MODELING OF PUBLIC PERCEPTION IN METRO-LED UNDERGROUND PUBLIC SPACE FOR PLANNING OPTIMIZATION: A CASE STUDY OF HONG KONG

AUTOR(I) / AUTHOR(S): Qi Pan, Yun-Hao Dong, Shiu-Tong Thomas, Fang-Le Peng

Download Full Pdf   

DOI:  10.46793/ACUUS2025.5.4.51

SAŽETAK / ABSTRACT:

Metro-led underground public spaces (MUPS) have become integral components of modern urban environments, particularly in high-density cities. However, their enclosed nature and artificial characteristics present various challenges affecting user experience and well-being. While traditional surveys and questionnaires have provided valuable insights into public perception of these spaces, such methods are often resource-intensive and limited in scale. This study proposes an innovative framework for evaluating public perception in MUPS by leveraging social media data and Large Language Model (LLM) technology. We developed a six-dimensional perception indicator system termed “FEPICS” (Functionality, Engagement, Pleasurability, Inclusiveness, Comfort, and Safety), encompassing 37 distinct indicators. Using Google Maps review data from eight metro stations along Hong Kong’s Tsuen Wan Line, we employed LLM-based classification methods to extract and quantify public perception information, achieving a semantic recognition accuracy of 91,4%. Our analysis revealed that the “Functionality” dimension received the highest public attention, while “Inclusiveness” and “Comfort” garnered relatively less focus. The indicator “transfer” demonstrated the highest positive perception value, whereas “crowd congestion” exhibited the strongest negative sentiment. Through our perception evaluation index, we identified Jordan and Tsim Sha Tsui stations as best performers, attributable to their superior environmental design elements despite high crowding levels. These findings highlight the importance of balancing functional efficiency with environmental quality in MUPS design. The proposed FEPICS framework and LLM-based methodology offer a systematic approach for understanding and quantifying public perception in underground spaces, contributing to evidence-based planning practices. This study demonstrates the potential of integrating social media analytics with advanced language models for urban perception research, while providing practical insights for optimizing underground public space development.

KLJUČNE REČI / KEYWORDS:

metro-led underground public space, social media data, public perception, large language model

PROJEKAT / ACKNOWLEDGEMENT:

This work was supported by the National Natural Science Foundation of China (NSFC) [grant number: 42301289] and [grant number: 42071251], and Hong Kong Scholars Program [grant number: XJ2023059].

LITERATURA / REFERENCES:

• Bobylev, N. (2016). Underground space as an urban indicator: Measuring use of subsurface. Tunnelling and Underground Space Technology, 55, 40-51. https://doi.org/10.1016/j.tust.2015.10.024
• Delgado Jalón, M. L., Gómez Ortega, A., & De Esteban Curiel, J. (2019). The social perception of urban transport in the city of Madrid: the application of the Servicescape Model to the bus and underground services. European Transport Research Review, 11(1), 37. https://doi.org/10.1186/s12544-019-0373-5
• Dong, Y.H., Peng, F.L., & Guo, T.F. (2021). Quantitative assessment method on urban vitality of metro-led underground space based on multi-source data: A case study of Shanghai Inner Ring area. Tunnelling and Underground Space Technology, 116, 104108. https://doi.org/10.1016/j.tust.2021.104108
• Dong, Y.H., Peng, F.L., Li, H., & Men, Y.Q. (2023). Spatiotemporal characteristics of Chinese metro-led underground space development: A multiscale analysis driven by big data. Tunnelling and Underground Space Technology, 139, 105209. https://doi.org/10.1016/j.tust.2023.105209
• Guo, C., & Yang, Y. (2025). A multi-modal social media data analysis framework: Exploring the complex relationships among urban environment, public activity, and public perception—A case study of Xi’an, China. Ecological Indicators, 171, 113118. https://doi.org/10.1016/j.ecolind.2025.113118
• Jasińska, K., & Kłosek-Kozłowska, D. (2024). Passengers’ experience in underground non-transfer metro stations: The impact of spatial characteristics. Tunnelling and Underground Space Technology, 143, 105482. https://doi.org/10.1016/j.tust.2023.105482
• Liu, J., Yan, H., White, M., & Huang, X. (2025). A comparative analysis of perceptions of insecurity in Milan and Beijing metro stations. Frontiers of Architectural Research, 14(4), 863-884. https://doi.org/10.1016/j.foar.2024.12.003
• Liu, S.C., Peng, F.L., Qiao, Y.K., & Dong, Y.H. (2024). Quantitative evaluation of the contribution of underground space to urban resilience: A case study in China. Underground Space, 17, 1-24. https://doi.org/10.1016/j.undsp.2023.11.007
• Ma, C.X., Peng, F.L., Qiao, Y.K., & Li, H. (2023). Influential factors of spatial performance in metro-led urban underground public space: A case study in Shanghai. Underground Space, 8, 229-251. https://doi.org/10.1016/j.undsp.2022.03.001
• Mehta, V. (2014). Evaluating Public Space. Journal of Urban Design, 19(1), 53-88.
• Pan, Q., Ng, S.T. T., Peng, F.L., & Dong, Y.H. (2025). A bottom-up approach of knowledge graph modelling for urban underground public spaces: Insights into public cognition. Tunnelling and Underground Space Technology, 163, 106710. https://doi.org/10.1016/j.tust.2025.106710
• Shao, Y., Ng, S. T., Xing, J., Zhang, Y., Kwok, C. Y., & Cheng, R. (2024). Dynamic station criticality assessment of urban metro networks considering predictive passenger flow. Tunnelling and Underground Space Technology, 154, 106088. https://doi.org/10.1016/j.tust.2024.106088
• Su, T., & Sun, M. (2025). Understanding park-based health-promoting behavior and emotion with large-scale social media data: The case of Tianjin, China. Cities, 162, 105987. https://doi.org/10.1016/j.cities.2025.105987
• Yin, L., Han, M., & Nie, X. (2024). Unlocking Blended Emotions and Underlying Drivers: A Deep Dive into COVID-19 Vaccination Insights on Twitter Across Digital and Physical Realms in New York, Using ChatGPT. Urban Science, 8(4). https://doi.org/10.3390/urbansci8040222
• Zeng, R., Zhao, Z., Zhang, J., Shen, Z., & Luo, J. (2025). Indoor environmental quality and user satisfaction: Post-occupancy evaluation of urban underground complex. Building and Environment, 270, 112492. https://doi.org/10.1016/j.buildenv.2024.112492