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ASTRI sets up joint innovation lab to develop smarter railway

The Hong Kong Applied Science and Technology Research Institute (ASTRI) has kicked off a significant initiative that will see the railway system in Hong Kong become smarter and safer.

ASTRI will explore innovations that will apply its award-winning technology solutions in a collaboration with MTR Corporation. The two parties last Friday signed a Memorandum of Understanding (MOU) to establish the MTR ASTRI Joint Railway Innovation Laboratory.

The Chief Executive Officer of ASTRI stated that it pursues innovation that enhances Hong Kong’s technology-based industries and improves the lives of its residents – and that includes how they get to work or go about for leisure.

The MOU will set in motion far-reaching opportunities as ASTRI’s award-winning research and innovation will be put to real-life use cases to help the city’s railway operator enhance efficiency and productivity.

Hong Kong Applied Science and Technology Research Institute (ASTRI) was founded by the Government of the Hong Kong Special Administrative Region in 2000 with the mission of enhancing Hong Kong’s competitiveness in technology-based industries through applied research.

The institute’s core R&D competence in various areas is grouped under five Technology Divisions, namely Artificial Intelligence and Big Data Analytics, Communications, Cybersecurity, Cryptography and Trusted Technologies, Integrated Circuits and Systems, and IoT and Sensors.

Five areas of applications including Smart City, Financial Technologies, Intelligent Manufacturing, Health Technologies, and Application Specific Integrated Circuits.

Hong Kong’s smart, efficient public railway

A crucial aspect of more intelligent city living is public transport, an earlier OpenGov Asia report notes. This means making sure residents can navigate their daily lives efficiently, reliably, and sustainably.

Hong Kong’s railway services, operated by MTR Corporation Ltd, are the backbone of the city’s transport. Public transit is the most used mode of transport: 90% of citizens use public transport in their daily lives. Train travel is the most efficient way of moving large amounts of people through the compact city, and MTR Corporation has made social and environmental friendliness a priority.

Through its ongoing digital transformation, the aim is to enhance the reliability, efficiency and integration of their services, not only in Hong Kong but in its other global cities of operation.

The only way to push beyond current high performance is through better digital technology. One aspect is discovering how best to predict failure. This predictive maintenance relies on image and data analytics supported by artificial intelligence and helps in the regular inspection of equipment and the detection of problems early on and consistently.

AI is also used to assist with planned construction and maintenance on the railways. By using AI to optimise planning, it is possible to maximise the usage of the line when it is not in service to minimise the disruption when unavoidable planned construction or system upgrades are necessary, and ensure that ongoing works do not disrupt the functional rail lines.

AI-enhanced driving is a tool which goes together with more reliable operations. MTR Corporation’s trains already have an automatic operation, but sometimes the fail-safe principle will slow operations. By introducing artificial intelligence into the driving of trains, MTR Corporation will be able to mitigate this risk when every second count in train operations.

At the heart of these applications of technology is the drive to improve the customer experience. Advanced data analytics are being used to predict customer movements and demands to better plan its service – in due course, the same technology will be used even to respond in real-time to meet customer demand.

Furthermore, MTR Corporation is providing real-time service information to their customers, such as when the next train is due to arrive and plans to expand its offerings to include how crowded trains will be and which carriages have space available.

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