InspirationInspiration
Urban traffic congestion wastes time, fuel, and productivity. We wanted to build a software-based solution that uses real-time map data and AI to help people avoid traffic while providing useful insights for better urban mobility.
What it does
Our system monitors live traffic conditions, predicts congestion using AI/ML, and recommends faster and less congested routes. It also provides accident and road-closure alerts, estimated travel time, fuel consumption, congestion heatmaps, and an admin dashboard with traffic analytics.
How we built it
We built the frontend using React.js and the backend using Node.js and Express.js. MongoDB stores user and traffic-related data. Google Maps APIs provide mapping, routing, and traffic information, while a Python-based ML model analyzes historical and current traffic data to predict congestion.
Challenges we ran into
The main challenges were working with real-time traffic data, integrating multiple APIs, designing an effective congestion prediction model, and combining AI predictions with route optimization. We also had to ensure that the system remained simple and responsive for users.
Accomplishments that we're proud of
We created a completely software-based smart mobility solution without requiring physical sensors or cameras. The system combines live traffic monitoring, AI-based prediction, intelligent route recommendations, and an analytics dashboard into one platform.
What we learned
We learned how to integrate third-party APIs into a full-stack application, process geographical and traffic data, develop and evaluate ML models, design responsive dashboards, and connect AI predictions with real-world decision-making.
What's next for us
We plan to improve prediction accuracy using larger historical datasets, add more advanced ML models, support multiple cities, improve fuel and emission estimation, and develop personalized route recommendations based on individual travel preferences.
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