Forecast airport demand early enough to position the right driver supply

Design decisions that matter

Predicting actual wait time aligns closely with the driver's question, but depends on a trustworthy label that may become noisy during rematch-heavy periods. Streaming features improve responsiveness, yet late flight updates and quiet-period sparsity can make them less reliable than historical aggregates. Smoothing reduces spikes but can erase the start of a real surge. A five-minute forecast may be accurate but too late for repositioning; an hour-ahead forecast gives time to act but carries wider uncertainty. Point estimates are simple to display, while distributions represent risk more honestly and require a clear policy for when uncertainty is too high. The final action threshold must balance rider reliability against driver congestion, because the lowest forecast error does not automatically produce the healthiest marketplace.

Where a project like this can fail

1. 01Future information leaks into trainingA finalized arrival time or completed queue count may not have been available when the forecast was served, making offline accuracy look unrealistically strong.
2. 02Queue labels mix different journeysRematches, priority access, abandonment and ordinary FIFO waits can produce similar records with different meanings. Those paths need segmentation or filtering.
3. 03Guidance creates a self-defeating surgeA shortage forecast can attract enough drivers to create oversupply. Rollouts need behavioral measurement, holdouts and a way to throttle guidance.