International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 11 Issue: 05 | May 2024
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p-ISSN: 2395-0072
Dynamic Traffic Light Management System Anup Murumkar1, Danish Shaikh2, Sanskriti Sharma3 , Dhiraj Khivasara4 1,2,3,4 Student, Department of Electronics and Telecommunications Engineering, Vishwakarma Institute of
Information Technology, Pune, Maharashtra, India ---------------------------------------------------------------------***--------------------------------------------------------------------Currently, three main methods are used for traffic control: Abstract - The increase in urban population and vehicle numbers has resulted in a critical issue: traffic 1) Manual Control: This system requires human congestion. This issue not only frustrates and delays intervention, usually from traffic police stationed in specified drivers, but also contributes to higher fuel use and air regions. They use signboards, signal lights, and whistles to pollution. While pollution impacts many locations, control traffic flow. megacities face the brunt of the consequences. To 2) Traditional Traffic Lights with Fixed Timers: These properly manage this ever-increasing difficulty, realtraffic lights use predetermined timers, with fixed numerical time road traffic density must be monitored to ensure values defining the duration of red and green signals. These efficient signal control and traffic management. The timed settings cause the lights to go on and off automatically. effectiveness of traffic control has a substantial impact on traffic flow, necessitating optimization to meet 3) Electronic Sensors: A more advanced solution involves increasing demand. Our suggested approach uses live installing loop detectors or proximity sensors on highways. video feeds from traffic junctions to calculate traffic These sensors collect real-time traffic data, which is utilized density using image processing and artificial intelligence to change traffic signal timings accordingly. (AI). Furthermore, our system includes an algorithm that These conventional approaches have several limitations. The adjusts traffic lights based on vehicle density, to reduce manual controlling system demands a significant amount of congestion and improve transit times, and reducing labor. Due to limited traffic police resources, human traffic pollution. management is not feasible in all regions of a city or town. Thus, a more effective traffic control system is required. Static traffic control involves a fixed traffic signal with a timer for each phase, which does not respond to real-time traffic on the road. Using electronic sensors, such as proximity sensors or loop detectors, can lead to conflicting accuracy and coverage due to the expense of collecting highquality information. Limited budgets may limit the number of facilities available. Furthermore, due to the limited effectiveness range of motion, the total coverage on a network of facilities requires a lot of sensors.
Keywords: Traffic control, Traffic light system, Traffic
management, Intelligent transport systems, Smart surveillance, Computer Vision, Machine Learning, Object detection, YOLO.
1. INTRODUCTION Urban regions are dealing with an increase in vehicle traffic, resulting in capacity restrictions on road networks and a lower level of service. One important contributing issue is the use of fixed signal timers at crossings, which do not adjust to changing traffic circumstances. This rigidity causes several traffic-related concerns. As the demand for road capacity increases, there is an urgent need for innovative traffic control technologies, notably in the field of Intelligent Transport Systems (ITS).
Dynamic traffic management control systems have been prompted by the advent of new technology, especially in the fields of computer vision and artificial intelligence (AI). These systems optimize traffic flow, reduce congestion, and improve overall road network efficiency by utilizing the capability of real-time data collecting and analysis. This study examines the theory and practice of a dynamic traffic management control system, which aims to upend established traffic control models. This system attempts to dynamically modify traffic signal timings based on real-time traffic density and vehicle categorization data by utilizing computer vision techniques and clever algorithms. By using cutting-edge technology like YOLO (You Only Look Once), the suggested system is expected to yield notable enhancements in traffic control effectiveness, resulting in decreased fuel consumption and environmental impact.
Mumbai and Bangalore are prime examples of how severe traffic congestion can be. Bangalore has the terrible title of having the world's worst traffic flow, closely followed by Mumbai in fourth place, as per a comprehensive report on traffic conditions across numerous cities and countries. During rush hours, journeys in Bangalore take 71% longer, while in Mumbai, the increase is 65% [9].
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