computation Technology and Automation. 2009; 476–479.
  10. Chavan SS, Deshpande RS, Rana JG. Design of Intelligent Traffic Light Controller Using Embedded System. 2009 2nd International Conference on Emerging Trends in Engineering & Technology, Nagpur, India. 2009; 1086–1091. doi: 10.1109 ICETET.2009.76.
  11. Chen YL, Yang HH. Minimization of travel time and weighted number of stops in a traffic-light network. Eur J Oper Res. 2003 Feb 1; 144(3): 565–80.
  12. Wangermann JP, Stengel RF. Principled negotiation between intelligent agents: a model for air traffic management. AI Engineering. 1998 Jul 1; 12(3): 177–87.
Regular Issue Subscription Review Article
Volume 02
Issue 02
Received 02/05/2024
Accepted 13/08/2024
Published 21/10/2024
Publication Time 172 Days
482

Login


My IP

PlumX Metrics