Researchers have measured the thinking time of London taxi driversβfamous for their knowledge of more than 26,000 streets across the cityβas part of a study into the future of AI route-mapping.
Unlike a satnav, which calculates every possible route until it gets to the destination, researchers at the University of York, in collaboration with University College London and the Champalimaud Foundation, found that London taxi drivers rationally plan each route by prioritizing the most challenging areas first and filling in the rest of the route around these tricky points.
The work is published in the journal Proceedings of the National Academy of Sciences.
Current computational models to understand human planning systems are challenging to apply to the real world or at large scale, and so researchers measured the thinking time of London taxi drivers while they planned travel journeys to various destinations in the capital city.
Previous studies have shown the uniqueness of the London taxi driver’s brain; they have a larger posterior hippocampus region than the average person, with their brain changing in volume as a result of their cab-driving experience.
Dr. Pablo Fernandez Velasco, British Academy Postdoctoral Fellow at the University of York, said, “London is incredibly complex, so planning a journey in a car ‘off the top of your head’ and at speed is a remarkable achievement.
“If taxi drivers were planning routes sequentially, as most people do, street-by-street, we would expect their response times to change significantly depending on how far they are along the route.
“Instead, they look at the entire network of streets, prioritizing the most important junctions on the route first, using theoretical metrics to determine what is important. This is a highly efficient way of planning, and it is the first time that we are able to study it in action.”
Researchers showed that taxi drivers use their cognitive resources in a much more efficient way than current technology, and argue that learning about expert human planners can help with AI development in a number of ways.
Dan McNamee from the Champalimaud Foundation said, “The development of future AI navigation technologies could benefit from the flexible planning strategies of humans, particularly when there are a lot of environmental features and dynamics that have to be taken into account.
“Another way to enhance these technologies would be to integrate the information about human experts into AI algorithms designed to collaborate with humans. This is a very important point, because if we want to optimize how an AI algorithm interacts with a human, the algorithm has to ‘know’ how the human thinks.”
Professor Hugo Spiers from University College London added, “This study certainly confirms what other studies have foundβthe London taxi driver’s brain is incredibly efficient and its larger volume is put to good use in making sense of a highly complex city like London.”
