Impact Projects 2023-24
Principal Investigator: Prof J. Chen (Department of Civil Engineering)
Telephone: 2859 2646
- Development of a Smart Interactive Kiosk that adopts IoT technology (mainly related to QR codes) and can educate people about lithium battery recycling through animation, and survey people about their recycling habits in Hong Kong;
- Establishment of an analytical system for summarizing and predicting lithium battery recycling through application of machine learning methods and collection of data using Smart Interactive Kiosk and internet data sources; and
- Collaboration with HKU undergraduate students and one NGO, Endeavour Environmental Education Fund, to promote Smart Interactive Kiosk and increase awareness of the importance and environmental impact of lithium battery recycling among the Hong Kong community.
This project aims to promote green and sustainable energy in HK through applying Internet of Things (IoT) Technology and machine learning methods. Using IoT technology, the project will design and implement a smart interactive kiosk for the education and promotion of lithium battery recycling. Using the machine learning methods, the project will develop an analytical system for summarizing and predicting the usages of lithium batteries. The project then results in the details of lithium battery lifecycle in HK. The project will also involve collaboration with HKU undergraduate students and one NGO to promote recycling efforts in the HK community.
Target Deliverables in 2023/24:
- Design and development of the smart interactive kiosk for education and promotion of lithium battery recycling;
- Animation video to introduce the importance and environmental impact of lithium battery recycling and the process of lithium battery recycling;
- Questionnaire survey to investigate people's understanding of lithium battery recycling and their recycling habits;
- Report analyzing the results of the questionnaire survey and providing recommendations for improving recycling behaviors and promoting the use of the kiosk;
- The details of the lithium battery lifecycle data and near future projection of the lithium battery usages; and
- Collaboration with the NGO to promote the kiosk and recycling efforts in the Hong Kong community.
Principal Investigator: Dr Edith Ngai (Department of Electrical and Electronic Engineering)
- To use smart technologies to help building managers identify potential leaks and use patterns that contribute to very high levels of water usage; and
- To translate target beneficiaries’ heightened appreciation of the importance of achieving water sustainability goals, at both the local and the global scale, into measurable changes in the level of water use of participating buildings, which can then inform ESG reporting practices of management companies.
The WaterWise Hong Kong project aims to digitalize metering data to help building managers identify potential leaks and other anomalies. Currently, most non-domestic water meters are read manually every four months, with very limited information other than total volume of use. By sharing round-the-clock data on water use efficiency with the Water Supplies Department and building managers, this will assist water infrastructure management and planning, and enable informed decisions about water consumption and reduction of environmental impacts. The project also offers capacity building programs for staff on incorporating water conservation practices into ESG reporting and business strategy.
Target Deliverables in 2023/24:
- A report on the KE outcomes and impacts achieved;
- An impact case study based on the relevant template of the UGC’s Research Assessment Exercise (RAE);
- Social media posts, infographics and/or posters on LinkedIn and for the buildings;
- A project website;
- An article on the KE outcomes and impact in layman terms (in both English and Chinese) for distribution to the media; and
- Records and photos of seminars and sharing sessions with building managers and WSD.
Principal Investigator: Dr J.T. Ke (Department of Civil Engineering)
- This project will first establish an agent-based simulation platform to simulate customers’ order requesting behaviors and taxis’ movements for searching and delivering passengers in e-hailing taxi markets. The simulation platform will provide some portals, through which the shared mobility companies can test and evaluate their dispatching and other operational strategies.
- The second objective of the project is to develop a visualization tool on the basis of the simulation platform to demonstrate the taxis’ movements on Hong Kong’s actual road network and their matching with waiting passengers.
- The project will also develop some AI-driven matching algorithm for matching e-hailing taxis and passengers in the broadcasting mode. The performances of the algorithms will be tested based on our proposed simulation platform. Specifically, as a passenger raises his order, this matching algorithm will select a group of nearby drivers to broadcast this order request. In the meantime, we will also develop and deliver an AI-driven matching algorithm in the dispatching mode that can determines the pair-by-pair matching between passengers and drivers in each matching time interval. These two matching algorithms are expected to improve the system efficiency by reducing passengers’ waiting time, improving taxis’ utilization rate or increasing the order fulfillment rate.
This project will establish a large-scale agent-based simulation platform for shared mobility service operations. The simulation platform will provide accessible portals for relevant companies/organizations in Hong Kong, such as ride-hailing companies and e-hailing taxi firms, to test their operational strategies in terms of matching, pricing and repositioning. The proposed simulation platform will also be employed to evaluate the impacts of government regulations and policies for shared mobility services, such as price regulation and license control. In addition, the project will develop some Artificial-Intelligence based algorithms to improve the efficiency of service providers’ operations. These AI-based algorithms can assist large and small e-hailing companies in better managing their taxi fleets to improve system efficiency, safety, sustainability and equity. Some visualization tools will be implemented in the simulation platform to demonstrate the trajectories and movements of taxis and ride-hailing vehicles on the actual transportation network of Hong Kong.
Target Deliverables in 2023/24:
- An agent-based simulation platform will be established to simulate customers’ order requesting behaviors and taxis’ movements for searching and delivering passengers in e-hailing taxi markets. The simulation platform will provide some portals, through which the ride-hailing companies can test and evaluate their dispatching and other operational strategies;
A visualization tool on the basis of the simulation platform will be provided to demonstrate the taxis’ movements on the Hong Kong Road Network and their matching with waiting passengers. The visualization tool can be published in the Inno Wing Two at HKU for demonstration and monitoring;
- An efficient matching algorithm for matching e-hailing taxis and passengers in the real time will be developed and tested in our simulation platform. Specifically, as a passenger raises his order, the matching algorithm will select a group of nearby drivers to broadcast this order request so as to avoid vicious competition between drivers and reduce drivers’ waste of time on the way to pick up the passenger;
- An efficient repositioning algorithm will be delivered to guide idle drivers move from regions with excessive supply to regions with excessive demand. This algorithm will help balance the imbalance between supply and demand over the whole network. The e-hailing taxi companies can employ this algorithm to recommend the best cruising route for drivers when they are idle, in order to increase their probability of getting matched with the next passenger; and
- An interactive tool will be provided for visitors of the Inno Wing Two to alter the matching and repositioning strategies of the shared mobility systems. The system dynamics, including vehicles’ movements and supply and demand conditions, will change correspondingly, which will be showcased by the visualization module integrated in the simulation platform. Media coverage in newspaper, radio and/or TV.