Traffic Light Management System
A smart AI based solution for traffic management on routes with heavy traffic from different directions, with real-time monitoring and adaptation of traffic light timings.
Created on 10th November 2024
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Traffic Light Management System
A smart AI based solution for traffic management on routes with heavy traffic from different directions, with real-time monitoring and adaptation of traffic light timings.
The problem Traffic Light Management System solves
Our project addresses the inefficiency in urban traffic light systems that often leads to congestion, wasted fuel, and increased air pollution. Traditional traffic signals operate on fixed schedules, which do not adapt to real-time traffic conditions, causing unnecessary delays or inadequate green light durations. By integrating live video feed analysis to measure vehicular density at intersections, our system dynamically adjusts green light durations based on real-time traffic flow. This allows for a more balanced and responsive traffic system, reducing wait times, fuel consumption, and emissions. The system also prioritizes intersections with higher traffic density, easing congestion during peak hours. By optimizing traffic light control in this way, our project offers a scalable solution for cities seeking to improve transportation efficiency, reduce environmental impact, and enhance commuter satisfaction.
Challenges we ran into
In developing this project, we encountered several key challenges:
Real-Time Data Processing: Processing video feeds for density calculation at high speeds was computationally demanding, especially with multiple traffic lights and intervals. We had to optimize our algorithms to ensure quick, accurate calculations without sacrificing precision.
Lighting and Environmental Variability: Different lighting conditions (day vs. night) and weather (rain, fog) affected the video analysis. Adjusting parameters dynamically to maintain reliable density calculations was challenging and required adaptive filtering techniques.
Hardware and Resource Constraints: Implementing this system on standard hardware posed constraints in terms of processing power and storage, particularly when handling continuous video streams. This led us to carefully balance computational load and resource usage.
Algorithm Optimization: Ensuring that our density calculation and green-light allocation algorithms were both responsive and fair required multiple iterations and tuning to handle varying densities without causing inefficiencies at other lights.
Scalability and Integration: Making the system scalable and compatible with existing infrastructure required careful planning, as real-world deployment would involve integration with different types of traffic light controllers and network setups.