MetroFlow
Smarter,Stress-free Metro Travel with AI
The problem MetroFlow solves
Commuters often face uncertainty about metro rush levels, leading to long waits, discomfort, and wasted time. Current apps only show train timings, but not how crowded a train or station actually is.
Our solution provides:
Live Rush Data → Helps people decide the best time or station to travel.
Comfort & Safety → Reduces overcrowding, improves passenger experience, and promotes safer travel.
Time Management → Allows commuters to plan journeys efficiently, avoiding unnecessary delays.
Community-Driven Insights → Users can contribute real-time updates, making the system smarter with every trip.
In the long run, this can integrate with Smart City initiatives, helping authorities optimize metro operations and making public transport smarter and more reliable.
Challenges we ran into
While building the Metro Rush Detection & Comparison App, one of the biggest challenges we faced was handling real-time crowd data. Initially, our system either showed delays in live updates or sometimes displayed inconsistent rush levels across stations. This reduced the accuracy of predictions and created confusion.
We also struggled with a bug in integrating multiple data sources. The formats were mismatched, which caused the backend to crash whenever both inputs were processed simultaneously.
Technologies used