HomeARTIFICIAL INTELLIGENCEPhygital: Cognitive Technologies Drive Intelligent Airline Operations

Phygital: Cognitive Technologies Drive Intelligent Airline Operations

According to the Air Transport Action Group’s Waypoint 2050 report, global air travel will increase to nearly 10 billion passengers per year by 2050. While the forecast of nearly four billion travelers is currently hugely encouraging – significantly as travelers, tour operators, and airlines recover from the pandemic – the foreshadowing of likely airport congestion is worrying, even if it could result in delayed or canceled flights.

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The aviation industry should seize this opportunity to add digital technologies to physical facilities. In doing so, it is pursuing a sustainable growth course and cushioning the growing pains of the future.

Artificial intelligence (AI) and cognitive technologies provide tailwinds for flight operations and workflow management. They make it possible to create value from unstructured data. In addition, they recognize movement sequences, anomalies, and patterns in video images and enable autonomous functions. While natural language conversational support is a widespread AI use case in the travel and hospitality industry, it is also embracing technical and business processes.

For example, cameras equipped with computer vision, IoT sensors, biometric technology, and self-service applications provide rich visual, textual, and contextual data. These provide insights into passenger demographics, behavior, intentions, purchases, and operational activities. Airlines can now leverage cloud-hosted, AI-driven analytics solutions that leverage data for seamless convergence of physical and digital systems. Such an ecosystem can improve landside and airside operations, covering services across the entire value chain.

Automates Shoreside Operations

As of June 2018, IATA Resolution 753 requires each piece of baggage to be tracked at four critical points during the customer journey:

  1. Handover from passenger to airline.
  2. Loading onto the aircraft.
  3. Delivery at the transfer area.
  4. Return to the passenger.

This tracking data should also be shared with interline partners. AI-supported baggage handling systems automate these processes. The systems share the real-time status of the baggage with everyone involved, including passengers. In addition, intelligent cameras that work with the help of computer vision accurately identify unsafe baggage. This increases the overall efficiency of baggage screening.

A smooth flow of baggage and passengers is the yardstick for terminal operations. For example, face and iris recognition technology allow airlines and ground staff to use self-boarding gates. Thanks to biometrics, contactless identification of passengers at airport touchpoints is possible. At the same time, the security check can be automated. Immutable identity authentication speeds up passenger screening, passport verification, and immigrant processing. In 2018, Miami Airport introduced facial recognition screening for inbound travelers. It can screen ten passengers per minute and considerably relieves crowded arrival areas.

AI systems with visual sensors are the “eyes on the ground” – they monitor the overall situation at the airport – whether it’s passengers, employees, freight, or check-in halls. Intelligent monitoring from the departure hall to the aircraft also provides critical operational data. This includes, for example, the volume of arriving and departing travelers or the time spent at the control stations. The real-time data enables managers to make timely decisions to meet all needs. This includes managing passenger throughput and improving the experience for passengers, flight crew, and airport employees. This also enables airport operators to identify bottlenecks in the passenger flow and eliminate them as quickly as possible.

Tracking the volume and movements of travelers optimize queue management and increases resource productivity. However, AI-driven efficiency goes beyond smooth operation during peak travel times. Airlines using self-service solutions and automated kiosks to streamline passenger service and baggage handling can integrate the data with central service databases and airport management systems. This reduces overhead while streamlining arrival and departure operations. In addition, machine learning models and analytical solutions based on IoT sensor data and video recordings can predict peak frequencies and problems during specific periods. The result is improved self-service processes,

Optimized In-Flight Services

AI platforms also enhance the in-flight experience by mitigating technical and logistical issues that could interrupt or otherwise negatively impact the journey. Algorithms synthesize real-time data to precisely control the coordination of services such as in-flight catering, ground equipment handling, disabled passenger handling, water supply, and air conditioning. Sensor data with different parameters are processed via cloud portals. These include, for example, the air quality in the cabin or the food supply. This way, aircraft turnaround times can be shortened, and safety improved.

Analytical solutions correlate real-time data feeds with aircraft-specific, standardized metrics and historical data to identify problems and report anomalies. These include, but are not limited to, safety issues and delays in maintenance activities on the ground. In addition, AI systems augment technical support with recommendations that help maintenance and engineering teams troubleshoot and diagnose events faster. Incidents can be managed proactively, and emergency planning can be improved.

This includes, for example, unplanned maintenance work, which often leads to flight delays and cancellations. This increases general costs, including compensation for travelers. Therefore, airlines significantly use predictive maintenance applications to reduce equipment or aircraft failures. Real-time data from IoT-enabled aircraft and onboard condition monitoring sensors provide insight into the current technical condition, revealing malfunctions and alerting potential failures. Maintenance personnel and technicians on site can thus carry out physical inspections faster and more effectively – thanks to the sensor information; they know exactly where to look and repair. Delta Air Lines, for example, has implemented a predictive fleet maintenance program in partnership with Airbus.

Airline planning teams are responsible for the smooth running of thousands of domestic and international flights every day. It would be best if you considered independent, dependent, and mutually exclusive variables for routing and planning. For example, the experience of pilots and flight attendants could be linked to the respective flight route and aircraft type. For example, some airports in Central America require additional airport-specific qualifications so that the pilots can land there. All crews must also comply with complex labor agreements (unions) and government regulations, which vary from country to country.

This summer has been a significant challenge for airlines worldwide as they have to make do with limited staff. At the same time, they operate at major airports, where local staff is also severely limited. The result is costly flight disruptions, cancellations, lost baggage, and overbooking. The first airports, such as London Heathrow, are introducing capacity limits for travelers to remain operational in the tension between the travel boom after the pandemic and a lack of staff.

AI models could help here. They optimize crew and flight plan management by accounting for operational regulations, availability, maintenance schedules, and costs. Machine intelligence also addresses qualitative issues such as jet lag and crew fatigue. Intelligent models help to minimize health risks due to long-haul flights, time zone changes, or general overhauls and integrate the relevant data points into the duty roster system—another critical point: AI systems optimize fuel consumption in aviation for route planning. Maximum fuel efficiency is both an economic and an ethical imperative.

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