UrbanCycle

A smart, community-driven app for urban cyclists. Report hazards in real time, get instant safety alerts, and receive AI-enhanced route suggestions for a safer, stress-free ride in the city.

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Inspiration

-As avid bike riders who have experienced firsthand the dangers of unexpected road hazards and inadequate cycling apps, we wanted to build a platform that prioritizes cyclist safety and community feedback. UrbanCycle was born out of our desire to make city biking safer and more enjoyable for everyone.

What it does

  • UrbanCycle is a smart, community-driven app designed for urban cyclists. It allows users to:
  • Report road hazards in real time using a simple button.
  • Receive instant safety alerts based on reports from other cyclists.
  • Access AI-enhanced route suggestions for safer navigation.
  • Coordinate group rides and track real-time safety scores for routes.

How we built it

  • Frontend: Built using React Native for a cross-platform mobile experience.
  • Backend: Developed with Flask/Django, handling data processing and API calls.
  • Mapping: Integrated Leaflet.js for dynamic map visualizations.
  • AI Model: Leveraged Python and Qiskit for machine learning-based route optimization and hazard prediction.
  • Firebase: Used for real-time hazard updates and user data management.

Challenges we ran into

-Setting up a seamless real-time data pipeline with Firebase and handling async updates. - Integrating machine learning for route suggestions with limited training time. - Managing issues with iOS simulator setup and dependencies in React Native. - Navigating unexpected roadblocks in the Podfile configurations.

Accomplishments that we're proud of

  • Successfully built and launched a functional MVP in just 24 hours.
    • Integrated AI-based route optimization using Qiskit despite the time constraints.
    • Created a seamless user interface with real-time hazard reporting and updates. ## What we learned
  • Gained experience in real-time data handling using Firebase and React Native.
    • Improved our skills in integrating machine learning with mobile apps.
    • Learned valuable lessons about team coordination and efficient problem-solving in a fast-paced environment.

What's next for UrbanCycle

  • Implement voice-activated hazard reporting for hands-free use.
    • Enhance AI models with more training data for better route suggestions.
    • Expand community features for group ride planning and safety scores.
    • Launch a public beta and gather user feedback to refine the app.

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