Reduce Traffic Congestion Using Automated Traffic Navigation System

Year : 2024 | Volume : 15 | Issue : 02 | Page : 34 40
    By

    Shreyas Girish Chitnis,

  • Anushka Vitthal Kadu,

  • Maithilee Bharat Shinde,

  • Swaraj Dilip Zavare,

  • Sharayu Patil,

  1. Student, Department of Computer Engineering, Vishwaniketan’s Institute of Management Entrepreneurship and Engineering Technology (ViMEET), Khalapur, Maharashtra, India
  2. Student, Department of Computer Engineering, Vishwaniketan’s Institute of Management Entrepreneurship and Engineering Technology (ViMEET), Khalapur, Maharashtra, India
  3. Student, Department of Computer Engineering, Vishwaniketan’s Institute of Management Entrepreneurship and Engineering Technology (ViMEET), Khalapur, Maharashtra, India
  4. Student, Department of Computer Engineering, Vishwaniketan’s Institute of Management Entrepreneurship and Engineering Technology (ViMEET), Khalapur, Maharashtra, India
  5. Assistant Professor, Department of Computer Engineering, Vishwaniketan’s Institute of Management Entrepreneurship and Engineering Technology (ViMEET), Khalapur, Maharashtra, India

Abstract

The concept of activity routing without reliance on traditional activity signals is gaining significant traction. This innovative approach aims to enhance urban vehicular movement by eliminating the need for conventional traffic signal systems. This summary delves into the intricate realm of signal-free activity routing, incorporating advanced technologies such as artificial intelligence, machine learning, and sensor networks. The benefits, including reduced congestion, increased fuel efficiency, and improved traffic flow, are thoroughly discussed. Furthermore, the study identifies challenges such as ensuring safety, managing intersections, and coordinating vehicle movements. The importance of additional research and development in this field is emphasized, as it holds the potential to bring about a paradigm shift in urban mobility. The integration of these advanced technologies can lead to more efficient and intelligent traffic management systems, capable of adapting to real-time conditions and reducing the overall environmental impact. This exploration underscores the transformative potential of signal-free activity routing, advocating for continued innovation and collaboration to address the existing obstacles and fully realize its benefits for urban transportation systems.

Keywords: Machine learning, intelligent traffic management system, traffic navigation, signal systems, intersection, vehicle movements

[This article belongs to Journal of Computer Technology & Applications ]

How to cite this article:
Shreyas Girish Chitnis, Anushka Vitthal Kadu, Maithilee Bharat Shinde, Swaraj Dilip Zavare, Sharayu Patil. Reduce Traffic Congestion Using Automated Traffic Navigation System. Journal of Computer Technology & Applications. 2024; 15(02):34-40.
How to cite this URL:
Shreyas Girish Chitnis, Anushka Vitthal Kadu, Maithilee Bharat Shinde, Swaraj Dilip Zavare, Sharayu Patil. Reduce Traffic Congestion Using Automated Traffic Navigation System. Journal of Computer Technology & Applications. 2024; 15(02):34-40. Available from: https://journals.stmjournals.com/jocta/article=2024/view=155758


References

  1. Dikshit S, Atiq A, Shahid M, Dwivedi V, Thusu A. The use of artificial intelligence to optimize the routing of vehicles and reduce traffic congestion in urban areas. EAI Endorsed Trans Energy Web. 2023 Dec 15; 10: 1–13.
  2. He W, Li H, Zhi X, Li X, Zhang J, Hou Q, Li Y. Overview of V2V and V2I wireless communication for cooperative vehicle infrastructure systems. In 2019 IEEE 4th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC). 2019 Dec 20; 127–134.
  3. Li Z, Yu H, Zhang G, Dong S, Xu CZ. Network-wide traffic signal control optimization using a multi-agent deep reinforcement learning. Transp Res Part C: Emerg Technol. 2021 Apr 1; 125: 103059.
  4. Smith S, Razo M. Using traffic microsimulation to assess deployment strategies for the connected vehicle safety pilot. J Intell Transp Syst. 2016 Jan 2; 20(1): 66–74.
  5. Wang N, Wang X, Palacharla P, Ikeuchi T. Cooperative autonomous driving for traffic congestion avoidance through vehicle-to-vehicle communications. In 2017 IEEE Vehicular Networking Conference (VNC). 2017 Nov 27; 327–330.
  6. Helbing D, Schreckenberg M. Cellular automata simulating experimental properties of traffic flow. Phys Rev E. 1999 Mar 1; 59(3): R2505.
  7. Shah PM, Gupta N. Analysis of speed parameters of mixed traffic flow on the sections of arterial streets (Jalandhar and Chandigarh Cities). Indian J Sci Technol. 2016 Dec; 9(47): 1–6.
  8. Chao Q, Bi H, Li W, Mao T, Wang Z, Lin MC, Deng Z. A survey on visual traffic simulation: Models, evaluations, and applications in autonomous driving. Comput Graph Forum. 2020 Feb; 39(1): 287–308.
  9. Tsouros I, Polydoropoulou A, Tsirimpa A, Karakikes I, Tahmasseby S, Mohammad AA, Alhajyaseen WK. From Raw Data to Informed Decisions: The Development of an Online Data Repository and Visualization Dashboard for Transportation Data. Procedia Comput Sci. 2024 Jan 1; 231: 161–7.
  10. Tumiatti A. (2019 Jul 26). Object detection with non-traditional sensors. [Online]. Webthesis. Politoit. Available from: https://webthesis.biblio.polito.it/11704/
Regular Issue Subscription Review Article
Volume 15
Issue 02
Received 06/04/2024
Accepted 30/04/2024
Published 09/07/2024
232


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