IT IS A myth that, whatever his faults, Benito Mussolini, dictator of Italy in the 1920s, 1930s and 1940s, made the trains run on time. He didn’t. If even a man with dictatorial powers cannot enforce a railway timetable, what hope is there in a messy democracy? In India a third of trains are held up. Seven out of ten are late during the rush hour at some of Britain’s busiest stations. Nor is the fabled reliability of Japan’s railway always what it seems, with a number of commuter lines into Tokyo experiencing hold-ups.
Plamen Angelov of the University of Lancaster, in Britain, has an idea that he hopes will make train delays rarer. Often, Dr Angelov observes, the problem is not the inefficiency of operators but the behaviour of passengers—the “platform-train interface”, to use railway parlance. When trains arrive, passengers crowd around the doors waiting to board, restricting the flow of those getting off. When they are about to depart, people often hold doors open, delaying that departure. (A recent study by Japan’s Railway Bureau found that passengers attempting to board trains after their scheduled departure times accounted for almost 50% of delays.)
Passengers also frequently stand too close to the carriages for safety. Waiting for malefactors to move back behind the yellow safety line on a platform might hold a train up for less than a minute. But over the course of a journey those minutes add up. Even a slight delay is compounded if it causes a train to slip out of its running order and be held at a subsequent station, or be required to follow a slower service.
Dr Angelov thinks that applying artificial intelligence to the problem might help. And that is what he and his team are doing. Using images from the CCTV cameras already mounted in carriages and on platforms, their system employs algorithms that have been trained to detect objects such as people, luggage, pushchairs and bicycles. It then measures the movements and positions of these objects relative to areas such as the train doors or the yellow safety line and uses this information to predict problems.
The cameras in the carriages detect how busy particular doors are getting as passengers leave their seats and gather next to the exits when the train approaches a station. At the same time, the station cameras monitor the numbers waiting for the train to arrive, whereabouts they are standing along the platform, and how encumbered they are. The two sets of data can then be compared, providing warning of likely areas of congestion. This permits passengers—particularly those on the platform—to be directed to doors that will be less busy. Indeed, says Dr Angelov, the process could be automated by using LED strips along a platform that illuminate in green, amber or red to mark the easiest places to board an arriving service.
The system Dr Angelov and his colleagues have devised does not rely on having a central computer to do the number-crunching. The video-analysis algorithms will be embedded into small electronic devices incorporated into the cameras themselves. So far, the researchers have tested the system using video supplied by railway companies. They are now working with Digital Rail, a new firm based at the university, to conduct tests on a live railway with a view to commercialising it. Commuters are creatures of habit. But if video analysis can help to make their trains run on time, then even the most hardened travellers may be prepared to change their routine.