Anchored at the NUS Institute of Data Science, the Grab-NUS AI Lab will leverage data from the Grab platform to solve complex, real-world challenges in Southeast Asia. Having facilitated billions of rides in Southeast Asia, Grab’s vast troves of data can provide deeper insights into how cities across Southeast Asia move today. Through the use of AI algorithms, Grab’s wealth of data can bring forth meaningful insights, such as building richer maps, understanding passengers’ preference, modelling of traffic conditions, analysing driver behaviour and detecting real-time traffic events.
By combining the data with NUS’ R&D expertise in the field of AI, and under the supervision of senior Grab research scientists and NUS faculty members, the Grab-NUS AI Lab will map out traffic patterns and identify ways to directly impact mobility and liveability of cities across Southeast Asia.
The Grab-NUS AI Lab will also play an important role in developing local AI talents. Post-doctoral research scientists, as well as PhD students enrolled in NUS under EDB’s Industrial Postgraduate Programme, will have the opportunity to work off real-life data from Grab, and hone their data analytics and AI skills by addressing real-world challenges and creating smart solutions for urban transportation in Singapore, Asia and the world.
The Grab-NUS AI Lab was officially launched on 18 July 2018. It is located in the innovation 4.0 building situated on the NUS Kent Ridge campus. Led by renowned NUS professors, together with senior Grab research scientists, the projects include:
- Passenger AI: Researchers will develop algorithms to understand passengers’ needs and preferences to serve passengers with smarter personalised transportation services;
- Driver AI: Algorithms will be developed to gather insights on drivers’ preferences and behaviours to improve drivers’ proficiency and ensure safe journeys;
- Traffic AI: Researchers will develop algorithms to detect traffic events and predict urban traffic flow based on insights on the local traffic.
- Location AI: Richer maps will be developed for accurate localisation and modelling of points-of-interests to better deliver transportation services.
- Grab-in-Motion Transportation AI Platform: A robust AI platform will be developed for large-scale machine learning and visual analytics to process massive transportation data and create novel AI applications.