Flight Delay Operations Intelligence

A reproducible aviation analytics case study that identifies operational delay patterns, route risk, carrier volatility, and actionable mitigation priorities using U.S. 2024 flight data.

Why this project matters

Flight delays are not just passenger inconvenience metrics. They are operational signals tied to route fragility, carrier performance variability, airport congestion, and downstream disruption risk.

This project turns raw delay data into decision-oriented insights for operations, performance, and planning teams.

Key questions

• Which months show the highest operational delay burden?
• Which origins and carriers underperform consistently?
• Which routes show elevated disruption risk?
• What proportion of delay burden comes from each delay reason?
• Which interventions would likely reduce late departures most effectively?

Tools used

• Python
• pandas
• matplotlib
• PowerShell
• CSV-based reproducible pipeline

Run locally

Run the full pipeline:

powershell -ExecutionPolicy Bypass -File .\run.ps1

Fast mode:

powershell -ExecutionPolicy Bypass -File .\run.ps1 -SkipSampleGen

What the pipeline does

The pipeline will:

• Create .venv if missing
• Install requirements.txt
• Generate data/sample_multi_month.csv if the source dataset exists
• Run:
  • analysis_eda.py
  • analysis_ops.py
  • main.py
• Write outputs into the outputs/ folder

Preview

Average delay by month

Late rate by month

Key outputs

Report

• REPORT.md — executive summary and operational recommendations

Tables

• outputs/by_month_metrics.csv
• outputs/worst_origins.csv
• outputs/worst_carriers.csv
• outputs/top_risky_routes.csv
• outputs/delay_reason_share.csv

Charts

• outputs/avg_delay_by_month.png
• outputs/late_rate_by_month.png
• outputs/dep_delay_hist.png
• outputs/avg_delay_by_dayofweek.png

Key findings

• Delay burden is not distributed evenly across the year.
• A limited number of origin airports account for a disproportionate share of operational underperformance.
• Some carriers show persistent delay volatility, suggesting structural operational pressure rather than isolated issues.
• A subset of routes repeatedly appears in the risk profile, indicating corridor-level fragility.
• Delay reason mix suggests that intervention priorities should vary by segment instead of using a single blanket solution.

Operational recommendations

• Prioritize review of repeatedly underperforming origin stations.
• Flag high-risk routes for schedule buffer and turnaround review.
• Separate carrier-wide issues from airport-specific congestion before intervention.
• Track monthly delay burden as an early-warning operational metric.
• Monitor delay-reason share over time to distinguish tactical vs structural disruption drivers.

Notes

If data/sample_multi_month.csv is missing and the large source dataset is not present under ops-bigdata/, the pipeline will fail with a clear error.

Recommended entry point for reviewers: REPORT.md.