Abstract
Rail transit plays a crucial role in improving urban sustainability and livability. In many Chinese cities, the planning of rail transit routes and stations is focused on facilitating new developments rather than revitalizing existing built-up areas. This approach reflects the local governments’ expectations of substantial growth to reshape the urban structure. However, existing research on transit-oriented development (TOD) rarely explores the spatial interactions between individual transit stations and investigates how they can be integrated to achieve synergistic effects and balanced development. This study proposes that rail transit systems impact urban structure through two “forces”: the provision of additional and reliable carrying capacity and the reduction of travel time between locations. Metro passenger flow is used as a proxy for these forces, and community detection techniques are employed to identify the actual and optimal spatial clusters in Wuhan, China. The results reveal that the planned sub-centers align reasonably well with the optimal spatial clusters in terms of spatial configuration. However, the actual spatial clusters tend to have longer internal travel times compared to the optimal clusters. Further exploration suggests the need for equalizing land use density within planned spatial clusters served by the metro system. Additionally, promoting concentrated, differentiated, and mixed functional arrangements in metro station areas with low passenger flows within the planned clusters could be beneficial. This paper presents a new framework for investigating urban spatial clusters influenced by a metro system.
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References
Burger M, Meijers E (2012). Form follows function? Linking morphological and functional polycentricity. Urban Studies, 49(5): 1127–1149
Calthorpe P (1993). The Next American Metropolis: Ecology, Community, and the American Dream. Princeton: Princeton Architectural Press
Castells M (1996). The Rise of the Network Society. Oxford: Blackwell
Cervero R, Guerra E (2011). Urban densities and transit: A multidimensional perspective
Chakour V, Eluru N (2016). Examining the influence of stop level infrastructure and built environment on bus ridership in Montreal. Journal of Transport Geography, 51: 205–217
Chen W, Chen X, Cheng L, Liu X, Chen J (2022). Delineating borders of urban activity zones with free-floating bike sharing spatial interaction network. Journal of Transport Geography, 104: 103442
Curtis C, Olaru D (2010). The relevance of traditional town planning concepts for travel minimization. Planning Practice and Research, 25(1): 49–75
Ducruet C, Beauguitte L (2014). Spatial science and network science: Review and outcomes of a complex relationship. Networks and Spatial Economics, 14(3–4): 297–316
Fortunato S, Hric D (2016). Community detection in networks: A user guide. Physics Reports, 659: 1–44
Gao S, Liu Y, Wang Y, Ma X (2013). Discovering spatial interaction communities from mobile phone data. Transactions in GIS, 17(3): 463–481
Girvan M, Newman M E (2002). Community structure in social and biological networks. Proceedings of the National Academy of Sciences of the United States of America, 99(12): 7821–7826
Giuliano G, Small K A (1991). Subcenters in the Los Angeles region. Regional Science and Urban Economics, 21(2): 163–182
Ibraeva A, De Almeida Correia G H, Silva C, Antunes A P (2020). Transit oriented development: A review of research achievements and challenges. Transportation Research Part A, Policy and Practice, 132: 110–130
Jacobs J (1961). The Death and Life of Great American Cities. London: Vintage
Jin M, Gong L, Cao Y, Zhang P, Gong Y, Liu Y (2021). Identifying borders of activity spaces and quantifying border effects on intraurban travel through spatial interaction network. Computers, Environment and Urban Systems, 87: 101625
Jun M J, Choi K, Jeong J E, Kwon K H, Kim H J (2015). Land use characteristics of subway catchment areas and their influence on subway ridership in Seoul. Journal of Transport Geography, 48: 30–40
Kloosterman R C, Musterd S (2001). The polycentric urban region: Towards a research agenda. Urban Studies, 38(4): 623–633
Leslie T F (2010). Identification and differentiation of urban centers in Phoenix through a multi-criteria kernel-density approach. International Regional Science Review, 33(2): 205–235
Li F, Zhou T (2019). Effects of urban form on air quality in China: An analysis based on the spatial autoregressive model. Cities, 89: 130–140
Li Y, Liu X (2018). How did urban polycentricity and dispersion affect economic productivity? A case study of 306 Chinese cities. Landscape and Urban Planning, 173: 51–59
Lin D, Allan A, Cui J (2015). The impact of polycentric urban development on commuting behaviour in urban China: Evidence from four subcentres of Beijing. Habitat International, 50: 195–205
Liu X, Derudder B, Wu K (2016). Measuring polycentric urban development in China: An intercity transportation network perspective. Regional Studies, 50(8): 1302–1315
Liu X, Gong L, Gong Y, Liu Y (2015). Revealing travel patterns and city structure with taxi trip data. Journal of Transport Geography, 43: 78–90
Liu X, Wang M (2016). How polycentric is urban China and why? A case study of 318 cities. Landscape and Urban Planning, 151: 10–20
McMillen D P (2001). Nonparametric employment subcenter identification. Journal of Urban Economics, 50(3): 448–473
Munoz Mendez F, Klemmer K, Han K, Jarvis S (2018). Community structures, interactions and dynamics in London’s bicycle sharing network. In: Proceedings of the 2018 ACM International Joint Conference and 2018 International Symposium on Pervasive and Ubiquitous Computing and Wearable Computers, 1015–1023
Reichardt J, Bornholdt S (2006). Statistical mechanics of community detection. Physical Review E: Statistical, Nonlinear, and Soft Matter Physics, 74(1): 016110
Roth C, Kang S M, Batty M, Barthélemy M (2011). Structure of urban movements: Polycentric activity and entangled hierarchical flows. PLoS One, 6(1): e15923
Song Y, Merlin L, Rodriguez D (2013). Comparing measures of urban land use mix. Computers, Environment and Urban Systems, 42: 1–13
Stead D, Marshall S (2001). The relationships between urban form and travel patterns. An international review and evaluation. European Journal of Transport and Infrastructure Research, 1: 113–141
Sun B, He Z, Zhang T, Wang R (2016a). Urban spatial structure and commute duration: An empirical study of China. International Journal of Sustainable Transportation, 10(7): 638–644
Sun Y, Fan H, Li M, Zipf A (2016b). Identifying the city center using human travel flows generated from location-based social networking data. Environment and Planning. B, Planning & Design, 43(3): 480–498
Tanahashi Y, Rowland J R, North S, Ma K L (2012). Inferring human mobility patterns from anonymized mobile communication usage. In: Proceedings of the 10th International Conference on Advances in Mobile Computing & Multimedia. ACM: 151–160
Traag V A, Bruggeman J (2009). Community detection in networks with positive and negative links. Physical Review E, 80: 036–115
Vasanen A (2012). Functional polycentricity: Examining metropolitan spatial structure through the connectivity of urban sub-centres. Urban Studies, 49(16): 3627–3644
Vasanen A (2013). Spatial integration and functional balance in poly-centric urban systems: A multi-scalar approach. Tijdschrift voor Economische en Sociale Geografie, 104(4): 410–425
Wang T, Yue W, Ye X, Liu Y, Lu D (2020). Re-evaluating polycentric urban structure: A functional linkage perspective. Cities, 101: 102672
Wikipedia (2019). Wuhan
Xiao L, Lo S, Zhou J, Liu J, Yang L (2021). Predicting vibrancy of metro station areas considering spatial relationships through graph convolutional neural networks: The case of Shenzhen, China. Environment and Planning. B, Urban Analytics and City Science, 48(8): 2363–2384
Yang J, Chen J, Le X, Zhang Q (2016). Density-oriented versus development-oriented transit investment: Decoding metro station location selection in Shenzhen. Transport Policy, 51: 93–102
Yue Y, Zhuang Y, Yeh A G, Xie J Y, Ma C L, Li Q Q (2017). Measurements of POI-based mixed use and their relationships with neighbourhood vibrancy. International Journal of Geographical Information Science, 31(4): 658–675
Zhang Y, Marshall S, Cao M, Manley E, Chen H (2021). Discovering the evolution of urban structure using smart card data: The case of London. Cities, 112: 103157
Zhong C, Arisona S M, Huang X, Batty M, Schmitt G (2014). Detecting the dynamics of urban structure through spatial network analysis. International Journal of Geographical Information Science, 28(11): 2178–2199
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Xiao, L., Xu, W. Urban spatial cluster structure in metro travel networks: An explorative study of Wuhan using big and open data. Front. Eng. Manag. 11, 231–246 (2024). https://doi.org/10.1007/s42524-024-0296-2
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DOI: https://doi.org/10.1007/s42524-024-0296-2