U.S. Airport Operational Performance Dashboard

Hub Airport Analysis  ·  Calendar Year 2024  ·  140 Airports  ·  7.08M Flight Records

BTS T_ONTIME_REPORTING FAA ACAIS CY2024 Airport IQ 5010 31 Large Hubs 32 Medium Hubs 77 Small Hubs Departures ≥15 min late Arrivals ≥15 min late
CY24 Hub Enplanements
0.95B
Across all 140 hub airports
Avg Departure Delay Rate
20.8%
Flights ≥15 min late departing
Avg Arrival Delay Rate
20.9%
Flights ≥15 min late arriving
Controllable Delay Share
75.1%
Carrier + late aircraft causes
Volume vs. Departure Delay Rate
CY2024 annual enplanements vs. departure delay rate (≥15 min)  ·  Quadrant lines at national average & median enplanements
Key Findings
National Baseline

In CY2024, hub airports averaged a 20.8% departure delay rate (20.9% for arrivals). Out of 6,653,159 reportable departures, 1,385,127 left ≥15 minutes late—roughly 1 in every 5 flights.

Volume Is Not Destiny

The scatter plot shows no strong linear relationship between airport size and delay rate. Several large hubs maintain below-average delay rates despite processing tens of millions of passengers, while some smaller hubs exceed 25%. Operational discipline matters more than volume alone.

Controllable Causes Dominate

Of all delay minutes at hub airports, 75.1% trace to controllable causes—airline-driven carrier delays and late-arriving aircraft cascading through the network. Weather accounts for just 5.6%, while NAS/ATC constraints represent 19.2%.

Best Performers

Island and West Coast airports lead on punctuality. Pacific island airports (GUM 8.3%, ITO 10.3%, KOA 11.2%) post the lowest raw delay rates, driven by limited connecting traffic and favorable conditions — GUM carries only 726 BTS-reported departures and should be interpreted cautiously. Among well-trafficked airports, HNL, PDX, SJC, and IAD combine strong on-time performance with high volume.

Departure vs. Arrival Gaps

Airports where the departure rate exceeds the arrival rate are net delay generators—they send proportionally more late flights than they receive. The inverse often reflects connecting-flight propagation from elsewhere in the network. The two measures capture different flight populations and cannot be used to infer in-flight schedule recovery.

Delay Cause Breakdown — Top 20 Airports by Delayed Flights
Share of total delay minutes by cause category (BTS reporting carriers, CY2024, ≥15 min delays)
Carrier

Delays caused by circumstances within the airline’s control — maintenance, crew availability, aircraft cleaning, baggage loading, or fueling.

Late Aircraft

The inbound aircraft arrived late from a prior flight, leaving insufficient turnaround time. Delays cascade through the network as the same plane serves multiple legs.

NAS

National Airspace System delays — non-extreme weather, heavy traffic volume, air traffic control, or temporary flight restrictions impacting the broader airspace.

Weather

Significant meteorological conditions — storms, fog, ice, or wind — that directly prevent safe departure or arrival, as determined by the carrier or ATC.

Security

Delays caused by TSA screening, terminal evacuations, re-boarding due to security breaches, or other security-related holds at the gate or checkpoint.

All Hub Airports — Full Comparison

CY2024 operational metrics for all 140 hub airports. Click any column header to sort.

Code Airport Hub State CY24 Enplanements Dep Flights Arr Flights Dep Delay % Arr Delay % Dep–Arr Gap Controllable % ℹ

★ GPI (Glacier Park Intl), HVN (Tweed-New Haven), and IWA (Phoenix–Mesa Gateway) are not served by BTS-reporting carriers.  ⚠ GUM (Guam Intl) has only 726 BTS-reported departures for CY2024 (≈2/day from reporting carriers); interpret its metrics with caution. Dep–Arr Gap: positive = more delays departing than arriving (net delay generator); negative = more delays arriving than departing. Controllable % based on departure delay minutes only.

How This Was Built
Data Pipeline

A Python script orchestrates the entire build. It reads 24 monthly CSV files from the Bureau of Transportation Statistics (12 departure + 12 arrival, one per month for CY2024), aggregates them by airport code, and computes all metrics from scratch on each run. The FAA enplanement Excel workbook and Airport IQ 5010 spreadsheet are loaded via openpyxl. All three sources are joined in memory using the FAA Loc_Id (IATA airport code) as the common key. The script then renders the complete HTML file using string templates and json.dumps() to embed live data arrays directly into the JavaScript.

Scale & Coverage
  • 7,079,081 individual flight records processed
  • 24 CSV source files (Jan–Dec 2024, dep + arr)
  • 3 data sources joined: BTS, FAA ACAIS, Airport IQ 5010
  • 140 hub airports (31 Large, 32 Medium, 77 Small)
  • 137 airports with BTS reporting carrier data
  • 7 metrics computed per airport: dep rate, arr rate, dep–arr gap, 5-category cause breakdown, controllable %
  • Output: single self-contained HTML file, ~110 KB, no server required
Front-End Stack

Built entirely in vanilla HTML, CSS, and JavaScript — no front-end framework. All interactivity (sort, filter, search, chart toggles) is hand-written JS. Two visualization libraries loaded from CDN:

  • Plotly.js — interactive scatter plot with hover, log-scale axis, and quadrant annotations
  • Chart.js — horizontal stacked bar chart with departure/arrival toggle

The comparison table supports bi-directional sort on all 11 columns, hub-tier filtering (All / Large / Medium / Small), and live text search across airport code, name, city, and state.

How This Was Built
API Orchestration & Data Engineering

Orchestrated a 7M+ record BTS flight dataset pipeline, normalizing operational performance metrics across 140 hub airports into a sub-second query interface. Data architecture applies the same hub-classification logic used by FAA and DOT for national operational oversight — large, medium, and small hub tiers with deterministic performance thresholds. All computation runs client-side via an orchestrated JavaScript logic layer — no server latency, no cold-start delays.

National-Scale Analytical Framework

The performance benchmarks and delay-cause attribution architecture mirror the analytical frameworks used to monitor operational compliance across national aviation infrastructure. Departure rate, arrival rate, delay gap, and controllable-cause breakdowns are calculated with the same precision required to inform federal-level operational decisions — not just to visualize them. Engineered for scale, not for demos.

Cloud-Native Deployment Stack

Production-grade static deployment via Netlify global CDN — zero server dependencies, instant load performance. Chart.js orchestrates multi-series delay-cause visualization; D3-style table logic handles bi-directional sort, tier filtering, and live search across 140 airports. Built through an agentic AI workflow — the same rapid implementation model delivered to clients.

Governance: structured output validation enforces deterministic display logic — null-state handling, rate thresholds, and hub-tier classification are explicit constraints, not inferred defaults.

BTS Transtats · 140 Hub Airports · 7M+ Flight Records · Chart.js · Vanilla JS · Netlify Global CDN · Agentic AI Workflow