Predictive Analytics: Smart Airports Leveraging AI & Data Science to Deliver Great Flying Experience

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Airport delays impact passenger movements and their activities inside the airport. This affects both the aero and non-aero revenue for the airports. With less delays at various touchpoints, passengers are left with more time to spend at the terminal, investing more interest in shopping and other activities. Nonetheless, airports are able to deliver a great flying experience to the passengers they serve.

Predictive airport analytics takes passenger movement and terminal & gate operations data to the next level, by predicting congestion impact on the passenger activities. Staying ahead of such events means avoiding hassles for the flyers and keeping airport operations running smoothly.

Airports and airlines alike can benefit from more efficient passenger movements at the terminals and the gates. Just like the airports, airlines are able to provide a smooth boarding process to the flyers, leading to more happy customers.  From terminal & gate operations to the optimized queues, time and money can be saved by accurately predicting future operational conditions. When the airport management plans a full 12 hours ahead of the schedule using reliable predictive analytics, the entire operational behavior changes for the good with management’s preparedness.

Creating a better aircraft boarding process with queue-less experiences

One of the benefits of using artificial intelligence based predictive airport analytics is to see what is happening even before something has actually happened. AI based predictive analytics helps make sense of noisy, complex data generated in huge volumes at various touchpoints in the airport. With properly managed data and AI driven insights, airport managers have a better view of airport operations, including the aircraft boarding processes.  

An AI powered predictive analytics platform provides a dynamic comprehension of how many passengers are at the gates, average number of bags each passenger is carrying, average waiting time at security checks, average number of people in all the queues at any given time. In fact, with AI applications airport systems can even comprehend who’s at the gate and who’s not, the bags they have and the other people they’re traveling with, and even how these people physically

move. A smart predictive analytics platform brings all the disparate data sets together to answer questions that matters most to the airport management: How can the aircraft boarding process be made more better so that there are never any lines?

With predictive insights from the disparate data on the passenger movement around the gates, airport management can take the right decisions to architect just-intime and queue-less experiences for the fliers. In-fact with access to well-structured data about the flyers and their movements around the airport, AI based system can also co-ordinate more personalized information to passengers about zone based boarding routines of their flights, to free up the gates even further. This can further better the overall passenger experience.

With fewer crowded waiting rooms at the airport gates, passengers would spend more leisure time at airport restaurants and stores—resulting in better non-aero revenues for the airport.

Well linked Ground Transportation and the Air travel

A well-linked ground transportation and air travel serves as the right tool to leverage more leisure time at the terminals from the passengers. With a smooth transition time for the passengers, right from entering the terminals to the take-off, relieves the flyers of spending more time in the waiting areas or the queues. This has a cascading effect on the overall sales at the retail stores, restaurants, shops, etc, since the passengers now hardly worry about missed flights and have more spare time for shopping. This helps increase the non-aero revenue for the airport manifolds.

The link between ground transportation and air travel is somewhat not ideal for most of the airports. For instance, the traffic congestions at the arrivals and departures lanes of airports. Many airports are still trying to figure out how to manage the car parking, the taxi services, wait time management of the cab drivers. Predictive analytics suggests ways to replace all the lost revenue.

With predictive analytics, almost all the information about the passengers travelling at any given time through the airport is available to the management, like, number of passengers at the gates, average queue times during different times of the day. Predictive analytics helps the airport managers make sense of this information in a way that the operations are optimized for better results. With AI based smart intelligence, Predictive analytics platform suggests ways which can auger well for the overall passenger experience at the airports.

The primary goal of the airport management is to provide all the passengers a once in a lifetime flying experience every time they travel through the airport. Well-informed decisions based on smart predictive analytics helps provide such fulfilling flying experiences. However, what would be missing from these experiences? Delayed last bag times, car parking hassles, long queues at various checkpoints, Jammed curbs. In their place would be a far more robust connection between the airside and landslide that eliminates a currently disjointed relationship between aircraft and ground transportation.

Artificial Intelligence, Data Science & Machine learning to Build Smarter Airports

Airports around the world are increasingly implementing Artificial intelligence (AI), Machine learning and data analytics to offer personalized services and enhance the customer experience. With the help of technology airport managers are able to better optimize airport operations. With the advancement in these technologies with each passing day, in future we expect to see the end-to-end journey revolutionized by touch-free passenger journey at the airports -from entry in the airport to the actual take-off.

Features of AI based Predictive analytics:
  • Prediction

The prediction capability uses arrival and departure schedule data to predict the state of another variable in the future, for example, gate congestion in 6 hours’ time. This, along with other insightful predictions, are used to create a Digital Twin of the airport to better visualize all the aspects of airport operations. With prediction capabilities, the management is able to greatly improve airport operations as a result.

  • Estimation

Estimated quantifications of live data are produced using AI regression techniques. For example: the number of people at various airport locations at a given time, gender and destinations of a segment of passengers at the airport. This helps the airport management to suggest personalized offers and services based on the profile of each individual flyer.

  • Classification

AI based systems have the capability to classify objects based highly complex feature patterns. This technique can be applied at the security gates to detect any fraudulent activity. Objects like knives, guns, or any other such objects can easily be detected to avoid any untoward happenings. Besides this, the airports handle a number of physical documents which can be saved as online documents and AI be used to field tag in these freeform PDF documents.

With the increasing adoption of technologies like AI, machine learning and data science by the airports, not only the airports but also the airlines and passengers alike will greatly benefit. Early insights into key business drivers in the airport operations will improve pro-active communications, optimize ground operations and enhance the overall airport planning. A hassle-free flying experience for the passengers inadvertently means increased revenues for the airports and the airlines.

© Glorious Airport Analytics Research & Innovation Lab

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