Intelligent Traffic Signal Management using Global Positioning System and Distribution based optimization in Edge-Cloud Ecosystem

Shabariram, C P (2025) Intelligent Traffic Signal Management using Global Positioning System and Distribution based optimization in Edge-Cloud Ecosystem. International Journal of Computational and Experimental Science and Engineering, 11 (2). ISSN 2149-9144

[thumbnail of Intelligent Traffic Signal Management using Global Positioning System and Distribution based optimization in Edge-Cloud Ecosystem.pdf] Text
Intelligent Traffic Signal Management using Global Positioning System and Distribution based optimization in Edge-Cloud Ecosystem.pdf - Published Version
Available under License Creative Commons Attribution.

Download (927kB)

Abstract

Increasing population and Industrialization are the major problems of today’s modern world. Due to this, there’s an increased traffic demand. And this, besides positive profits, also has its negative impacts like pollution and accidents. To divert the congestion of vehicles, a traffic signal has been designed, typically operating on a predefined timer. The traditional system fails to respond to live traffic conditions. However, this approach is not an entirely effective solution for managing traffic. The scope of the proposed system is to dynamically change the time between each green signal by monitoring the traffic in a specific direction. This solves the problem of longer unnecessary waiting time of passengers through an automated system which works using Google cloud and IoT Edge device. The primary objective of the system lies in efficient opening of traffic signals by continuously watching the traffic density in a road of single direction using Google Maps, analyzing traffic strength with color detection, and sending/receiving these data through cloud. The system can be easily integrated in real time on existing traffic signals, with minimal setup costs. The result indicates a minimal waiting time due to dynamic traffic density and self adaptive nature. In the best-case scenario, each lane takes 20 seconds, making the system more efficient than conventional traffic systems by reducing the cycle time by 27.76 seconds per signal loop.

Item Type: Article
Subjects: C Computer Science and Engineering > Optimization Techniques
C Computer Science and Engineering > Cloud and Edge Computing
C Computer Science and Engineering > IoT and Security
Divisions: Computer Science and Engineering
Depositing User: Dr Krishnamurthy V
Date Deposited: 10 May 2025 04:09
Last Modified: 10 May 2025 04:09
URI: https://ir.psgitech.ac.in/id/eprint/1430

Actions (login required)

View Item
View Item