DEVELOPING A DATA-DRIVEN BAYESIAN NETWORK MODEL FOR LAYOUT PLANNING OF URBAN UNDERGROUND COMMERCIAL FACILITIES

АУТОР(И) / AUTHOR(S): Yong-Kang Qiao, Ji-Shuai Xiao, Nikolai Bobylev, Fang-Le Peng

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DOI:  10.46793/ACUUS2025.1.8.48

САЖЕТАК / ABSTRACT:

Underground commercial facilities are intricately linked to urban public life, representing the most impactful use of underground space within urban spatial systems. However, existing planning approaches for these facilities largely depend on the subjective judgments of urban planners, resulting in considerable uncertainties in their development. To address these limitations, this study developed a Bayesian Network model to extract valuable information embedded in a diverse array of multi-source datasets of underground space and urban development. The modeling process combined machine learning techniques and expert knowledge integrating the correlations among influencing factors such as population, commercial vibrancy, transportation accessibility, GDP, land price and strategic locations. The average prediction error rates for the number of floors and total development area of underground commercial facilities were 16.67% and 22.22%, respectively. Case studies in Shanghai and Zhengzhou demonstrated the effectiveness and applicability of the proposed model for master planning of urban underground commercial facilities. It is anticipated that the findings of this study will provide valuable guidance for the sustainable use of urban underground space.

КЉУЧНЕ РЕЧИ / KEYWORDS:

Urban underground commercial space, Bayesian network, machine learning, expert knowledge, master planning

ПРОЈЕКАТ / ACKNOWLEDGEMENT:

This work was supported by the National Natural Science Foundation of China (NSFC) [grant numbers 42201284, 42071251].

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