Exploring the Influence from London Development on London Underground Mobility Through Network Analysis
DOI:
https://doi.org/10.54097/3cyn1156Keywords:
Network Analysis, London Underground Mobility, Urban Transportation PlanningAbstract
As cities grows up, people continue to gather in cities, and human activities in cities have become more intense than ever before. These human activities will subtly reshape urban space, causing significant transformation of the urban physical environment and socio-economic environment. The method of big data provides a promising perspective for us to explore the impact of human activities on the urban environment. This study investigates the commuting patterns of London citizens in the subway and explores the impact of human activities on the importance of network nodes in subway stations. We use network analysis to characterize the node’s importance through degree, closeness, and betweenness centrality, and then compare these centralities with the centrality with the human mobility as weight. The result shows that the node’s importance based on centrality characteristics are largely reshaped by human mobility, which in turn drive the government to build new lines to hold the mobility flow. This research is helpful for providing insights for the London subway and urban construction.
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[1] H. Wang et al., ‘A multivariate hierarchical regionalization method to discovering spatiotemporal patterns’, GIScience Remote Sens., vol. 60, no. 1, p. 2176704, Dec. 2023, doi: 10. 1080/ 15481603.2023.2176704.
[2] Z. Li et al., ‘Exploring the association between multi-mode transport and the built environment: A comparative study of metro, bus, taxi, and shared bike use’, Sustain. Cities Soc., vol. 114, p. 105789, Nov. 2024, doi: 10.1016/j.scs.2024.105789.
[3] X. Liu and H. Xia, ‘Networking and sustainable development of urban spatial planning: Influence of rail transit’, Sustain. Cities Soc., vol. 99, p. 104865, Dec. 2023, doi: 10.1016/ j.scs. 2023.104865.
[4] F. Ma, F. Ren, K. F. Yuen, Y. Guo, C. Zhao, and D. Guo, ‘The spatial coupling effect between urban public transport and commercial complexes: A network centrality perspective’, Sustain. Cities Soc., vol. 50, p. 101645, Oct. 2019, doi: 10.1016/j.scs.2019.101645.
[5] S. Porta, P. Crucitti, and V. Latora, ‘The Network Analysis of Urban Streets: A Primal Approach’, Environ. Plan. B Plan. Des., vol. 33, no. 5, pp. 705–725, Oct. 2006, doi: 10.1068/ b32045.
[6] G. Pflieger and C. Rozenblat, ‘Introduction. Urban Networks and Network Theory: The City as the Connector of Multiple Networks’, Urban Stud., vol. 47, no. 13, pp. 2723–2735, 2010, doi: 10.1177/0042098010377368.
[7] B. Schaller, ‘The New Automobility: Lyft, Uber and the Future of American Cities’, Jul. 2018, Accessed: Oct. 01, 2024. [Online]. Available: https://trid.trb.org/View/1527868.
[8] S. Chen and D. Zhuang, ‘Evolution and Evaluation of the Guangzhou Metro Network Topology Based on an Integration of Complex Network Analysis and GIS’, Sustainability, vol. 12, no. 2, Art. no. 2, Jan. 2020, doi: 10.3390/su12020538.
[9] M. Li, W. Yu, and J. Zhang, ‘Clustering Analysis of Multilayer Complex Network of Nanjing Metro Based on Traffic Line and Passenger Flow Big Data’, Sustainability, vol. 15, no. 12, Art. no. 12, Jan. 2023, doi: 10.3390/su15129409.
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