Open AccessThe network pattern of underground pedestrian system and its role to urban resilience
Author(s) -
Ziwei Zhao,
Jun Xie,
Chenhao Zhang,
Daoxing Guo,
Yulu Chen,
Yuan Yuan
Publication year - 2021
Publication title -
iop conference series. earth and environmental science
Language(s) - English
Resource type - Journals
eISSN - 1755-1307
pISSN - 1755-1315
DOI - 10.1088/1755-1315/703/1/012026
Subject(s) - resilience (materials science) , megacity , pedestrian , computer science , network analysis , graph , social network analysis , geography , data science , risk analysis (engineering) , business , transport engineering , engineering , theoretical computer science , economy , world wide web , physics , electrical engineering , economics , thermodynamics , social media
In recent years, underground pedestrian system (UPS) has been developed worldwide, especially within central megacity areas, but research on UPS characteristics is still in the qualitative description stage, and quantitative methods are lacking to assess the impact of UPS on resilience. Therefore, it is essential to quantitatively measure UPS resilience and thus be able to make decisions on how UPS can be improved. The authors propose topological analysis of UPS networks based on computational and functional graph representations, which represents a new approach to theoretical discussions and empirical evidence for planning and configuring a UPS, thereby contributing to urban resilience. These representations provide functional views in which nodes represent single underground spaces and links represent underground sideways. Various graph measures are computed for structural analysis, and results provide insights into the required physical properties for planning resilient UPS. Based on validations applied to two typical high-density areas in Nanjing Xinjiekou and Guangzhou Zhujiang New Town, China, the authors examine how different UPS network patterns influence resilience performance, then further discuss the social and economic factors affecting network patterns, and give suggestions.