Machine-learning to inspire Singapore metro buildout

Researchers are trying to distill smart transit philosophy into a machine-learning algorithm. Scientists hope their smart transit model will reveal a recipe for a smarter city, organized in way that relieves the congestion common on the mass transit systems of major cities. "Singapore needs an efficient transport system to support people's activities given the existing and planned infrastructure," project leader Christopher Monterola, a researcher at the Agency for Science, Technology and Research's Institute of High Performance Computing, explained in a news release. "To guide planners, we needed a model that could predict ridership under the regional centers plan." Like many cities, Singapore consists of a large central downtown, or an inner central business district, surrounded by less dense residential and industrial zones. With so many commuting in and out of the central business district at rush hour, the setup promotes congestion.