Intelligent Traffic Signal Management using Global Positioning System and Distribution based optimization in Edge-Cloud Ecosystem
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.
Authors
- P. Priya Ponnuswamy (ORCID: https://orcid.org/0000-0002-1690-1503)
- Shabariram Chokkalingam Palaniappan
- Bhuvana Shanmugam
Institutions
- PSG INSTITUTE OF TECHNOLOGY AND APPLIED RESEARCH (IN)
- Vellore Institute of Technology University (IN)
Publication Details
- Journal
- International Journal of Computational and Experimental Science and Engineering
- Published
- 2025-04-13
- DOI
- https://doi.org/10.22399/ijcesen.1513
- Citations
- 1
- Primary Topic
- Traffic Prediction and Management Techniques
- Type
- article
- Field-Weighted Citation Impact
- 1.01