🚦 Smart Traffic Management System
Inspiration
Urban traffic congestion is one of the biggest challenges faced by modern cities. Most traffic signals still operate using fixed-time intervals without considering the actual traffic density on the road. This often leads to unnecessary waiting times, fuel wastage, increased air pollution, and delays for emergency vehicles.
We wanted to explore how Artificial Intelligence and Computer Vision could be used to make traffic management smarter. Our goal was to build a system capable of analyzing traffic conditions automatically and recommending adaptive signal timings based on real-time vehicle density.
What it does
The Smart Traffic Management System analyzes traffic videos using AI to detect, track, and count vehicles. Based on the detected traffic volume, it predicts congestion levels and recommends an appropriate green signal duration.
The system performs the following tasks:
- Detects cars, buses, trucks, and motorcycles using YOLOv8
- Tracks each vehicle with ByteTrack to avoid duplicate counting
- Calculates the total number of vehicles and traffic density
- Classifies congestion as Low, Medium, High, or Severe
- Recommends dynamic traffic signal timing
- Generates traffic analytics in CSV format
- Displays an interactive dashboard with charts, metrics, and traffic insights
This provides traffic authorities with valuable data for making informed decisions instead of relying solely on fixed traffic signal schedules.
How we built it
The project was developed in two stages.
Stage 1 – AI-Based Traffic Analysis
We used Google Colab to process traffic videos with the YOLOv8 object detection model. The model identifies different vehicle types in each frame, while ByteTrack assigns a unique tracking ID to every vehicle so that each one is counted only once.
The processed information is stored as structured analytics data, including vehicle counts and congestion statistics.
Stage 2 – Analytics Dashboard
The analytics generated from the detection stage are displayed in a modern dashboard built with Streamlit. The dashboard includes:
- Vehicle count summary
- Traffic density visualization
- Congestion level indicator
- Dynamic signal timing recommendation
- Interactive charts and graphs
- Downloadable analytics reports
Challenges we faced
Building the project involved several technical challenges:
- Ensuring vehicles were counted only once while moving across multiple video frames.
- Maintaining reliable detection when vehicles overlapped or partially occluded one another.
- Balancing detection accuracy with processing speed.
- Designing a congestion analysis method that produced meaningful signal timing recommendations.
- Creating a dashboard that clearly presented traffic analytics in an intuitive and user-friendly way.
These challenges helped us better understand real-world computer vision systems and the importance of combining AI with effective data visualization.
What we learned
Through this project, we gained practical experience with:
- Computer Vision using YOLOv8
- Multi-object tracking using ByteTrack
- Video processing with OpenCV
- Data analysis using Pandas
- Interactive dashboard development with Streamlit
- Data visualization using Plotly
- Building end-to-end AI pipelines from video processing to analytics
We also learned how AI can be applied to solve real-world transportation and smart city challenges.
Future Improvements
We plan to extend the project with several advanced capabilities:
- Live CCTV camera integration
- Real-time traffic monitoring
- Emergency vehicle prioritization
- Automatic traffic signal control
- Accident detection and instant alerts
- Historical traffic trend analysis
- AI-based congestion forecasting
- Multi-camera intersection monitoring
- Cloud deployment for city-wide scalability
Impact
The Smart Traffic Management System demonstrates how AI and Computer Vision can improve urban mobility by providing intelligent traffic insights and adaptive signal recommendations. By reducing congestion, minimizing delays, and supporting data-driven decision-making, the solution contributes toward safer, more efficient, and more sustainable transportation systems.
Mathematical Representation
Let:
- (C) = Number of cars
- (M) = Number of motorcycles
- (B) = Number of buses
- (T) = Number of trucks
Log in or sign up for Devpost to join the conversation.