The AI boss that deploys Hong Kong’s subway engineers
An algorithm schedules and manages the nightly engineering work on one of the world’s best subway systems – and does it more efficiently than any human could.
JUST after midnight, the last subway car slips into its sidings in Hong Kong and an army of engineers goes to work. In a typical week, 10,000 people carry out 2600 engineering works across the system – from grinding rough rails smooth and replacing tracks to checking for damage. People might do the work, but they don’t choose what needs doing. Instead, each task is scheduled and managed by artificial intelligence.
Hong Kong has one of the world’s best subway systems. It has a 99.9 per cent on time record – far better than London Underground or New York’s subway. It is owned and run by MTR Corporation, which also runs systems in Stockholm, Melbourne, London and Beijing. MTR is now planning to roll out its AI overseer to the other networks it manages.
“It will probably
Chun’s AI program works with a simulated model of the entire system to find the best schedule for necessary engineering works. From its omniscient view it can see chances to combine work and share resources that no human could.
The schedule generated is still subject to human approval. Urgent, unexpected repairs can be added manually – the system simply reschedules less important tasks.
It also checks the maintenance it plans for compliance with local regulations. Chun’s team encoded into machine readable language 200 rules that the engineers must follow when working at night, such as keeping noise below a certain level in residential areas.
The AI overseer saves MTR two days a week of wrangling over the repair schedule. Also, MTR’s repair teams now have 30 minutes longer to finish their night’s work – a small time boost that saves MTR $800,000 a year.
The main
difference between normal software and Hong Kong’s AI is that it contains human knowledge that takes years to acquire through experience, says Chun. “We asked the experts what they consider when making a decision, then formulated that into rules – we basically extracted expertise from different areas about engineering works,” he says.Adel Sadek, a transport engineer at the University of Buffalo in New York, says Hong Kong’s system shows the power that niche artificial intelligence can have, as opposed to the dream of a human-level intelligence.
AIs like Chun’s do face a problem. His team spent months finding the most efficient algorithm for designing schedules. It settled on a genetic algorithm, which pits many solutions to the same problem against each other to find the best one. But the people that had to carry out the scheduled work took a while to get used to the idea, as they didn’t like not knowing why they were doing certain things.
Sadek says similar problems crop up when trying to get transport departments in the US to use AI to improve their networks. “People get scared when you talk to them about AI,” he says. “A Department of Transport official is responsible for lives, they want to see how the decisions are being made.”
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