Right now everyone is talking about AI, but when it comes to airports, do you know how to put it to good use? While some airports have started using AI to assist with self-checkout and customer service, many are still yet to realize that it has so much more to offer in the way of accurate, data-driven predictions and informing strategic decisions for improving operational efficiency, passenger experience and security. Here we will look at why investing in AI for airports, or should we say machine learning (more on that later) is a smart move for your business.
AI is often regarded as the answer to many airport industry problems. However, until recently its use within terminals has largely been limited to self-check-in, cleaning robots and customer service chatbots. Realizing the full potential of intelligent automation at the airport still appeared a little out of reach. So, what’s changed? We don’t imagine you’ll be particularly surprised to hear the answer is COVID-19.
Yes, thanks to the pandemic and the turbulent times it brought for the industry, there has never been a more important time for airports to be tech-smart and flexible to face the challenges ahead. Operational planning impacted by volatile flight schedules, fluidity in travel restrictions, not to mention the need for screening and social distancing, have all resulted in airports prioritizing digital innovation and putting AI at the very top of the list.
Essentially, the more operational data you can capture and analyze to make accurate predictions, the faster you can ‘get back to normal,’ restore passengers’ confidence and get them back in the airport spending money.
Thanks to the likes of Google and Amazon, the terms Artificial Intelligence (AI) and Machine Learning are much more widespread than ever before. However, although the terms are often used interchangeably, they’re not quite being the same thing.
AI is the simulation of human intelligence processes by machines. Whereas machine learning is a single component of AI, better described as ‘the science of getting computers to act without being explicitly programmed.’ Essentially, it focuses on the use of data and algorithms to imitate the way humans learn.
With machine learning, a computer system can take a range of data and utilize it. Its algorithms adapt to data, adjust to new conditions and are capable of developing behaviours and outcomes that were not programmed in advance. The more data is fed into a machine learning system, the more it can improve its accuracy.
If you’re new to the world of airport AI and this talk of machine learning is brain-boggling, it might be useful to share a very basic example. Let’s say you’re performing a Google search and you make a typo. You then spot your mistake and perform another Google search using the correct spelling. Google’s machine learning algorithm will see that you’ve searched for something else a couple of seconds later and realize what you were looking for the first time. It will then learn this correction, so that next time a user makes a similar typo it can ask, ‘did you mean…?’ to help them bring up the best search results.
Another real-world example of machine learning is facial recognition. An intelligent computer system can recognize an object as a digital image, and from a database of people, identify and match features to faces to reveal the person’s identity. This is used on social media platforms like Facebook, when it automatically suggests friends to tag in the photo you’ve uploaded, and it’s also used in law enforcement.
And of course, everyone has heard of ChatGPT and its abilities. From extrapolating content from brief prompts, to condensing data into simple snippets. By using the entire internet as its source, it can generate content like never seen before.
Better predictions across the terminal
Machine learning (ML) is a valuable tool for airport operations as it enables the gathering of data and automating predictions for various services. These include capacity and resource planning for passenger flow, predicting concession footfall, shuttle services, and estimating the time required for passenger processing. By analyzing factors such as queue lengths, productivity, and the number of open lanes, machine learning models make accurate predictions possible.
Additionally, by predicting peak footfall periods, airports can open new lanes, deploy more staff, and inform retailers to plan accordingly, optimizing queue management and checkpoint resources.
Accurately combining multiple data sources and automating the delivery of accurate forecasts without human intervention provides a significant advantage. This helps airports prevent problems that disrupt passenger flow, which can have negative consequences on revenue and passenger satisfaction.
Better operational performance
Airports face significant pressure to improve efficiency and reduce costs across their operations. Machine learning predictions can play a significant role in improving operational performance, enabling your airport to be proactive in its planning rather than reactive, leading to better operational performance. By anticipating peak and quiet times, airports can inform their staff and allocate resources to meet the level of demand and adjust their pricing strategies or offers accordingly.
For example, Rezcomm utilizes AI algorithms to predict the number of occupied parking spots at any given time. As a result, airports can optimize their parking inventory and pricing strategies, offering the right price at the right time to maximize the use of parking spaces and increase revenue. By using ML predictions to inform their pricing strategies and offers, airports can optimize their revenue and increase operational performance.
Better travel experience through data-driven certainty
Machine learning is also a powerful tool that can identify and solve passenger pain points, creating a more seamless and efficient experience for passengers. By improving the passenger experience with less waiting, a smoother journey through the airport, and easier navigation, passengers are more likely to return, increasing the airport’s lifetime value. Additionally, satisfied passengers are more likely to recommend the airport to others through word of mouth, leading to increased brand recognition and reputation.
The use of machine learning can also create more time for passengers to spend in stores, restaurants, and duty-free shops, resulting in increased revenue. Passengers with extra time before their flights can spend it shopping and dining. This not only increases the airport’s revenue but also creates a more enjoyable experience for passengers, contributing to their overall satisfaction.
Rezcomm’s Business modules provide airports with a convenient and scalable solution to incorporate AI technology into your operations, helping you future-proof your business. We offer quick and easy implementation, usually launching within 2 weeks and expert 24/7 support.
Make better business decisions, stay competitive and increase revenue, ensuring a brighter future for your airport and your passengers.