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App Logo
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Main screen of Smart Traffic Management AI
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Selecting the first traffic detection video
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Detecting the traffic and suggesting how much time should the green light be given for heavy fraffic free roads
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Detailed pie chart analytics
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Graph of the traffic video
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Incase of emergency situations(medical emergencies like ambulances) the traffic will be cleared giving the way for ambulance ensuring safety
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Piechart for the second video
Inspiration
Traffic congestion is a problem we all experience every day. Fixed traffic signals often cause unnecessary delays because they don't adapt to real-time traffic conditions. We wanted to explore how Artificial Intelligence and Computer Vision could make traffic management smarter by analyzing traffic density and adjusting signal timings automatically. Our goal was to build a solution that could reduce waiting time, improve traffic flow, and help emergency vehicles reach their destinations faster.
What it does
Smart Traffic AI is an intelligent traffic management system that uses AI to monitor traffic through camera feeds. It detects and counts vehicles on each road, analyzes traffic density, and recommends optimized signal timings based on real-time conditions instead of fixed timers.
The system also provides a live dashboard displaying vehicle counts, congestion levels, and traffic signal status, making it easier to understand and manage traffic efficiently.
How we built it
We built the project using Python for the backend and integrated computer vision to process traffic footage. AI models detect and count vehicles in each lane, while the backend analyzes the data to determine the best signal timing. A simple web interface displays the traffic statistics and simulated signal changes in real time.
Challenges we ran into
One of the biggest challenges was accurately detecting vehicles under different lighting conditions and camera angles. We also had to figure out how to convert vehicle counts into meaningful traffic signal timings. Making the system responsive while processing video frames efficiently was another challenge we faced during development.
Accomplishments that we're proud of
We're proud of building a working prototype that demonstrates how AI can improve traffic management. Seeing the system detect vehicles and automatically adjust traffic signals based on congestion was a rewarding experience. We also created a clean dashboard that makes the traffic data easy to understand
What we learned
This project helped us learn how AI and computer vision can solve real-world problems. We gained experience working with object detection, video processing, backend development, and integrating different technologies into one application. Most importantly, we learned the importance of keeping the solution simple, practical, and user-focused.
What's next for Smart Traffic AI
We plan to improve the system by supporting live CCTV feeds instead of recorded videos, detecting emergency vehicles to create green corridors, predicting traffic congestion before it happens using machine learning, and integrating with smart city infrastructure. In the future, we'd also like to develop a mobile dashboard for traffic authorities and expand the system to manage multiple intersections simultaneously.
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