25 Jan '17, 1pm

Machine-learning program predicts public transport use in Singapore

Machine-learning program predicts public transport use in Singapore

From left: Gary Lee, Hu Nan, Erika Fille Legara, and Christopher Monterola. Credit: A*STAR Institute of High Performance Computing From schools and shops to hospitals and hotels, a modern city is made of many different parts. Urban planners must take account of where these services are located when designing efficient transit networks. A*STAR researchers have developed a machine-learning program to accurately recreate and predict public transport use, or 'ridership', based on the distribution of land-use and amenities in Singapore. Traditional cities comprise an inner central business district (CBD), where most people work, surrounded by outer residential and industrial zones. Unfortunately for commuters, the high volume of people traveling to and from the CBD can cause gridlock at peak hours. To alleviate some of this frustration, the Singaporean government is working on ...

Full article: https://phys.org/news/2017-01-machine-learning-singapore....

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