TAPO for Airports – A Streaming usecase

Airports, especially the busy ones face an interesting challenge when it comes to serving the commuters, they need a smoother way to handle passengers in queues without long frustrating waits and thereby elevate the overall experience. No one likes to wait/stand in long queues. But airports, unfortunately, have lots of queues one for check-in, baggage drop, security, taxi, public transport and so on. The context of this post is only long wait times at taxi queues at the airports.

Generally, problems can be solved by engineering solutions or sometimes with psychological solutions, as to how Rory Sutherland in his TED talk ‘Perspective in everything’ explain eloquently, how countdown clocks in London tubes made waiting for trains less painful. (It’s a 20-minute video, but you could skip to 7:59 and watch through 8:30). But my proposal looks at the problem in 360 degrees. 

I termed my solution as TAPO (TAxi-Passenger demand Optimisation) for busy airports. The solution and approach outline how real-world events like passengers enqueuing/dequeuing for taxi’s at airport and taxi’s going in and out of airport can be leveraged to solve a real-world problem using Kafka streaming data platform as one of the key ingredients.

Here you go!