hyryu.postech.ac.kr

Hyunyoung Ryu, Ph.D.

Urban Data & Process Analytics

Hyunyoung Ryu (PhD) is a Research Assistant Professor at POSTECH, affiliated with the Analytics and Information Management Lab and the Future Open City Innovation Center (FOIC). Her research focuses on the integration of urban data science, sustainable urban planning, and addressing the challenges of population decline. She is currently leading the development of the Urban Transformation Process (UTP) Model, supported by the Sejong Science Fellowship from the National Research Foundation of Korea (2022–2027).

#UrbanData #SustainabilityScience #BigData #SmartCity #UrbanTransformation

Education

Ph.D. in Sustainability Science – The University of Tokyo, 2016

M.S. in Urban and Regional Planning – Seoul National University, 2011

B.S. in Landscape Architecture – Seoul National University, 2009

Professional Experience

Research Assistant Professor, Analytics and Information Management Lab, POSTECH (2024–present)

Postdoctoral Researcher, Future Open City Innovation Center (FOIC), POSTECH (2019–2024)

Consultant, African Development Bank (AfDB), Asia External Representation Office, Tokyo (2017–2018, fixed-term)

Assistant Project Researcher, CSR Design Green Investment Advisory, Tokyo – C40 Building Energy Retrofit Project (2016)

Postdoctoral Researcher, Spatial Planning and Design Lab, The University of Tokyo (2016)

Awards

Sejong Science Fellowship, National Research Foundation of Korea (2022–2027)

MEXT Scholarship, Government of Japan (2012–2016)

Projects

Basic Research Lab for Smart Signal Control in the Era of Autonomous Vehicles
(Ministry of Science and ICT, 2022.6 – 2025.2)
Development of a Hydrogen Production Base and Refueling Station Location Model for Optimal Supply Route Design
(Korea Energy Technology Evaluation and Planning, 2021.4 – 2022.9)
Development of a Data-Based Economic Incentive Model for Citizen-Participatory Smart City Operation
(National Information Society Agency, 2020.10 – 2021.1)
Intelligent Service Model Based on Urban Data for Enhancing Citizen Experience
(National Information Society Agency, 2019.11 – 2020.2)
Gyeongsangbuk-do Smart City Regional Hub Project
(Gyeongsangbuk-do Province, 2020.3 – 2021.2)
Pohang Smart City Master Plan Development
(City of Pohang, 2019.1 – 2020.2)

Publications

Ryu, H., & Song, M. (n.d.). Development and application of the Urban Transformation Process (UTP) model for shrinking cities: Cases from Japan and Korea. Manuscript in preparation.
Lee, H., Ryu, H., & Chin, J. (n.d.). Urban AI Readiness through Platform Indicators of Algorithmic Governance: Evidence from City Open Data Portals across Continents. Manuscript submitted.
Ryu, H., Lim, J., & Song, M. (n.d.). Automatic generation of open data-based traffic simulation model. Manuscript submitted.
Lee, S., Kim, H., Kim, B. I., Song, M., Lee, D., & Ryu, H. (2024). Site and capacity selection for on-site production facilities in a nationwide hydrogen supply chain deployment plan. International Journal of Hydrogen Energy, 50, 968–987.
Ryu, H., Lee, D., Shin, J., Song, M., Lee, S., Kim, H., & Kim, B. (2023). A web-based decision support system (DSS) for hydrogen refueling station location and supply chain optimization. International Journal of Hydrogen Energy, 48(93), 36223–36239.
Jang, H., Ryu, H., & Kwahk, J. (2023). A framework for simulating the suitability of data usage in designing smart city services. Journal of Urban Planning and Development, 149(3), 0423019.
Ryu, H., & Lim, H. (2023). Linking smart city and urban sustainability issue: A comparative study of smart city services in Japan and Korea. Urban and Regional Planning Review, 10, 263–293.
Ryu, H., Kim, B., Song, M., Kim, H., Lee, D., Lee, S., Shin, J., Yoo, Y., Kim, S., & Lee, H. (2022). Optimization of hydrogen refueling station deployment and supply chain networks: Current status and research suggestions. Journal of the Korean Institute of Industrial Engineers, 48(2), 211–226. (In Korean)
Cho, H., Ryu, H., & Song, M. (2022). Pass2vec: Analyzing soccer players’ passing style using deep learning. International Journal of Sports Science & Coaching, 17(2), 355–365.
Lim, H., Ryu, H., & Lee, J. (2020). A comparative study of smart city projects and strategies in Korea and Japan. Journal of Korea Planning Association, 55(2), 143–155. (In Korean)

Patent

Ryu, H., Lim, J., Song, M. (2024). Method and apparatus for automatic generation of SUMO-based traffic simulation models using open data. Korean Patent Application No. 10-2024-0164456, filed November 18, 2024.

Recent Works

Shrinking Cities in
Korea and Japan

Focusing on demographic transitions, this research identifies and interprets population patterns in Korea and Japan. Through clustering and feature importance analysis, it reveals five distinct urban trajectories—continuous increase, recent decline, peak, continuous decline, and recovery—and highlights the influence of aging, geographic context, and proximity to capital regions. The findings offer insights for urban policy and planning in the context of national population decline.

#PopulationDecline, #Typology, #Clustering, #Trajectories, #Korea, #Japan

Urban Transformation Process (UTP) Model

The Urban Transformation Process (UTP) model is a data-driven framework designed to capture and analyze the temporal dynamics of urban change. Built on process mining techniques, structured event logs is created to identify sequential patterns and transition pathways in city development. The model supports strategic urban planning and decision-making by revealing how cities evolve over time, aligning with the goals of smart and sustainable urban development.

#UrbanTransformation, #ProcessMining, #ShrinkingCities

Automatic Generation of Open Data-based Traffic Simulation Model

Automation of traffic simulation model generation enables efficient use of open data—such as road networks, signal systems, and traffic volumes—within the SUMO platform. By minimizing manual preprocessing, it facilitates rapid modeling of complex, multi-intersection urban networks. This approach enhancaes traffic prediction, improves simulation accuracy, and supports digital twin applications for real-time urban mobility analysis.

#OpenData, #DataProcessing, #TrafficSimulation, #SUMO, #DigitalTwin

News

[Conference Presentation] "Urban Transformation Process (UTP) Model for Shrinking Cities: Based on Japan and Korea with Broader Policy Insights", Applied Urban Modelling (AUM) 2026: Re-imaging Urban Modeling, University of Cambridge, Cambridge, UK / Planned (June 2026)
[Conference Presentation] "Development and Application of the Urban Transformation Process (UTP) Model for Shrinking Cities: A Process Mining Approach", International Conference of Asia-Pacific Planning Societies (ICAPPS), ChungHwa University, Hsinchu, Taiwan (August, 2025)
[Guest Presentation] Senseable City Lab, MIT, Boston, US (July, 2025)
[Personal Note] Maternity Leave (August-October, 2024)
[Conference Presentation] "Unravelling Issues of Declining Cities in Korea: A Text Mining Approach on News Data", AMPS Livable Cities, London, UK / Virtual (June, 2024)

Updated 28 Dec 2025

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