AI in Aerospace Domain
Are we ready to take to the skies without a human pilot? With advances in AI and data science, it is now conceivable to travel in self-driving automobiles. It is still too early to say “yes” to flying without a human pilot, but it may be achievable in the future. Before air travel becomes completely pilotless, years of certification and testing will be required.
The good news is that airlines do use data science and machine learning to automate or speed up operations. Airlines can evaluate the passengers demand across different routes and use data insights to optimize aircraft ground handling and fuelling. They can also redefine passenger’s airport experience with biometric boarding with new distribution technologies. Airlines can also provide personalizing offers for individual travelers based on their preference and their willingness to pay. Use of data science in this industry has increased since 2019 and is expected to grow exponentially in the coming years.
Different use cases of AI in Aerospace domain
In-flight sales and food supply
Estimation of optimal food order demand based on flight time, day of flight, route, origin and destination, and other factors. If a person is flying at 6 a.m., he/she may order breakfast and a cup of coffee but during this time there is no or little chance to order aeroplane meals. The demand for food items on a 6 a.m. flight may differ significantly from that of a Friday night flight.
The airline supply management specialist must estimate how many snacks and drinks to bring onboard in order to drive food eaters without wasting food. Cbine waste is a major environmental concern. In 2018, airlines produced 6.1 million tonnes of waste, the majority of which was disposed of in a landfill. A data science algorithm for food demand prediction can help airlines save money while also doing the right thing for the environment.
Fuel consumption optimization
Commercial aviation contributed 2.4 percent of global co2 emissions from fossil fuel use in 2018. The percentage doesn’t seem significant but here is another fact, carbon emission increased by 32% over the past five years that’s why aircraft manufacturers and Airlines are looking for ways to improve their fuel efficiency. The second big reason to reduce the carbon emission is a financial one, 2018 airlines spent 23.5% of total expenses on jet fuel and that is huge.
To become more fuel efficient an airline must accurately predict how much fuel it needs for every scheduled flight. The best scenario is to have a single analytical tool. Using predictive models like time series algorithms and Neural networks, the system could produce forecasts for each month and each airport the carrier flies to, it can generate forecasts for the 12 months horizon and considers such influencing factors as fuel price, number of trips and a time period. The accuracy of the prediction is good compared to traditional analytical approaches.
Boarding and checking bags with facial recognition
Facial recognition technology is about analysing persons facial landmarks for given purposes the airlines use this biometric tech as a boarding option. Equipment scans the traveler faces and matches them with the photos stored in border control agency databases. Passports, visa, other travel documents. Travelers first scan themselves and then their passport at a self-service kiosk and then proceed to check-in their bags with the scanner too and go through another special scanner. It is stated by the government agencies such as US customs and board of projection that creating a seamless traveler experience fast and safe is important. Numerous airports are already using biometric gates in selected airports. Travelers seem to like the new boarding option.
To analyze the travelers overall experience and sentiments
Sentiment analysis is analysing an opinion or feelings about something using text data, regarding almost anything. Sentiment analysis always helps companies in their decision making process. In the current age, many people are joining social media platforms, websites like facebook and twitter. We can easily parse and use these tweets for public sentiment analysis.
In the aerospace industry, it would be helpful to use sentiment analysis to know the sentiment of people, customers towards travel experience and products. Things which can be done like by predicting whether a tweet contains positive, negative or neutral sentiment about the airline. To achieve this goal, a typical machine learning pipeline can be used. By importing the required dataset and then by performing exploratory data analysis any trends in the datasets can be observed. The next step is to perform text preprocessing to convert textual data to numeric data that can be used by a machine learning algorithm. The last step is to use machine learning algorithms to train and test our sentiment analysis models. The sentiment analysis is one of the most commonly used NLP tasks as it helps determine overall public opinion about a certain topic/product/service.