The Hong Kong Polytechnic University (PolyU) has received funding from the MTR Academy’s 2023 MTR Research Funding Scheme for six forward-looking research projects aimed at exploring advanced railway technology applications.
PolyU researchers from Faculty of Construction and Environment and Faculty of Engineering lead these innovative research projects to initiate cutting-edge technologies for the future advancement of railways. These six projects have received total funding of HK$7.25 million.
Prof. Christopher CHAO, Vice President (Research and Innovation) of PolyU, said, “PolyU has consistently demonstrated strong research capabilities in the field of smart railways, dedicating itself to providing comprehensive and practical innovative solutions to the industry. The MTR Research Funding Scheme plays a crucial role in supporting these transformative advancements. We will continue to drive innovation, delivering long-term benefits to the railway development of Hong Kong, the nation, and even the global stage.”
The awarded research projects encompass a broad spectrum of potential applications for environmental, social and governance aspects of rail operators and smart community development. Leveraging PolyU’s academic and research excellence in construction, environment and engineering, these projects initiate innovative solutions for emergency evacuation, achieving sustainable development goals, noise control in railway engineering, autonomous modularised trains and fire resilience.
Launched by MTR Academy on February 2023, MTR Research Funding (MRF) Scheme supports forward-looking research projects to explore, shape and realise the mass public transport systems of tomorrow, breach the boundaries of current solutions and thinking, and offer insights into achieving services and operational excellence for tomorrow's transport. Maximum total funding of each proposal up to HK$1.5 million for 3 years.
|Faculty of Construction and Environment
Dr Xinyan HUANG
Associate Professor of Department of Building Environment and Energy Engineering
|Intelligent Emergency Digital Twin System in Metro Station for Fire Evacuation
The proposed intelligent safety monitoring system employs top-notch technologies, including AIoT, computer vision, and deep learning, to achieve a smart fire emergency response and evacuation.
It can predict the dynamic flow of occupants and the risk of a stampede in a fire emergency, and can provide onsite evacuation guidance by dynamic exit sign systems. Moreover, it can recognise the status and behaviours of occupants to identify those who need immediate help near the fire incident and transfer real-time information to firefighters and rescue teams.
Dr Anthony Chun Yin YUEN
Assistant Professor, Presential Young Scholar, Department of Building Environment and Energy Engineering
|Coupling Fire and Toxicty Predictions Using CFD-MD Simulations for Enhanced Pedestrian Movement Modelling and Fire Resilience Designs of Metro Stations
The identification of parental combustion fuel gases, which are essential for toxicity, such as the pyrolysis and combustion releases resulting from the burning of specific building and furnishing materials, are yet to be characterized.
This project will develop a new strategy to incorporate the coupled and non-linear pyrolysis-combustion kinetics via Molecular Dynamics (MD) characterisation of the thermal degradation process validated by thermal gravimetry (TG) data. It considers the formation pathways and predictions of toxic chemical species and smoke/soot particulates, contributing to firefighting and egress system designs.
Dr Siu Kai LAI
Associate Professor of Department of Civil and Environmental Engineering
|Development of a New Inerter-based Rail Damper to Mitigate Railway-induced Ground-borne Noise by a Physics-informed Deep Learning Framework
Using rail dampers is an effective mitigation measure to reduce the impact of structure-borne noise caused by railway vehicles. However, their installation difficulty and working performance pose limitations.
To overcome these challenges, this project proposes a new inerter-based rail damper in metamaterial structure. This design aims to enhance the force transfer characteristics of a mechanical system. In addition, a time-sequencing physics-informed neural network (TS-PINN) approach will be employed to optimise the structural design.
|Faculty of Engineering
|Prof. Chi-yung CHUNG
Head of Department of Electrical and Electronic Engineering and Chair Professor of Power Systems Engineering
|Developing an Electromagnetic Energy Harvesting System to Energize Monitoring Systems in the Hong Kong MTR System
This project aims to develop a sustainable energy supply system for wireless sensor networks (WSNs) devices by utilizing electromagnetic energy harvesting (EMEH) techniques without interfering with the operation of the railroad system. WSNs energized with the EMEH techniques will offer a long-lasting and comprehensive monitoring network while paving the way towards sustainable smart railway systems.
A cost-effective, efficient, reliable, scalable, and sustainable smart solution for energizing monitoring and condition assessment systems on railways is proposed.
|Dr Hongbo YE
Research Assistant Professor of Department of Electrical and Electronic Engineering
|Planning and Operation of Future Railway System with Autonomous Modularized Trains and Virtual Coupling Signaling
This project proposes a vision of future railway systems, realised by autonomous modularised trains running under virtual coupling signaling, in order to handle the tempo-spatial variation of passenger demand.
The proposal consists of three phases: developing optimisation models and algorithms for scheduling, investigating rescheduling methods, and exploring energy-efficient trajectory planning.
|Dr Jingzheng REN
Associate Professor of Department of Industrial and Systems Engineering
|Promoting Environmental Social and Governance (ESG) in MTR for Pursuing Sustainable Development Goals and Better Service Quality
This project aims to establish a scientific and comprehensive criteria system for performance assessment regarding ESG and sustainable development goals (SDGs).
By combining multi-criteria decision-making methods and systems dynamics, the proposed novel multi-dimensional assessment tool can incorporate the interdependences and interactions among the ESG and SDGs for identifying the complex cause-effect relationships and the critical factors, and it can further map and analyse them in both qualitative and quantitative ways. This new tool for performance assessment and improvement related to ESG and SDGs not only can use for Metro/Rail operators, but also can implement in other fields and cities.