Today, AI and Machine Learning are Implementing in every Industry and one of them is Aviation Industry. AI and Machine Learning work as a backbone in Aviation Industry. Artificial Intelligence is more and more rolling out across the aviation industry. The way businesses approach their data, operations, and revenue stream is disrupted by AI in the aviation industry. Artificial intelligence is already being used by the world's leading airlines to improve operational efficiency, avoid costly errors, and increase customer satisfaction. There are many different areas where the aviation industry can be empowered by machine learning.
The aviation industry leverages Artificial Intelligence with machine
learning, computer vision, robotics, and NLP. In order to maximize overall
customer experience, primary advantages include Predictive Maintenance, Pattern
Detection, Auto-Scheduling, Targeted Ads, and Customer Feedback analysis.
A recent study shows that aviation professionals are considering using
artificial intelligence to monitor pilot voices for passengers to have a
hassle-free flying experience. This technology is intended to bring about
tremendous changes in the aviation world.
Airlines are looking at how technology can help minimize the impact on
the experience of passengers and their company of disruption. Over the next
three years, 80% of them plan to invest in major programs or R&D in
prediction and warning systems that rely heavily on AI, according to the SITA
report.
They came together in October 2017 to launch the New Experience in
Travel and Technology (NEXTT) initiative to maximize the use of digital
technologies in the face of increasing numbers of passengers. AI is highlighted
as a priority, especially in relation to its ability to enhance decision-making
in real-time and, thus, effectiveness.
It is now expected that AI in the aviation market will grow from $152.4
million in 2018 to $2.2 billion by 2025.
So here we discuss how Artificial Intelligence and machine learning uses in Airlines:
Identifying Passenger information while checking-In
Security is a primary concern for airports and, therefore, it is imperative that the authorities properly check the documents and identify the passengers who are travelling. AI-enabled facial recognition systems and software can help airport authorities recognise passengers by using the data and comparing it with their passport pictures. For example, Delta Airlines, one of the American airlines, has installed cameras and deployed facial recognition technology to identify their passengers while checking in.
In addition, in their security scanners, airport authorities may also
use advanced technologies to identify possible threats at large and popular
airports in the world. In their mobile applications, several airlines have also
implemented this technology and streamlined the entire boarding process to
provide their clients with a better travel experience in the middle of their
crisis. Technology like artificial intelligence and machine learning will also
aid in speeding up the process of attending customers, which in turn would help
the officials in a longer run.
Baggage Screening
It is also imperative for airport authorities, in addition to identifying travellers and checking their documents, to review and screen the luggage of travellers in order to detect any potential threats. The luggage screening process could be tedious using traditional methods. Security officials can, however, quickly identify dangerous and illegal items in the luggage of travellers in a much simpler way with AI-based systems. These systems assist in automated screening, which through X-rays and computed tomography can detect potential threats in the baggage.
Japan's Osaka Airport plans to install Syntech ONE 200, an AI platform
for baggage screening with multiple conveyor belts. Automated baggage scanning
can help security officers identify suspicious objects easily and efficiently.
The compatibility of the Syntech One 200 with the X-ray protection device
increases its likelihood of detecting possible hazards.
Assisting Customers
AI can be used to help customers at the airport and, at the same time, it can help a business reduce its operating costs and labour costs. Airlines companies are now using AI technologies to help their customers quickly solve problems by obtaining precise information on future flight journeys on their internet-enabled devices. In the next five years, more than 52 percent of airline companies worldwide are planning to install AI-based tools to enhance their customer service functions.
Artificial Intelligence may address numerous common customer queries,
helping them with check-in requests, flight status, and more. Nowadays
artificial intelligence is also used in air cargo for different purposes such
as revenue management, protection, and maintenance and it has shown amazing
results till date.
Air Traffic Control
Changing the weather and the height of the control tower, which can cause air traffic delays, is one of the main challenges at the airport. Air traffic control (ATC) must rely on radars during bad weather to keep the airport operating smoothly. Some airports have installed ultra-high definition cameras with AI technology on top of the towers in an attempt to solve this issue. To provide direct views of the airport to traffic controllers, AI can be used. Searidge Technologies' AI systems, such as AIMEE, use machine learning to interpret photos, record aircraft and alert controllers so that they can signal the next aircraft to arrive on the cleared runway.
Ticket Prices and Crew Management
AI algorithms could also assist airlines to optimise ticket prices based on different factors, such as seasonality, fuel prices, rivalry, etc. Faculty, a British company specialising in AI solutions, has developed an AI model that was able to provide predictions that were accurate between 70% and 80% up to 90 days before each flight.
Then crew management is there. All aspects need to be taken into
account, such as certification, availability and qualification of pilots,
flight attendants and engineers. It will improve HR productivity to schedule
and re-schedule workers using an AI-based roster system and ideally optimise
crew layovers.
Fuel Consumption Optimization
Airlines use AI systems to collect and analyse flight data on flight distance, altitudes, actual passenger count, aircraft weight, weather, and so on to reduce the environmental effects of aircraft and reduce flight costs. Neural network models, for example, can be used to predict the fuel usage of an aircraft. Systems will apply it after pre-processing the data and training the model and then estimate the amount of fuel that is required for one flight. This helps eliminate fuel waste and decrease the excessive weight and fuel consumption of aircraft.
Maintenance Predictions
In order to enhance the efficiency of their aircraft maintenance operation, Airbus, the leading aircraft manufacturer, is introducing AI applications. A cloud-based programme, Skywise, assists in the efficient storing of data. A huge amount of data is collected and registered in real-time by the fleet, processed and stored in the Cloud server. For the airline business to determine an appropriate method for aircraft maintenance, Predictive Analytics and AI establish a systematic solution.
Route Planning
Carriers need to consider hundreds of factors when deciding route and frequency demand for specific city pairs, particularly with the rise in point-to-point travel. Demographics, industry links, week and day time, season, holidays, activities, fuel prices, etc., all determine whether or not a route will be lucrative and when. ML can manage far more data than traditional analytical methods in order to decide optimum routes and schedules. To assess both leisure and business travel demands, it can analyse search engine data, booking agent data, social media posts and comments, along with recruitment and technical pages.
Conclusion
For airlines and airport authorities around the globe, the use of artificial intelligence in aviation has made many tasks simple. From the identification of passengers to bag screening and the provision of quick and reliable solutions for customer service.
Also Read: Example of How Machine Learning is changing modern advertising industry
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