International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 11 Issue: 11 | Nov 2024
p-ISSN: 2395-0072
www.irjet.net
Smart Adaptive Traffic Signal Management System Zarina Begum1, Anand Deshmukh2, Aneesh Deshmukh3, Ayush Gude4, Gautam Warvatkar5, Shreyas Dhomane6 1Professor, Dept. of Artificial Intelligence and Data Science, BRACT’s Vishwakarma Institute of Technology, Pune,
India
2Student, Dept. of Artificial Intelligence and Data Science, BRACT’s Vishwakarma Institute of Technology, Pune,
India
3Student, Dept. of Artificial Intelligence and Data Science, BRACT’s Vishwakarma Institute of Technology, Pune,
India
4Student, Dept. of Artificial Intelligence and Data Science, BRACT’s Vishwakarma Institute of Technology, Pune,
India
5Student, Dept. of Artificial Intelligence and Data Science, BRACT’s Vishwakarma Institute of Technology, Pune,
India
6Student, Dept. of Artificial Intelligence and Data Science, BRACT’s Vishwakarma Institute of Technology, Pune,
India ---------------------------------------------------------------------***---------------------------------------------------------------------
Abstract - The conventional traffic management system,
dynamic nature of modern urban traffic. These conventional systems are inherently rigid, unable to adapt to fluctuating traffic volumes throughout the day. Consequently, this rigidity leads to several inefficiencies: prolonged waiting times for drivers, increased fuel consumption, heightened levels of air pollution due to idling vehicles, and a general decrease in the quality of urban life.
which relies heavily on fixed signal timers and manual control by traffic police, often results in significant inefficiencies, prolonged waiting times, and increased fuel consumption. As urban areas continue to grow and vehicle numbers rise, these issues become more pronounced, leading to heightened congestion and reduced transportation efficiency. This research paper proposes a novel approach to traffic management that dynamically adjusts signal timings based on real-time vehicle counts at intersections. By accurately measuring the number of vehicles and using this data to determine the optimal duration of green and red lights, the system aims to streamline traffic flow, minimize delays, and decrease overall congestion. This method not only enhances the efficiency of traffic movement but also contributes to environmental sustainability by reducing idle times and subsequent emissions. Our study includes a comprehensive analysis of current traffic problems, the design of the proposed system, and a discussion on the potential benefits and challenges of implementation. The proposed solution offers a practical and immediate improvement over traditional traffic control methods, presenting a viable path towards smarter and more efficient urban mobility.
Traffic congestion is not merely an inconvenience; it has farreaching economic, environmental, and social impacts. Economically, it results in substantial losses due to wasted time and reduced productivity. Environmentally, the continuous idling of vehicles at congested intersections contributes significantly to greenhouse gas emissions, exacerbating climate change and deteriorating urban air quality. Socially, the frustration and stress associated with daily commuting in congested areas can lead to adverse health effects and decreased overall well-being Recognizing these issues, there is a pressing need to innovate beyond traditional traffic management approaches. This paper explores an intelligent traffic control system designed to address the limitations of current methods by dynamically adjusting signal timings based on real-time vehicle counts. The proposed system aims to optimize traffic flow through intersections by accurately counting vehicles and adjusting signal timings accordingly. This real-time adjustment allows for more efficient traffic movement, reducing wait times and minimizing congestion.
Key Words: Intelligent Traffic Control, Real-time Vehicle Counting, Signal Timer Optimization, Traffic Congestion, Urban Mobility, Traffic Management System, Transportation Efficiency, Environmental Sustainability.
1.INTRODUCTION
The core of this intelligent system is its ability to collect and process real-time data on vehicle numbers at intersections. By employing straightforward counting mechanisms, the system can determine the optimal duration for green and red lights, ensuring that traffic is managed more effectively. This method stands in contrast to traditional fixed-timer systems, which often operate on outdated assumptions about traffic
Urbanization and the exponential growth of vehicle ownership have led to significant challenges in managing traffic flow efficiently. Traditional traffic management systems, which rely on fixed signal timings and manual oversight by traffic police, often fall short in addressing the
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