| RESEARCH HIGHLIGHTS |


[RESEARCH HIGHLIGHTS]


A*STAR researchers created a program that predicts public transport usage in Singapore based on land use and the location of amenities, an essential capability for smart city planning.


Urban planning


SMART CITY TRANSPORT SYSTEMS


A MACHINE-LEARNING PROGRAM USES LAND USE AND AMENITIES DATA TO PREDICT PUBLIC TRANSPORT USE IN SINGAPORE


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 Singapore1. Traditional cities consist of an inner central


business district (CBD), where most people work, surrounded by outer residential and


4 A*STAR RESEARCH


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 creating regional centers by the year 2030. The planners hope to encourage businesses to open at specified regional centers around the city-state, easing peak-time pressure and encouraging public transport use. “We’re aiming to understand the recipe for a smart city,” explains Christopher Monterola


"SINGAPORE NEEDS AN EFFICIENT TRANSPORT SYSTEM TO SUPPORT PEOPLE’S ACTIVITIES GIVEN THE EXISTING AND PLANNED INFRASTRUCTURE."


at the A*STAR Institute of High Perfor- mance Computing, who led the project in collaboration with scientists across Singapore. “Singapore needs an efficient transport system to support people’s activities given the existing and planned infrastructure. To guide planners,


ISSUE 6 | JANUARY – MARCH 2017


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