This project was developed for the Gemini & Firebase Buildathon organized by TensorFlow User Group Ghaziabad.
It is an AI + Optimization powered intelligent decision-support system that assists railway section controllers in making optimized, real-time decisions for train precedence and crossings.
Indian Railways currently relies on manual decision-making by section controllers. With rising traffic, limited track capacity, and frequent disruptions (delays, breakdowns, weather), this manual system struggles to maintain punctuality, safety, and throughput.
Challenges:
- Manual precedence decisions under pressure
- Limited infrastructure shared by express, passenger, and freight trains
- Real-time disruptions affecting efficiency
- Lack of intelligent decision-support tools
An intelligent decision-support system that combines AI + Operations Research + Firebase Cloud tools to:
- Generate conflict-free, feasible train schedules
- Re-optimize in real time under delays or disruptions
- Provide What-If simulations powered by Gemini AI
- Deliver recommendations (Proceed / Hold / Reroute) via a dashboard
- Track performance KPIs like punctuality, average delay, and throughput
- Optimization Engine (Google OR-Tools) → Generates conflict-free train schedules.
- What-If Simulation (Gemini AI) → Natural language disruption analysis.
- User-Friendly Dashboard (React/Streamlit + Firebase Hosting) → Visual train timelines, controller recommendations, overrides.
- Realtime Data Sync (Firebase Firestore) → Instant updates across dashboards.
- Performance KPIs (Looker Studio) → Live dashboards for delay, throughput, and utilization.
- Gemini AI → Natural language simulation & explanations
- Firebase Firestore → Realtime database & audit logs
- Firebase Hosting → Dashboard deployment
- Firebase Auth → Secure user login
- Firebase Functions → API backend for optimization engine
- Google OR-Tools → Integer Linear Programming for scheduling
- Looker Studio → Live KPI dashboards
- React.js / Streamlit → Frontend interface
- Phase 1 (Hackathon): Section-level prototype with CSV inputs, OR-Tools optimization, Firebase dashboard
- Phase 2: Real-time delays + Gemini-powered What-If simulation
- Phase 3: Multi-section optimization + predictive analytics
- Phase 4: Nationwide integration with Indian Railways TMS
- Human-in-the-loop design: AI assists, controller decides
- AI-powered What-If analysis with Gemini
- Google ecosystem-only build: scalable, cloud-native
- KPI tracking + audit logs for accountability
- Economic: Reduced fuel use, lower operational costs, efficient freight
- Environmental: Lower emissions from reduced idle time
- Operational: Stress-free controllers, improved punctuality
- Scalable: Section-level prototype → national adoption
- Community: Reliable railways, improved public trust
- Google OR-Tools Documentation: https://developers.google.com/optimization
- Firebase Documentation: https://firebase.google.com/docs
- Gemini AI: https://ai.google.dev/
- Indian Railways Operations Research Studies
This project was created as part of the Gemini & Firebase Buildathon. For research and educational purposes only.