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

Institutions

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
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

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

P. Priya Ponnuswamy, Shabariram Chokkalingam Palaniappan, Bhuvana Shanmugam
1 citations
International Journal of Computational and Experimental Science and Engineering
Traffic Prediction and Management Techniques
1.01
article

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

P. Priya Ponnuswamy, Shabariram Chokkalingam Palaniappan, Bhuvana Shanmugam
article en
1 citations

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.

International Journal of Computational and Experimental Science and EngineeringVol. 11(2)
PSG INSTITUTE OF TECHNOLOGY AND APPLIED RESEARCH (IN), Vellore Institute of Technology University (IN)
Openalex Percentile: Top 26%
Traffic Prediction and Management Techniques
1.01
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.