Per-MPPA Design Yardsticks
Historical precomputed ratios, with observation counts. Dates and comparability are unresolved. Sparse bands are suppressed; use Benchmark review for a traceable current study. These values do not size an airport.
Aeronova Airport Index
Historical values sorted for exploration. Dates and boundaries are not reconciled; rank does not establish performance. Stars are retained source-library markers, not a claim of current engagement.
Airport Deep-Dive Profile
Pick any airport from the dataset to see all 119 published technical and operational fields, organised into nine categories.
Side-by-Side Comparison
Select up to four airports. Click category headers to collapse/expand. Best-in-class flagged green, worst flagged red.
Peer Database
Filter by region, size class, regulatory regime. Click a row to open the deep-dive Profile. Click any header to sort.
DDFS Flight Intelligence & Sweat Load-Factor Planning
Uses the uploaded DDFS design-day schedule as flight-intelligence data. The model converts hourly movements, seat supply, aircraft code mix and load-factor stress into planning drivers for runway, apron, terminal processors, BHS and phasing.
Hourly Wave Signature
Heatmap intensity is based on movement saturation against declared runway ATM/hour. This identifies when the airfield is the binding constraint.
Passengers, Seats and Movements by Hour
Changing load factor increases passengers and processor demand while aircraft movements remain fixed. This is the core “sweat” logic.
Planning Intelligence Derived from DDFS
Use-Case Investigation & DDFS Data Mining
Turns DDFS flight-intelligence into reusable design rules for new projects. Select a reference operating pattern, set target MPPA, load factor and declared runway capacity, then read the implied runway, terminal, BHS, apron, landside and phasing drivers.
Pattern Fingerprint
Extracts the reusable DNA of the reference DDFS: gauge, heavy-body share, wave concentration, design-day peaking and runway pressure.
New Project Capacity Churn
Converts target MPPA into annual movements, design-day movements, peak-hour movements, PHP and 3-hour baggage/gate pressure.
Load-Factor Sweat Matrix
Shows how the same airfield schedule can be sweated by load factor: runway movements remain nearly fixed, but terminal, security, immigration, BHS, toilets, holdrooms and kerb demand rise linearly.
Use-Case Investigation Outputs for Design of New Projects
Distribution & Outliers
Reference cases highlighted in amber. Spot the outliers; calibrate the targets.
Methodology & Provenance — v0.2
v0.2 expands the v0.1 prototype from 14 to 119 fields per airport, with two new use-case views (Per-MPPA Yardsticks calculator + Profile deep-dive).
What's new in v0.2
- Per-MPPA Yardsticks tab — law-of-averages design ratios with interquartile bands, stratified by size class. Master planner enters target MPPA, sees implied provisioning across 22 design parameters.
- Profile tab — full 119-field deep-dive per airport, organised into 9 categories: capacity, navigation, terminal, BHS, landside, power/energy, water/waste, IT/security, innovation.
- Compare — extended from 14 to 60+ metrics with collapsible categories.
- Browse — clicking a row now opens the Profile.
Per-MPPA Yardsticks — what they're for
The yardsticks tab is the master planner's defensibility tool. Pick a target capacity and a peer class, and the app reads off the global median provisioning per MPPA with a 25th-75th percentile band.
For a 30 MPPA airport in the Large class, the median says you'll need about 375,000 m² terminal, 54 stands, 2 runways, 140 check-in counters, 8,800 parking spaces, 25 kWp/MPPA solar, and CAPEX of $330M.
The bands matter. A 30 MPPA airport at the 25th percentile is doing this with 290,000 m² and $240M CAPEX. At the 75th, it's 480,000 m² and $420M. The bands give a defensible window for AERA / lender / board scrutiny.
Public data sources
- ACI World — annual passenger traffic, ASQ rankings
- ACI ACA Registry — Carbon Accreditation levels
- Operator annual reports — Heathrow, Schiphol, Fraport, AdP, GMR, Adani, Zurich, BIAL, Changi, Dubai Airports
- AAI annual reports + AERA tariff orders — Indian airports
- Wikipedia — cross-referenced for capacity/runway data
- Master plan disclosures — DXN/NMI Phase-1, Ngungwi AeroCity
- Moodie Davitt — duty-free per-pax
- Anna.aero, CAPA, OAG — schedule, OTP, route data
Aeronova proprietary overlay
The wedge that makes Yardstick defensible vs. ACI World, Cirium, OAG, and ICF.
- Wealth Report — HNI density by city
- Logistics & Industrial — airport-adjacent rents (cargo benchmark feed)
- Hospitality — airport hotel ADR / occupancy
- Active Capital — FDI flows around airport hubs
- City Pulse — passenger demand trajectory
Field coverage caveats
- Capacity, runways, terminal, ASQ, ACA — >90% coverage.
- Commercial (non-aero/pax, capex/MPPA) — ~85%.
- Navigation, ATC, security — ~70%; some operators don't publish.
- Power, water, BHS — 30-50%; gaps remain on smaller airports.
- BESS, datacentre, kerb length, customs channels — 0% public; flagged as data gap, manual entry on Reference cases.
Production v1.0 will close these gaps via direct operator engagement on KF mandates and partnership data feeds.
Roadmap
- v0.2 (now) — 45 airports × 119 fields + DDFS flight intelligence, Per-MPPA Yardsticks tab, Profile tab
- v0.3 (Q3 2026) — 100 airports; close water/power/kerb data gaps via operator partnership; first Aeronova Airport Index publication
- v1.0 (Q1 2027) — 150 airports × 80+ metrics, live API integration into Aeroplanner Pro / Meridian / ARDI / RARE / PowerPlan Pro
- v2.0 (2028) — Subscription product to operators; cargo benchmark module