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How does the Aviation Industry Use Data Science?
How does the Aviation Industry Use Data Science?

September 20, 2022

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In order to automate or speed up procedures, the aviation industry does use A.I.AI, or rather data science and machine learning. In this post, we'll examine the application of data science in the aviation industry using actual use cases.

Revenue Management And Route Planning

One of the key issues airlines must resolve to thrive is how to price flights and determine traveler demand for particular city pairs. Carriers must analyze data while considering thousands of parameters to do this. At the same time, analysts can still use conventional statistical methods.

 

Demand analysis can now be done in more complex ways thanks to data science. IATA advises that airlines can leverage traveler behavior data, search abandonment on online travel agencies, metasearch sites, or social media buzz to define leisure demand.

Recruitment and procurement data from professional networking sites may indicate new business trip locations. Skyscanner employed machine learning-based clustering to group roughly 50,000 origins and destinations by similarities in a 2017 presentation for airlines. About 30 factors were considered, including the month of travel, the time of reservation, the length of stay at the destination, and many more.

 

Revenue teams might use event data to increase fares for particular routes and dates in order to capitalize on increasing demand. Some events, such as festivals, conferences, or expos, cause short-term surges in demand. Predict H.Q.'s aviation rankings use ranking algorithms that compare past flight reservations with event data to show how much a certain event may impact traveler demand.

In-flight Sales And Food Supply

Since some passengers never order meals on the plane, supply management experts for the airline must make an educated guess as to how many snacks and drinks to bring on board to satisfy those who eat on the flight while minimizing waste. EasyJet CEO John Lundgren tasked the data science team to examine the market for food products.

The researchers discovered that a 6:00 a.m. flight to Edinburgh would have a different item demand than a Friday night trip to Ibiza, depending on the itinerary. As a result, the jet was discarding three items of fresh food each time it took off, totaling around 800,000 yearly. It cost the carrier millions of pounds, according to John Lundgren. The airline was able to make significant financial savings and contribute to the environment thanks to the innovative algorithm for demand prediction that data scientists eventually developed.

 

Fuel Consumption And Optimisation

Over the previous five years, carbon emissions rose by 32%. Airlines and aircraft manufacturers are therefore looking for ways to increase fuel economy. Jet fuel accounted for 23.5% of all airline costs in 2018. The ideal case scenario is to have a single analytical tool since, in order to become more fuel-efficient, an airline must precisely forecast how much gasoline it requires for each scheduled flight.

In their endeavor to reduce fuel use, Southwest Airlines worked on a similar resolution. The group created 8 predictive models that used time series techniques and neural networks to produce 9600 projections for monthly fuel usage. In order to make projections much more precise, it estimates for a 12-month horizon and considers affecting elements like gasoline price, the number of travels, and time.

Boarding And Checking Bags With Facial Recognition

As a boarding alternative, airlines use this biometric technology. The technology analyses facial scans of travelers and compares them to images from border control agency databases. These photographs may be taken from a passport, a visa, or other travel documentation.

In order to check in, passengers must first acquire access to themselves, their passports, and a scanner before checking in their backs. With the help of this technology, smoother, faster, and safer travel is possible. At the Atlanta Airport, Delta Airlines inaugurated a biometric terminal.

 

Travelers appear to prefer the new boarding option as Delta implemented face recognition in another Atlanta Airport terminal in the summer of 2019, as well as in Minneapolis, Detroit, and other airports that have 49 additional gates outfitted with facial recognition software. Customers at Atlantic Terminal, for example, responded favorably to the biometric boarding process 72% of the time, according to a poll conducted by Delta.

Preparing Plane For Next Flight

When an aircraft isn't prepared for boarding and takeoff, such as when a catering truck is running late, or the cleaning crew is preoccupied with another jet, we may have to wait at the gate to board. US passenger airlines suffered losses from delays in 2018 of $74.29 on average per minute. According to a US Department of Transportation calculation, plane servicing delays accounted for 5.8% of all flight delays.

 

Assata is a start-up that makes software that uses image recognition algorithms to interpret video broadcasts from airfields. The software's neural networks can detect the movements and interactions of objects. In order to determine whether or not they need to take action, airline staff can view in real-time how the plane is being prepared for the subsequent trip, including fueling, cargo loading, and catering delivery.

Lufthansa With its deep turnaround solution, Lufthansa Systems tackles the same issue. The aviation IT service company established its subsidiary zero-g to work on its AI ambitions. The technology also analyses video data and keeps people informed of what's happening during flight services in real-time airlines.

 

We might anticipate airlines to start customizing offers for specific travelers based on their preferences and willingness to pay as a result of the use of data science and machine learning to assess passenger demand across various routes, use data insights to optimize aircraft ground handling and fueling, or redefine passenger airport experience with biometric boarding.

 



 


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