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AI Case Study

Dubai Airport improves customer waiting time and baggage tracking through out the airport using machine learning and visual recognition

Dubai airport, currently the busiest airport in the world, is using machine learning to analyse data collected from sensors as well as images to improve security queues. Currently 95% of the customers face queues of less than 5 minutes. They use visual feeds to identify bottle necks. The airport also uses AI to track luggage and send the information to customers.


Consumer Goods And Services

Travel And Leisure

Project Overview

"With 90 million travelers passing through it each year, Dubai Airports is the world’s busiest airport and passenger numbers are still growing. To deliver the additional capacity this requires, the airport looked to use its data to drive efficiencies.
The airport is supported by a huge network of connected sensors to monitor the various processes like queuing at security, where sensors track queue lengths and the time taken to process passengers, with solutions and bottlenecks spotted quickly, how long it will take to get luggage and send the information to customer's smartphone, how customers utilise the facilities throughout the airport for predictive maintenance and infrastructure re-design. "

Reported Results

According to the airport:

* 95% of passengers pass through security in five minutes or less
* Improved ability to predict baggage load and get all bags to the right destination



Customer Service



"Dubai Airport is currently the world’s busiest international airport, handling 90 million passengers this year. But with this figure expected to rise to around 100 million by 2020, and any possible physical expansion of the airport hindered by the limited geographical location, improvements had to come from innovation."



Data collected from connected sensors

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