Traffic Sense: Automating Traffic Lights

The project “Traffic Sense” aims to reduce traffic congestion which has become a prevalent issue in contemporary urban environments, leading to lower productivity, increased pollution, and longer commute times. The chosen project aims to address this problem by automating traffic lights and implementing a real-time system that optimizes traffic flow. Our approach involves the implementation of an automated traffic control system that dynamically adapts to real-time traffic conditions at intersections. In the following era, a significant increase in traffic has been detected. People try to step out earlier than necessary in order to avoid traffic and reach their destinations on time. In the early morning and evening hours when people are leaving for offices, schools or simply, coming back home from work, timers sometimes are not enough to control the rush. Apart from that, ambulances have to wait, even in cases of emergencies, for the signal to turn green, so that they may reach the hospital on time and save their patients. The objective of the project is efficient traffic flow, reduced response times, priority for emergency vehicles, weapon detection and safety enhancement. In the past a number of projects have been developed for the purpose of reducing traffic congestion through automated traffic lights, each project employing a different method such as the Smart Traffic Light Control System which uses IOT, Automated Real-Time Intelligent Traffic Control System for Smart Cities which utilizes sensor nodes to detect arriving vehicles and the Traffic signal control for smart cities using reinforcement learning which uses Q-Learning. The automated traffic control system, along with the various detection models, has demonstrated significant improvements in traffic flow optimization, road safety enhancement, and emergency response times. The evaluations conducted validate the effectiveness of the solution. As we look to the future, our focus remains on continuous refinement. We aim to implement the recommended enhancements, such as further training the models and exploring additional features like real-time monitoring of traffic violations. By doing so, we aspire to create a more effective and responsive system that caters to the evolving needs of urban environments.