Inspiration

Urban traffic congestion wastes time, fuel, and productivity. With the rise of smart cities, we envisioned an AI-powered traffic signal system that adapts in real-time to reduce waiting times and improve traffic flow efficiency.

What it does

VelociCity-Optiroute is an intelligent traffic signal optimization system that:

  • Collects real-time traffic data from intersections
  • Uses AI/ML algorithms to predict congestion and adjust signal timings dynamically
  • Prioritizes emergency vehicles and reduces overall vehicle idle time
  • Offers a dashboard for traffic authorities to monitor and manage intersections

How we built it

  • Backend: Python + Flask for logic and API endpoints
  • ML Model: Trained on synthetic traffic data using decision trees to predict optimal signal timing
  • Frontend: React dashboard to visualize traffic stats and signal states
  • Data Simulation: Used NumPy to simulate traffic load per lane -Deployment: GitHub for version control, demo hosted locally

Challenges we ran into

  • Creating realistic traffic patterns without access to live data
  • Tuning the ML model for variable vehicle density
  • Syncing multiple intersections with shared traffic flow
  • Frontend-backend integration under time constraints

Accomplishments that we're proud of

-Built a fully working prototype within the hackathon window -Successfully demonstrated dynamic signal adjustments based on simulated data -Designed a clean, functional dashboard for traffic monitoring -Collaborated effectively and kept codebase clean and modular

What we learned

-Importance of feature engineering in time-series data -Real-time system design and latency handling -Working with simulated sensor data for smart city applications -Seamless teamwork using GitHub and Devpost practices

What's next for VelociCity-Optiroute

-Integrate real-time data from IoT sensors and traffic cams -Deploy in simulation environments like SUMO for urban testing -Expand to multi-intersection networks with adaptive coordination -Partner with local traffic departments for pilot deployment

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