One option is that you build up a hierarchical model, with indexed airport, day and time. You can find the classic PyMC example for hierarchical models here: A Primer on Bayesian Methods for Multilevel Modeling — PyMC example gallery .
Another option is that you use a time-series model. That’s more complex, but probably more appropriate for the type of data you’re working with. There are plenty of resources on time-series in PyMC that you can find on google or youtube. You can find a very good intro here: 6. Time Series — Bayesian Modeling and Computation in Python .
Gaussian random walks and Gaussian processes (GP) are possible alternatives for time-series. Here’s an example for a GP regression: Gaussian Process Regression — PyMC3 3.1rc3 documentation .
If interested in going deeper into these problems, pymc-experimental recently added state-space models: `pymc-experimental` now includes state spaces models! .
Maybe this is too much to take in at once. But it may be useful to have access to several resources in one place, and maybe you can check them up incrementally. Many (as I did) starter with the hierarchical model example and very slowly explored more complex/specific models. May be worth considering starting with a simpler example as well (you’ll probably need different parametrisations for waiting time data, e.g. a Gamma likelihood, etc.). I hope this helps.