Singapore $9m research programme for smart city solutions

NUS and ST Engineering are collaborating on a S$9 million, multi-year advanced digital technologies research programme to further their common goals of building a people-centric, smart future for Singapore and beyond.

Research efforts of this new programme will focus on technologies related to Smart City as well as Smart Maintenance, Repairs and Overhaul (MRO), covering five areas: resource optimisation and scheduling; prescriptive analytics; decision and sense-making; reasoning engine and machine learning; as well as digital twin. These research areas support ST Engineering’s focus on developing differentiated and people-centric, smart city solutions that meet the present and future needs of cities around the world. The interdisciplinary research areas are also aligned with NUS’ endeavours as a driving force behind smart city innovations, leveraging its deep expertise that spans multiple domains and faculties.

Helmed by Associate Professor Aaron Chia from NUS Industrial Systems Engineering and Management as its Director, and Mr Jinson Xu, Head of the Data Analytics Strategic Technology Centre at ST Engineering, as its Co-Director, the programme will first focus on two key research projects to lay the foundations for digital transformation and Industry 4.0:

1. Enterprise Digital Platform (EDP)
   As the backbone of smart city solutions, the EDP is a flexible, modular and scalable artificial intelligence (AI) platform that will support all the AI methodological areas, enabling the synthesis of disparate data sources and other internal or external systems, to orchestrate cross-vertical data and insights from customers and partners. All AI models derived from research projects under this programme will be integrated onto a common AI engine stacked within the EDP, paving the way for future-ready platforms that catalyse technology transformation and create new information-based revenue streams.
2. Urban Traffic Flow Management
   In this project, researchers will develop algorithms that alleviate traffic congestion by using a holistic urban traffic flow smoothening approach based on traffic data analytics and AI technologies. Examples include traffic state estimation and prediction, in addition to effective active traffic control and management strategies identification and implementation. This will have future applications as autonomous vehicle technologies, 5G infrastructure and machine-to-machine (M2M) technologies start to mature and proliferate.

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