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The GREAT Smart Cities Institute (GSCI) has earned the prestigious “Best Smart Mobility Award” at the Hong Kong/Shanghai Co-operation Open Data Challenge (HSODC) 2025 for its innovative project Metro Maestro. This groundbreaking solution, developed in collaboration with the HKUST-MTR Joint Lab, is designed to revolutionize urban transit by using near real-time predictions to manage metro disruptions efficiently.
Metro Maestro goes beyond simple data analysis. By accurately forecasting passenger behavior and the movement across the metro network, it empowers metro companies to allocate resources swiftly and effectively, minimizing chaos during service interruptions.
This latest achievement adds to a series of significant milestones for GSCI, which has been consistently recognized for its pioneering work in smart urban solutions. Other accolades include the "Smart Mobility Grand Award" and the "Award of the Year" at the Hong Kong ICT Awards 2024, as well as honors from the Asia Pacific ICT Awards 2024 and the International Exhibition of Inventions in Geneva. These awards highlight the remarkable potential of the team’s collaboration with MTR Corporation Limited to advance meaningful research and development and foster innovation in the field of public transportation.
How Metro Maestro Powers a Smarter Commute
Ever wonder how the MTR team knows exactly where to send help when a line is delayed? Think of Metro Maestro as a digital "crystal ball" for Hong Kong's transportation network.
In a city as dynamic as Hong Kong, managing the daily movements of millions of people is a constant challenge. Metro Maestro addresses this by providing an unprecedented level of foresight for our public transportation network.
At its core, this smart mobility technology creates a detailed, living replica of the city’s entire transit system, from the MTR to buses and ferries. This digital "twin" allows us to simulate the journeys of over 4 million daily commuters.
Beyond this city-wide simulation, the system also features a powerful, specialized model solely for disruptions. Rather than simply reacting to problems, this model uses vast amounts of data to simulate and predict how passengers would react, providing the operator crucial foresight. This allows them to anticipate and manage disruptions before they escalate, ensuring best possible experience for passengers.
