Aviation Boarding Analytics

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1 · Boarding duration

Time from first passenger entering to last passenger seated.

2 · Boarding complete vs scheduled departure

Minutes between boarding completion and scheduled departure.

3 · First-pax timing → departure

At the moment the first passenger boards (lead time before scheduled departure), can we predict the buffer/delay at departure?

4 · Flow cadence

Average passenger flow (pax/min) over the course of boarding — good vs slow flow.

5 · Boarding time by aircraft / route / load

How boarding duration varies by aircraft type, route and load factor.

Switch series:

6 · Speed by airport & gate

Boarding duration by origin airport and gate. Handler / boarding-process not in source data.

7 · Congestion vs hand-luggage

Peak occupancy vs bags-per-passenger (a proxy — true luggage fill isn't measured).

8 · Time to first congestion

Seconds from boarding start until the first visible flow slowdown.

9 · Average flow rate

Distribution of average passenger flow per minute across boardings.

10 · Fastest vs slowest cabin flow

Average cadence of the fastest vs slowest boardings — what a good pattern looks like.

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1 · Payload weight per passenger

From ACARS loadsheets: total payload ÷ passengers vs a standard all-in weight — flags flights carrying more/less than expected. (Airframes, sparse / ZRH-live only.)

2 · Cabin baggage volume → boarding duration

Bags-per-passenger vs boarding duration. (Needs VionAI bag detection.)

3 · Fill level → boarding speed

Average flow slowdown by bag-fill bucket — where does boarding speed begin to decline? (Needs VionAI bag detection.)

7 · Cabin baggage pressure

Average bags-per-passenger by route / airport / passenger profile. (Needs VionAI bag detection.)

Group by:

4 · Overhead-bin capacity constraint

Predicting when bins fill needs a per-aircraft overhead-bin capacity reference.

⛔ No data source — needs an aircraft bin-capacity dataset (+ bag timeline).

5 · Cabin bags vs gate-checked at door

Splitting carried-on vs gate-checked bags needs a gate-checked-bag feed.

⛔ No data source — gate-checked bag counts are not captured anywhere.

6 · Time lost removing bags

Detecting bag-removal events needs a VionAI "bag removal" event type.

⛔ No data source — not in the VionAI event model.

8 · Optimal point to stop accepting bags

Combines bin-capacity (Q4) with the fill-vs-speed curve (Q3).

⛔ Blocked on Q4 (bin capacity) — buildable once that data exists.

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1 · Airport boarding performance

Median boarding duration by departure airport — which airports are consistently faster/slower. Bars show median; note shows p10–p90 spread (variability). Cabin-readiness signal isn't captured.

4 · What drives late boarding completion?

Correlation of each factor with the schedule buffer (boarding-complete → scheduled departure). Negative = pushes completion later. Aircraft-readiness has no signal; actual delay isn't available.

5 · Boarding SLA by airport

Share of boardings completing before scheduled departure, by airport — an objective boarding-readiness SLA. (Cleaning / aircraft-readiness SLAs need data we don't have.)

2 · Performance by terminal proxy

Median boarding by departure terminal (Airlabs). A coarse stand-in — true gate/stand isn't in the camera data (gate is "not implemented").

3 · By ground handler / cleaner / team

Comparing handlers, cleaning providers or airport teams needs their identity per turn.

⛔ No data source — handler/cleaner/team identity isn't in VionAI, Airlabs or Airframes.

6 · Provider delay codes vs camera evidence

Cross-checking reported IATA delay codes against camera-derived evidence needs the SWISS delay-code feed.

⛔ No data source — conditional on a SWISS delay-code feed ("if we can get data from SWISS").