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Bridging Global Perspectives: A Comparative Review of Agent-Based Modeling for Block-Level Walkability in Chinese and International Research

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Udgivet i:Buildings vol. 15, no. 19 (2025), p. 3613-3638
Hovedforfatter: Wang, Yidan
Andre forfattere: Wang Renzhang, Xu, Xiaowen, Zhang, Bo, White, Marcus, Huang, Xiaoran
Udgivet:
MDPI AG
Fag:
Behavior
Technological change
Built environment
Public health
Pedestrians
Urbanism
Neighborhoods
Data integration
Semantic segmentation
Transportation planning
Climate adaptation
Deep learning
Climate change
Mobility
Simulation
Perceptions
Walking
Growth patterns
Planning
International studies
Decision making
Spatial analysis
Data collection
Urban planning
Crime prevention
Design optimization
Sensory integration
Agent-based models
Statistical methods
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Resumen:As cities strive for human-centered and fine-tuned development, Agent-Based Modeling (ABM) has emerged as a powerful tool for simulating pedestrian behavior and optimizing walkable neighborhood design. This study presents a comparative bibliometric analysis of ABM applications in block-scale walkability research from 2015 to 2024, drawing on both Chinese- and English-language literature. Using visualization tools such as VOSviewer, the analysis reveals divergences in national trajectories, methodological approaches, and institutional logics. Chinese research demonstrates a policy-driven growth pattern, particularly following the introduction of the “15-Minute Community Life Circle” initiative, with an emphasis on neighborhood renewal, age-friendly design, and transit-oriented planning. In contrast, international studies show a steady output driven by technological innovation, integrating methods such as deep learning, semantic segmentation, and behavioral simulation to address climate resilience, equity, and mobility complexity. The study also classifies ABM applications into five key application domains, highlighting how Chinese and international studies differ in focus, data inputs, and implementation strategies. Despite these differences, both research streams recognize the value of ABM in transport planning, public health, and low-carbon urbanism. Key challenges identified include data scarcity, algorithmic limitations, and ethical concerns. The study concludes with future research directions, including multimodal data fusion, integration with extended reality, and the development of privacy-aware, cross-cultural modeling standards. These findings reinforce ABM’s potential as a smart urban simulation tool for advancing adaptive, human-centered, and sustainable neighborhood planning.
ISSN:2075-5309
DOI:10.3390/buildings15193613
Fuente:Engineering Database