![]() ![]() ![]() None of these graphs showed any pointers, as the incidents happened regardless of location, time, or train. ![]() They started with the raw train incident data, and working in a Jupyter notebook imported, cleaned, and consolidated it before producing analyses for time, location, and train IDs. It’s a minor departure from Hackaday’s usual hardware and open source fare, but there is still plenty to be learned from their techniques. Engineers had laid the blame on electrical interference, but despite their best efforts no culprit could be found.Įventually the problem found its way to the Singaporean government’s data team, and their story of how they identified the source of the interference makes for a fascinating read. Without warning, trains would lose their electronic signalling, and their safety systems would then apply the brakes and bring them to a halt. It was thus rather unfortunate for the Singaporean metro operators that trains on their Circle Line started to experience disruption. The position of every train is known exactly at all times, and with far less possibility for human error, the networks are both safer and more efficient.Īs you might expect, the city-state of Singapore has a metro with every technological advance possible, recently built and with new equipment. Nineteenth and twentieth century human and electromechanical systems have been replaced by up-to-date computers, and in some cases the trains even operate autonomously without a driver. Not necessarily in the trains themselves, though they have certainly changed, but in the signalling and system automation. If you have been a regular traveler on one of the world’s mass transit systems over the last few decades, you will have witnessed something of a technological revolution. ![]()
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