Rollout of Charging Station Networks for Public Service
Project Description
For many electric vehicle (EV) owners in Hong Kong, circling parking lots near their homes after work to find an available charging spot has become a daily routine; similarly, driving to shopping malls on weekends often results in finding few, if any, available chargers. The number of electric private cars has surged at an unprecedented rate, from approximately 14,000 in 2019 to 135,000 by September 2025. However, regarding supporting infrastructure, as of mid-2025, the public charger-to-vehicle ratio in Hong Kong has reached approximately 1:9. The widening "scissors gap" between the exponential growth of vehicles and the linear, slow construction of charging facilities is severely eroding the user charging experience. To address this, the government recently announced a HKD 300 million "Fast-Charging Station Subsidy Scheme," aiming to add 3,000 high-speed chargers.
The core philosophy of this project is to assist the government planer in transitioning from a mere funder to a "System Designer" and "Market Shaper." Mere financial injection without precise layout and institutional coordination will fail to maximize benefits. We aim to guide market participant behaviour through data-driven insights, allowing the market to operate efficiently on its own. This includes planning charging stations to optimize spatial supply and developing pricing mechanisms to reshape customer behaviour.
This research aims to enhance the layout and operational efficiency of Hong Kong's fast-charging network by leveraging data analytics and operations research methodologies. The study focuses on two primary objectives:
(1) Charging Station Network Optimization
We will conduct a comprehensive analysis of EV distribution hotspots, grid load constraints, and traffic flow patterns across Hong Kong to develop a scientific siting model for the deployment of 3,000 new high-speed chargers. A key focus will be investigating optimal deployment strategies for high-demand zones—specifically older districts with limited private infrastructure and major transport hubs—to effectively bridge the supply-demand gap.
(2) Market Mechanism Design
To effectively reshape user behaviour and manage congestion, we will jointly optimize the capacity allocation and pricing strategies for charging stations. By integrating pricing decisions into a queuing theory framework, we aim to determine the optimal mechanism for station usage, balancing service availability with operational efficiency.
The core philosophy of this project is to assist the government planer in transitioning from a mere funder to a "System Designer" and "Market Shaper." Mere financial injection without precise layout and institutional coordination will fail to maximize benefits. We aim to guide market participant behaviour through data-driven insights, allowing the market to operate efficiently on its own. This includes planning charging stations to optimize spatial supply and developing pricing mechanisms to reshape customer behaviour.
This research aims to enhance the layout and operational efficiency of Hong Kong's fast-charging network by leveraging data analytics and operations research methodologies. The study focuses on two primary objectives:
(1) Charging Station Network Optimization
We will conduct a comprehensive analysis of EV distribution hotspots, grid load constraints, and traffic flow patterns across Hong Kong to develop a scientific siting model for the deployment of 3,000 new high-speed chargers. A key focus will be investigating optimal deployment strategies for high-demand zones—specifically older districts with limited private infrastructure and major transport hubs—to effectively bridge the supply-demand gap.
(2) Market Mechanism Design
To effectively reshape user behaviour and manage congestion, we will jointly optimize the capacity allocation and pricing strategies for charging stations. By integrating pricing decisions into a queuing theory framework, we aim to determine the optimal mechanism for station usage, balancing service availability with operational efficiency.
Supervisor
LYU, Guodong
Quota
2
Course type
UROP1100
UROP2100
UROP3100
UROP3200
UROP4100
Applicant's Roles
(1) Data Analysis & Spatiotemporal Modeling
The applicant will lead the analysis of large-scale heterogeneous datasets, including EV registration distribution, charger utilization rates, traffic flow, and grid capacity. Key responsibilities include identifying spatiotemporal patterns in charging demand and isolating the primary drivers of the current charger-to-vehicle imbalance to inform evidence-based decision-making.
(2) Strategic Policy & Operational Framework Development
Leveraging analytical insights, the applicant will develop a "Smart Siting Model for Fast Chargers" and formulate targeted recommendations for the government’s subsidy scheme. Furthermore, the applicant will draft standardized installation guidelines for Owners' Corporations, directly addressing administrative hurdles to lower adoption barriers for private infrastructure.
(3) Impact Communication & Reporting
The applicant is responsible for synthesizing research findings into comprehensive reports and high-level presentations. These deliverables will demonstrate to stakeholders—including government bodies and industry leaders—how data-driven institutional innovation can effectively resolve Hong Kong’s EV charging infrastructure challenges.
The applicant will lead the analysis of large-scale heterogeneous datasets, including EV registration distribution, charger utilization rates, traffic flow, and grid capacity. Key responsibilities include identifying spatiotemporal patterns in charging demand and isolating the primary drivers of the current charger-to-vehicle imbalance to inform evidence-based decision-making.
(2) Strategic Policy & Operational Framework Development
Leveraging analytical insights, the applicant will develop a "Smart Siting Model for Fast Chargers" and formulate targeted recommendations for the government’s subsidy scheme. Furthermore, the applicant will draft standardized installation guidelines for Owners' Corporations, directly addressing administrative hurdles to lower adoption barriers for private infrastructure.
(3) Impact Communication & Reporting
The applicant is responsible for synthesizing research findings into comprehensive reports and high-level presentations. These deliverables will demonstrate to stakeholders—including government bodies and industry leaders—how data-driven institutional innovation can effectively resolve Hong Kong’s EV charging infrastructure challenges.
Applicant's Learning Objectives
(1) Mastery of Urban Data Analytics and Operations Research
To acquire advanced proficiency in handling urban big data, utilizing Geographic Information Systems (GIS), and applying operations research methodologies (specifically queuing theory and optimization algorithms) to solve complex facility location and pricing problems.
(2) Domain Expertise in EV Infrastructure and Grid Integration
To develop a holistic understanding of the technical and regulatory ecosystems of green mobility. This includes analyzing the interplay between power grid load constraints, traffic dynamics, and urban land-use planning to identify barriers to EV adoption.
(3) Translation of Theory into Policy Implementation
To cultivate the ability to translate quantitative insights into actionable policy frameworks. The applicant aims to bridge the gap between theoretical siting models and practical execution strategies, such as drafting standardized installation guidelines and optimizing subsidy schemes.
(4) Contribution to Smart City Sustainability
To understand the broader impact of infrastructure optimization on carbon neutrality, positioning the applicant to contribute meaningfully to Hong Kong’s sustainable smart city development through evidence-based research.
To acquire advanced proficiency in handling urban big data, utilizing Geographic Information Systems (GIS), and applying operations research methodologies (specifically queuing theory and optimization algorithms) to solve complex facility location and pricing problems.
(2) Domain Expertise in EV Infrastructure and Grid Integration
To develop a holistic understanding of the technical and regulatory ecosystems of green mobility. This includes analyzing the interplay between power grid load constraints, traffic dynamics, and urban land-use planning to identify barriers to EV adoption.
(3) Translation of Theory into Policy Implementation
To cultivate the ability to translate quantitative insights into actionable policy frameworks. The applicant aims to bridge the gap between theoretical siting models and practical execution strategies, such as drafting standardized installation guidelines and optimizing subsidy schemes.
(4) Contribution to Smart City Sustainability
To understand the broader impact of infrastructure optimization on carbon neutrality, positioning the applicant to contribute meaningfully to Hong Kong’s sustainable smart city development through evidence-based research.
Complexity of the project
Moderate