Air travel is stressful. Long lines at security, cramped seats in coach, lost baggage, and tight connecting flights鈥攖here is no shortage of pain points in any journey. Machine learning can鈥檛 give you more leg room, but it is poised to revolutionize your travel experience.
In her recent collaborative work with Heathrow Airport and researchers at University College London, Yael Grushka-Cockayne, visiting associate professor at 性视界 Business School, demonstrated how machine learning can successfully intervene to address one of these pain points鈥攖ight connecting flights.
As Grushka-Cockayne explains, there was enthusiasm among key stakeholders at Heathrow to upgrade existing data systems at the airport, and a consensus about the opportunity to better leverage data to improve the experience of connecting passengers鈥攚ho account for roughly one-third of all travelers who pass through Heathrow annually. The question was how to do this. 鈥淧eople want to use machine learning and big data鈥攁ll of these buzz words,鈥� says Grushka-Cockayne, 鈥渂ut if they don鈥檛 know how to focus in on a very specific task that can generate predictions, it is difficult to use the technology to actually improve decision-making.鈥�
Grushka-Cockayne and her team spent several months working with partners at Heathrow to define the scope of their research鈥攖he development of a machine learning model that could predict a passenger鈥檚 journey through Heathrow in route to his or her connecting flight. The goal was to be able to anticipate the number of people passing through immigration in real time (enabling more efficient staff allocation at immigration lines), and also to predict whether a passenger would be late for his or her flight (allowing the airport to proactively offer supporting services).
But it wasn鈥檛 easy to capture the complexity of a passenger鈥檚 journey through an airport in a statistical model. 鈥淢ost of the data was there,鈥� says Grushka-Cockayne, 鈥渂ut there were a lot of very old, fragmented systems鈥� that weren鈥檛 communicating effectively with each other. To build a model that could both identify connecting passengers and accurately predict their transfer time, it was essential to collate multiple levels of data: from the airport, from airlines, from passengers, and鈥攎ost importantly鈥攆rom baggage tags. As she explains, 鈥渢he baggage database ended up being really useful, because bags are tagged with barcodes to the final destination. You know who started out where and where they鈥檙e going to end up.鈥�
Once Grushka-Cockayne and her team had identified the data set, the final task was to select an analytically rigorous computing system that was still accessible to airport control staff. As she explains, 鈥渨e tried to find a balance鈥攁 tool that could generate better, more consistent predictions, but that was not overly complex,鈥� to enable seamless implementation.
Two years after a live trial in 2017, the results are impressive. The mean absolute error of passenger flow predictions has dropped over twenty percent after the adoption of the new model. But perhaps more tellingly, Heathrow stakeholders have since gone above and beyond the initial scope of the model to adapt the predictive technology to enhance other airport processes. 鈥淚 was shocked, I was speechless,鈥� says Grushka-Cockayne, 鈥渂y how rigorously they pursued the implementation of the model. I think it is indicative of the fact that the entire attitude towards data science and machine learning applications has changed.鈥� And this enthusiasm is not limited to Heathrow. Since the publication of their research, Grushka-Cockayne and her team have received requests from airport representatives worldwide鈥攆rom Paris, Singapore, Los Angeles, and more鈥攍ooking to implement similar systems at their own facilities.
This kind of model is just the beginning. The sheer amount of data being collected within the scope of air travel means that there are countless applications at the intersection of machine learning and travel. 鈥淭he notion of connecting through airports will become better,鈥� says Grushka-Cockayne. Part of the reason will be because systems improve鈥攁lready, fewer people are missing flights and less baggage is getting lost. But the other equally important piece of mitigating travel-related stress is simply having more transparency surrounding the entire process. 鈥淒ata has a lot to do with that,鈥� she says. 鈥淲hen you miss a flight but know that you鈥檙e already rebooked, or if you can see where exactly your bag got lost along the way, it helps to remove some of the stress. Data can give us a lot of comfort these days. So the next step is just to use the data more effectively to prevent these kinds of things from happening.鈥�