Seven PolyU intelligent transport projects awarded Smart Traffic Fund
For a densely populated city like Hong Kong, a safe and convenient transport environment is crucial. The Government launched the Smart Traffic Fund in November 2021, and out of a total of 17 approved projects, seven innovative projects led by PolyU researchers were granted the Fund with a total amount of around HK$21million. The technologies developed in the PolyU projects employ AI, deep learning, 3D geo-spatial models, wireless transmission, data-driven techniques and more to enhance the commuting convenience of the public and improve driving safety.
Details of the seven projects are as follows:
Smart Assessment of Bridge Deck Efficiency and Safety in Hong Kong
Professor Tarek Zayed, Associate Head (Research) and Professor, Department of Building and Real Estate
This project aims at developing a multi-tier inspection method for detecting surface and subsurface defects in concrete bridge decks; and designing a smart efficiency assessment model for bridge decks using non-destructive evaluation techniques to improve road safety.
Investigation of an online data-driven intelligent automation platform for drivers considering the psychological condition instability and behaviours for a sustainable and safe transportation system
Dr Ng Kam-hung, Assistant Professor, Department of Aeronautical and Aviation Engineering
The project aims to develop an online data-driven risk-taking behavioural prediction mechanism by identifying the driver’s psychological condition instability using intelligent automation techniques.
The smart charging development of zero-emission autonomous electric vehicles by the X2V and V2X technologies with respect to the dynamic traffic, grid and energy information
Dr Cao Sunliang, Assistant Professor, Department of Building Environment and Energy Engineering
This project aims at developing a smart charging energy management system to recommend where, when and how to charge electric vehicles with a view to minimising mileage for locating available charging facilities.
Network-wide Traffic Speed-Flow Estimator
Ir Professor William Lam Hing-keung, Chair Professor of Civil and Transportation Engineering, Department of Civil and Environmental Engineering
The project proposes a model-based data-driven approach to develop a network-wide traffic speed-flow estimator for estimating traffic speeds and traffic flows simultaneously.
Road Safety Assessment using Advanced Driving Simulation Approach with 3D Geo-spatial Model
Dr Sze Nang-ngai, Associate Professor, Department of Civil and Environmental Engineering
This project aims to develop a 3D geo-spatial model that can be used for safety assessment in driving simulation experiments. An evidence-based decision support tool will be developed for identifying accident-prone locations and recommending safety improvement measures.
Prediction of Traffic Speed and Volume considering Malfunctioning Detectors using Deep Learning
Professor Edward Chung, Professor, Department of Electrical Engineering
This project aims to develop a Deep Learning model for predicting traffic speed and volume within one hour when some detectors malfunction. The Deep Learning model is also applicable for inputing missing data in offline applications.
Development and Deployment of an AI-enabled Parking Vacancy Prediction Framework using Multi-source Data
Dr Ma Wei, Assistant Professor, Department of Civil and Environmental Engineering
This project aims to develop a framework for predicting short term parking vacancies for both on-street and off-street parking in Hong Kong. A website and mobile phone-based parking guidance application will then be developed to provide predicted parking vacancy information to the public.
With a view to achieving Smart Mobility in Hong Kong, as one of the areas of Hong Kong’s Smart City Blueprint, PolyU will continue to develop novel technologies and promote transport-related applications to assist the industry and authorities in establishing appropriate strategies.
Smart Traffic Fund
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