Smart Traffic Management System 🚦 Inspiration ✨

Urban cities face severe traffic congestion, leading to wasted time, higher fuel consumption, and increased pollution. I was inspired to create a system that uses AI and IoT sensors to monitor real-time traffic and optimize signal timings.

What I Learned 📚

How to collect and preprocess real-time traffic data.

The importance of feature selection in improving model accuracy.

Using Reinforcement Learning to dynamically adjust signal timings.

Deployment strategies for integrating ML models with IoT devices.

How I Built It 🔧

Data Collection: Simulated vehicle flow data using SUMO (Simulation of Urban Mobility) and gathered live datasets from open traffic APIs.

Data Preprocessing: Cleaned and normalized traffic density values.

Model Design: Implemented a Q-learning algorithm to optimize green light durations.

Reward function:

𝑅

− ( waiting time ) + 𝛼 ⋅ ( vehicles cleared ) R=−(waiting time)+α⋅(vehicles cleared)

where 𝛼 α is a weight factor balancing efficiency and fairness.

System Integration: Connected the model to a Raspberry Pi, simulating smart traffic lights.

Visualization: Built a dashboard using Python (Flask + Plotly) to show traffic flow and system decisions in real time.

Challenges Faced

Handling imbalanced traffic flow (some lanes had sparse data while others were overloaded).

Designing a reward function that balances fairness (avoiding lane starvation) with efficiency.

Simulating realistic scenarios to test the algorithm before real-world deployment.

Integrating ML models with limited-resource hardware (Raspberry Pi).

Future Scope

Extend to autonomous vehicle communication.

Integrate with city-wide IoT networks for smart cities.

Add predictive analytics to forecast peak congestion hours.

Built With

• docker-databases:-mongodb
• javascript-frameworks:-flask
• lambda)
• languages:-python
• opentraffic-api-other-tools:-github-actions-(ci/cd)
• postgresql-apis:-google-maps-api
• react.js-platforms:-aws-(ec2
• tensorflow