NUS, Grab set up $4.4M AI lab for urban transport

Ride-sharing operator Grab will tap data from its platform to extract insights on transport patterns in Southeast Asian cities, and jointly develop applications with National University of Singapore to "transform" urban transportation.

The National University of Singapore (NUS) and ride-sharing operator Grab have set up an artificial intelligence (AI) laboratory to study transport patterns across Southeast Asia and develop applications to "transform" urban transportation.

Launched with an initial joint investment of S$6 million (US$4.4 million), the research facility would be Grab's first AI lab as well as the Singapore university's first such facility with a commercial partner.

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The Grab-NUS AI Lab is housed at the NUS Institute of Data Science and will tap data from Grab's platform, which has processed more than 2 billion rides, to analyse real-world transport challenges in the region.

The partnership also would leverage the university's research expertise in AI and bring together Grab's research scientists and its faculty members to establish traffic patterns and "identify ways to directly impact mobility and liveability" of Southeast Asian cities, the two companies said.

NUS is part of Singapore's Data Science Consortium, which aims to boost the country's capabilities in the field as well as analytics and facilitate research collaboration between institutes of higher learning, research institutes, and market players.

The research facility would start by focusing on cities in which Grab operated, before expanding its efforts to include other cities in Southeast Asia and looking at challenges such as road congestion.

The Grab-NUS AI Lab team, comprising 28 researchers, would build an AI platform that was integrated with large-scale machine learning and visual analytics and able to develop applications from Grab's datasets. These, for instance, could help transport agencies monitor and optimise traffic flow.

The research facility would look to build algorithms with several key objectives, including offering more personalised services based on passengers' needs and intent, matching drivers to jobs they preferred, and improving accuracy of pick-up points.