Gokuld102

Gokuld102 / ai-traffic-signal-optimization

Public

AI-based smart traffic signal optimization using YOLO, OpenCV, and Machine Learning

ai computer-vision flask machine-learning opencv
32
0
100% credibility
Found Mar 30, 2026 at 32 stars -- GitGems finds repos before they trend. Get early access to the next one.
Sign Up Free
AI Analysis
Python
AI Summary

A web dashboard prototype simulating AI-powered traffic management that detects vehicles from video, adjusts signal timings dynamically, prioritizes emergencies, and predicts congestion.

How It Works

1
🔍 Discover Smart Traffic Helper

You find this project online that uses smart detection to make traffic lights adjust automatically to busy roads and emergencies.

2
💻 Get Everything Ready

You download the files, add a sample video of traffic from your camera or road footage, and start the simple memory service on your computer.

3
🌐 Launch Your Control Panel

You start the app and open it in your web browser to see a sleek dark dashboard with live updates and charts.

4
🎥 Scan the Traffic Video

Click the button to analyze your video, and watch as it counts cars, bikes, buses, and trucks in each direction instantly.

5
🚦 See Lights Adapt Smartly

The animated traffic lights change timings based on how busy each side is, predict jams ahead, and show density levels.

6
🚑 Test Emergency Priority

Flip the switch to simulate an ambulance coming, and watch it create a clear path by turning lights green just for that route.

✅ Traffic Flow Mastered

Your dashboard now runs live simulations with counts, predictions, alerts, and optimized signals – ready for demos or learning.

Sign up to see the full architecture

5 more

Sign Up Free

Star Growth

See how this repo grew from 32 to 32 stars Sign Up Free
Repurpose This Repo

Repurpose is a Pro feature

Generate ready-to-use prompts for X threads, LinkedIn posts, blog posts, YouTube scripts, and more -- with full repo context baked in.

Unlock Repurpose
AI-Generated Review

What is ai-traffic-signal-optimization?

This Python project builds an AI-based smart traffic management system that analyzes video feeds to detect and count vehicles per lane at a 4-way junction, then adjusts signal timings dynamically based on density. It prioritizes emergency vehicles like ambulances by creating green corridors and predicts congestion levels using machine learning, all viewable on a web dashboard with live metrics, charts, and animated lights. Drop in a traffic video, hit the API endpoints like /api/process_video, and get real-time counts, signal states, and predictions stored in MongoDB.

Why is it gaining traction?

Among AI-based GitHub projects like ai based smart parking systems or ai based smart grid tools, it stands out with a ready-to-run full stack: YOLO for instant vehicle detection, rule-based optimization plus ML forecasts, and a dark-theme UI that simulates a junction without extra setup. Developers grab it for the quick "wow" of uploading any MP4 and seeing adaptive signals flip in the browser—no custom training needed.

Who should use this?

IoT devs prototyping smart city apps, computer vision students experimenting with YOLO on real-world footage, or traffic researchers needing a baseline for ai traffic signal optimization demos. It's perfect for hackathons where you want a working ai based smart traffic management system in under 30 minutes.

Verdict

Grab it for learning or proofs-of-concept—solid docs, one-command setup via Flask on port 5000, but with just 32 stars and 1.0% credibility score, it's an educational prototype, not production-ready. Extend it with real cameras for serious use.

(198 words)

Sign up to read the full AI review Sign Up Free

Similar repos coming soon.