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Automated Connecting-Flight Optimization
A tool that recalculates every connection possible through the hub - day by day, with passenger volumes - when a single flight's time is shifted.
The challenge
Airline schedules are built in two seasons, summer and winter, as a one-week plan repeated across the season. It looks like a fixed structure - but shift a single flight by a few hours and every connection that flight could catch at the hub changes with it.
Working out that effect by hand means re-scanning the arriving and departing flight lists for every day of the week. Slow, tedious and error-prone. A missed connection is a ticket that could have been sold; a connection assumed to exist is a passenger stranded at the airport.
What a planner needed was an answer to "what happens if I move this flight three hours earlier?" in minutes.
The approach
I built a Python tool that automatically computes the effect of a schedule change on the connection network.
- Input in the planner's own terms. The user enters the season, the flight to shift and the size of the shift: how many hours the arrival at the hub moves, how many hours the connecting departure moves. Not a query language - the operation's own vocabulary.
- Everything else held constant. The calculation changes only the two specified flights and leaves the rest of the network exactly as it is, so the difference that comes out is the consequence of one decision - a clean answer to "what if?"
- A valid connection is defined by rules. For a transfer to be genuinely possible, operational constraints must hold: minimum connection time, maximum wait, airport match. Those rules are applied separately for each of the seven days, because the weekly plan is not identical day to day.
- Output ready to use. Results come as two lists: flights arriving at the hub (inbound) and departing from it (outbound), each row carrying day of week, flight number, departure and arrival airport, and the market passenger count. The planner sees not only "is a connection possible" but "how many passengers does it affect".
What the tool really buys is cheap experimentation: when a check that took hours by hand becomes a single query, a planner can compare several options instead of verifying just one.
Stack: Python, pandas; rule-based connection computation over seasonal weekly schedule data, joined to market passenger volumes.
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