FDPIS: Flight Delay Propagation Intelligence System
Live demo
The API runs on a free instance that sleeps after 15 minutes idle. The first
visit wakes it, which takes about 45 s, and the site shows a "waking up" banner
meanwhile. The demo serves the held-out test split (2025-07-19 … 07-31).
Research prototype. US BTS on-time data, Jan/Apr/Jul 2025. Not an operational
system. Crew alerts use a hypothetical one-crew-per-aircraft model;
notifications are simulated.
What it does
One late aircraft is never one late flight. FDPIS works in layers:
- Data and rotation graph. Builds every aircraft's daily rotation from BTS
on-time records.
- Layer 3, primary risk. Ranks each departure by delay risk before the day
begins (XGBoost, 32 features).
- Layer 4, propagation. Pushes an observed delay down the rest of that
aircraft's rotation using a hurdle gate plus quantile models, with an interval
to depth 2.
- Layer 5, actions. Checks the projected chain against FAA Part 117 or DGCA
FDTL duty rules, kept as data with their regulatory sources, and raises
dated crew alerts with simulated notifications.
Results (held-out test split)
All figures are from AUDIT_REPORT.md, which also covers the
limitations.
| Layer | Metric | Value |
|---|
| 3 | ROC AUC / PR-AUC | 0.6924 / 0.3339 |
| 3 | Precision at top 1% (carrier-weighted) | 0.781 (0.696) |
| 4 | Gate AUC / cascade MAE | 0.9272 / 8.35 min |
| 5 | Actionable recall at 85% precision (LIKELY only, FAA) | 30% |
Repository
| Path | What |
|---|
fdpis_*.ipynb | Data preparation, modelling, propagation, diagnostics and weather studies (notebooks 1–8) |
api/ | FastAPI model server and the Layer 5 rule engine (api/layer5/, rules in api/layer5/rules/*.json) |
fdpis-web/ | Next.js 14 frontend |
deploy/ | Deployment: slim serving build, Dockerfiles, parity and verification tools (deploy/README.md) |
AUDIT_REPORT.md | Every reported number and how it was produced |
data/ and results/ are produced by the notebooks and are not committed.
The deployment's slim artifacts in deploy/backend/artifacts/ are committed.
Run locally
# API: full artifacts, after running the notebooks
uvicorn api.main:app --port 8000
# API: the deployed slim build (test split), from a clean clone, no notebooks needed
docker build -f deploy/backend/Dockerfile -t fdpis-api .
docker run --rm -p 8000:10000 fdpis-api
# Frontend
cd fdpis-web && npm ci && NEXT_PUBLIC_API_URL=http://localhost:8000 npm run dev
Team
Shrish Singh Sourya, Ashish Pathak, Ssunavya Jain. Guide: Prof. Anuradha G.,
VIT Vellore.
