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Classifying High-Speed Rail Stations in the Yangtze River Delta, China: The Node-Place Modelling Approach

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Abstract

Following the rapid expansion of the high-speed rail (HSR) network in China, transit-oriented development (TOD) has been widely introduced in HSR station areas of both large and small cities. However, as most HSR stations are new construction, some problems have emerged, e.g., over-planned station areas, airport-like stations above local demand, lack of vitality in station areas, and long distances between stations and city centers. This paper examines the HSR-TOD pattern in the Yangtze River Delta, China, with the densest HSR networks in regional China. We employed the classic node-place model through multiple data sources to classify HSR stations and examine their features. The results show that 123 HSR stations can be classified into eight clusters. Each cluster has typical development characteristics of node and place values and their relations. Given increasing cities with multi HSR stations, the classification results are further analyzed to explore the functional division of HSR stations in the city. The results show that only large cities can accommodate the primary HSR station with high node and place values, suggesting the challenges of HSR-TOD approaches in the YRD. It further highlights that the node-place relation is a dynamic process that necessitates coordinating short- and long-term station area development in the context of rapid urbanization.

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References

  • Barab, Aacute, Si, A. (1999). Emergence of Scaling in Random Networks. Science, 286, 509-512.

  • Bertolini, L. (1996). Nodes and places: Complexities of rail station redevelopment. European Planning Studies, 4, 331–345.

    Article  Google Scholar 

  • Bertolini, L. (1999). Spatial development patterns and public transport: The application of an analytical model in the Netherlands. Planning Practice and Research, 14, 199–210.

    Article  Google Scholar 

  • Bertolini, L. (2007). Cities and transport: Exploring the need for new planning approaches, The Network Society: A New Context for Planning? Routledge, pp. 67–80.

  • Bertolini, L. (2008). Station areas as nodes and places in urban networks: An analytical tool and alternative development strategies. Rail development: impacts on urban dynamics, Physica-Verlag HD, pp. 35–57.

  • Campa, J. L., López-Lambas, M. E., & Guirao, B. (2016). High speed rail effects on tourism: Spanish empirical evidence derived from China’s modelling experience. Journal of Transport Geography, 57, 44–54.

    Article  Google Scholar 

  • Cao, Z., Asakura, Y., Tan, Z. (2020). Coordination between node, place, and ridership: Comparing three transit operators in Tokyo. Transportation Research Part D: Transport and Environment, 87, 102518.

  • Cascetta, E., & Pagliara, F. (2008). Integrated rails-based policies: The Regional Metro System (RMS) project of Naples and Campania. Transport Policy, 15, 81–93.

    Article  Google Scholar 

  • Caset, F., Blainey, S., Derudder, B., Boussauw, K., & Witlox, F. (2020). Integrating node-place and trip end models to explore drivers of rail ridership in Flanders, Belgium. Journal of Transport Geography, 87, 102796.

    Article  Google Scholar 

  • Caset, F., Vale, D. S., & Viana, C. M. (2018). Measuring the Accessibility of Rail Stations in the Brussels Regional Express Network: A Node-Place Modeling Approach. Networks and Spatial Economics, 18, 495–530.

    Article  Google Scholar 

  • Chen, X., & Lin, L. (2015). The node-place analysis on the “hubtropolis” urban form: The case of Shanghai Hongqiao air-rail hub. Habitat International, 49, 445–453.

    Article  Google Scholar 

  • Chorus, P., & Bertolini, L. (2011). An application of the node-place model to explore the spatial development dynamics of station areas in Tokyo. Journal of Transport and Land Use, 4, 45–58.

    Google Scholar 

  • Coronado, J. M., Ureña, J. M. d., Miralles, J. L. (2019). Short- and long-term population and project implications of high-speed rail for served cities: analysis of all served Spanish cities and re-evaluation of Ciudad Real and Puertollano. European Planning Studies, 27, 434-460.

  • Deng, T., Gan, C., Perl, A., & Wang, D. (2020). What caused differential impacts on high-speed rail station area development? Evidence from global nighttime light data. Cities, 97, 102568.

    Article  Google Scholar 

  • Diao, M., Zhu, Y., & Zhu, J. (2017). Intra-city access to inter-city transport nodes: The implications of high-speed-rail station locations for the urban development of Chinese cities. Urban Studies, 54, 2249–2267.

    Article  Google Scholar 

  • Freeman, L. C. (1977). A Set of Measures of Centrality Based on Betweenness. Sociometry, 40, 35–41.

    Article  Google Scholar 

  • Freeman, L. C. (1978). Centrality in social networks conceptual clarification. Social Networks, 1, 215–239.

    Article  Google Scholar 

  • Ghodousi, M., Alesheikh, A. A., & Saeidian, B. (2016). Analyzing public participant data to evaluate citizen satisfaction and to prioritize their needs via K-means, FCM and ICA. Cities, 55, 70–81.

    Article  Google Scholar 

  • Hou, Q., & Li, S.-M. (2011). Transport infrastructure development and changing spatial accessibility in the Greater Pearl River Delta, China, 1990–2020. Journal of Transport Geography, 19, 1350–1360.

    Article  Google Scholar 

  • Hu, S., He, Z., Wu, L., Yin, L., Xu, Y., & Cui, H. (2020). A framework for extracting urban functional regions based on multiprototype word embeddings using points-of-interest data. Computers, Environment and Urban Systems, 80, 101442.

    Article  Google Scholar 

  • Huang, B., Zhou, Y., Li, Z., Song, Y., Cai, J., & Tu, W. (2020). Evaluating and characterizing urban vibrancy using spatial big data: Shanghai as a case study. Environment and Planning B: Urban Analytics and City Science, 47, 1543–1559.

    Google Scholar 

  • Jiang, S., Alves, A., Rodrigues, F., Ferreira, J., & Pereira, F. C. (2015). Mining point-of-interest data from social networks for urban land use classification and disaggregation. Computers, Environment and Urban Systems, 53, 36–46.

    Article  Google Scholar 

  • Jiao, J., Wang, J., & Jin, F. (2017). Impacts of high-speed rail lines on the city network in China. Journal of Transport Geography, 60, 257–266.

    Article  Google Scholar 

  • Kim, H., Sultana, S., & Weber, J. (2018). A geographic assessment of the economic development impact of Korean high-speed rail stations. Transport Policy, 66, 127–137.

    Article  Google Scholar 

  • Li, B., Li, T., Yu, M., & Chen, B. (2017). Can equalization of public services narrow the regional disparities in China? A spatial econometrics approach. China Economic Review, 44, 67–78.

    Article  Google Scholar 

  • Li, X., Huang, B., Li, R., & Zhang, Y. (2016). Exploring the impact of high speed rails on the spatial redistribution of economic activities - Yangtze River Delta urban agglomeration as a case study. Journal of Transport Geography, 57, 194–206.

    Article  Google Scholar 

  • Li, Z., Han, Z., Xin, J., Luo, X., Su, S., & Weng, M. (2019). Transit oriented development among metro station areas in Shanghai, China: Variations, typology, optimization and implications for land use planning. Land Use Policy, 82, 269–282.

    Article  Google Scholar 

  • Li, Z., He, S., Su, S., Li, G., & Chen, F. (2020). Public Services Equalization in Urbanizing China: Indicators, Spatiotemporal Dynamics and Implications on Regional Economic Disparities. Social Indicators Research, 152, 1–65.

    Article  Google Scholar 

  • Long, Y., & Huang, C. (2019). Does block size matter? The impact of urban design on economic vitality for Chinese cities. Environment and Planning B: Urban Analytics and City Science, 46, 406–422.

    Google Scholar 

  • Lu, H., Zhao, P., Hu, H., Zeng, L., Wu, K. S., & Lv, D. (2022). Transport infrastructure and urban-rural income disparity: A municipal-level analysis in China. Journal of Transport Geography, 99, 103292.

  • Monajem, S., & Ekram Nosratian, F. (2015). The evaluation of the spatial integration of station areas via the node place model; an application to subway station areas in Tehran. Transportation Research Part D: Transport and Environment, 40, 14–27.

    Article  Google Scholar 

  • Moyano, A., Martínez, H. S., & Coronado, J. M. (2018). From network to services: A comparative accessibility analysis of the Spanish high-speed rail system. Transport Policy, 63, 51–60.

    Article  Google Scholar 

  • Niknam, T., Taherian Fard, E., Pourjafarian, N., & Rousta, A. (2011). An efficient hybrid algorithm based on modified imperialist competitive algorithm and K-means for data clustering. Engineering Applications of Artificial Intelligence, 24, 306–317.

    Article  Google Scholar 

  • Ortuño-Padilla, A., Espinosa-Flor, A., & Cerdán-Aznar, L. (2017). Development strategies at station areas in southwestern China: The case of Mianyang city. Land Use Policy, 68, 660–670.

    Article  Google Scholar 

  • Pagliara, F., & Mauriello, F. (2020). Modelling the impact of High Speed Rail on tourists with Geographically Weighted Poisson Regression. Transportation Research Part A: Policy and Practice, 132, 780–790.

    Google Scholar 

  • Paul, A., & Sen, J. (2018). Livability assessment within a metropolis based on the impact of integrated urban geographic factors (IUGFs) on clustering urban centers of Kolkata. Cities, 74, 142–150.

    Article  Google Scholar 

  • Peek, G.-J., Bertolini, L., & De Jonge, H. (2006). Gaining insight in the development potential of station areas: A decade of node-place modelling in The Netherlands. Planning Practice & Research, 21, 443–462.

    Article  Google Scholar 

  • Pezeshknejad, P., Monajem, S., & Mozafari, H. (2020). Evaluating sustainability and land use integration of BRT stations via extended node place model, an application on BRT stations of Tehran. Journal of Transport Geography, 82, 102626.

    Article  Google Scholar 

  • Pol, P. (2002). A renaissance of stations, railways and cities. Economic effects, development strategies and organisational issues of European high-speed-train stations. Delft: DUP Science.

  • Reusser, D. E., Loukopoulos, P., Stauffacher, M., & Scholz, R. W. (2008). Classifying rail stations for sustainable transitions – balancing node and place functions. Journal of Transport Geography, 16, 191–202.

    Article  Google Scholar 

  • Sabidussi, G. (1966). The centrality index of a graph. Psychometrika, 31, 581–603.

    Article  Google Scholar 

  • Schütz, E. (1998). Stadtentwicklung durch Hochgeschwindigkeitsverkehr, Konzeptionelle und Methodische Absätze zum Umgang mit den Raumwirkungen des schienengebunden Personen-Hochgeschwindigkeitsverkehr (HGV) als Beitrag zur Lösung von Problemen der Stadtentwicklung. Informationen Zur Raumentwicklungs, Heft, 6, 369–383.

    Google Scholar 

  • Shannon, C. E. (1948). A mathematical theory of communication. The Bell System Technical Journal, 27, 379–423.

    Article  Google Scholar 

  • Stoilova, S., & Nikolova, R. (2016). Classifying rail passenger stations for use transport planning@ application to Bulgarian rail network. Transport Problems, 11, 143–155.

    Article  Google Scholar 

  • Vale, D. S. (2015). Transit-oriented development, integration of land use and transport, and pedestrian accessibility: Combining node-place model with pedestrian shed ratio to evaluate and classify station areas in Lisbon. Journal of Transport Geography, 45, 70–80.

    Article  Google Scholar 

  • Vale, D. S., Viana, C. M., & Pereira, M. (2018). The extended node-place model at the local scale: Evaluating the integration of land use and transport for Lisbon’s subway network. Journal of Transport Geography, 69, 282–293.

    Article  Google Scholar 

  • Wang, B., Loo, B. P., & Li, L. (2020a). Situating High-Speed Railway Stations within Local Urban Contexts: Passenger Satisfaction with Intermodal Integration at the Hong Kong HSR Station. Built Environment, 46(3), 362–378.

    Article  Google Scholar 

  • Wang, Y., Deng, Y., Ren, F., Zhu, R., Wang, P., Du, T., & Du, Q. (2020b). Analysing the spatial configuration of urban bus networks based on the geospatial network analysis method. Cities, 96, 102406.

    Article  Google Scholar 

  • Wang, L., & Duan, X. (2018). High-speed rail network development and winner and loser cities in megaregions: The case study of Yangtze River Delta, China. Cities, 83, 71–82.

    Article  Google Scholar 

  • Wang, L., Wong, C., & Duan, X. (2016). Urban growth and spatial restructuring patterns: The case of Yangtze River Delta Region, China. Environment and Planning B: Planning and Design, 43(3), 515–539.

  • Wang, L., Zhang, W., & Duan, X. (2019a). Understanding accessibility changes from the development of a high-speed rail network in the Yangtze River Delta, China: Speed increases and distance deductions. Applied Spatial Analysis and Policy, 12(4), 1011–1029.

  • Wang, S., Wang, J., & Liu, X. (2019b). How do urban spatial structures evolution in the high-speed rail era? Case study of Yangtze River Delta, China. Habitat International, 93, 102051.

  • Watts, D. J., & Strogatz, S. H. (1998). Collectivedynamics of “small-world” networks. Nature, 393, 440–442.

    Article  Google Scholar 

  • Wei, S., Jiao, J., Wang, L., & Xu, J. (2020). Evolving Characteristics of High-Speed Rail Network Structure in Yangtze River Delta, China: The Perspective of Passenger Flows. Applied Spatial Analysis and Policy, 13, 925–943.

    Article  Google Scholar 

  • Wei, S., & Wang, L. (2020). Examining the population flow network in China and its implications for epidemic control based on Baidu migration data. Humanities and Social Sciences Communications, 7, 145.

    Article  Google Scholar 

  • Wei, S., Zheng, W., & Wang, L. (2021). Understanding the configuration of bus networks in urban China from the perspective of network types and administrative division effect. Transport Policy, 104, 1–17.

  • Wei, S., Yuan, J., Qiu, Y., Luan, X., Han, S., Zhou, W., & Xu, C. (2017). Exploring the potential of open big data from ticketing websites to characterize travel patterns within the Chinese high-speed rail system. PLoS ONE, 12, e0178023.

    Article  Google Scholar 

  • Wei, S., & Zhang, X. (2015). Passenger flow characteristics and effects based on the remaining high-speed rail tickets: A case study on high-speed rail stations from shanghai to nanjing. City Planning Review, 39, 38–42.

    Google Scholar 

  • Welch, T. F., & Widita, A. (2019). Big data in public transportation: A review of sources and methods. Transport Reviews, 39, 795–818.

    Article  Google Scholar 

  • Xu, W., & Huang, Y. (2019). The correlation between HSR construction and economic development – Empirical study of Chinese cities. Transportation Research Part a: Policy and Practice, 126, 24–36.

    Google Scholar 

  • Yang, H., Dijst, M., Witte, P., Van Ginkel, H., & Wang, J. E. (2018). Comparing passenger flow and time schedule data to analyse High-Speed Rails and urban networks in China. Urban Studies, 56, 1267–1287.

  • Yin, M., Bertolini, L., & Duan, J. (2015). The effects of the high-speed rail on urban development: International experience and potential implications for China. Progress in Planning, 98, 1–52.

    Article  Google Scholar 

  • Yin, P., Lin, Z., & Prideaux, B. (2019). The impact of high-speed rail on tourism spatial structures between two adjoining metropolitan cities in China: Beijing and Tianjin. Journal of Transport Geography, 80, 102495.

    Article  Google Scholar 

  • Zemp, S., Stauffacher, M., Lang, D. J., & Scholz, R. W. (2011). Classifying rail stations for strategic transport and land use planning: Context matters! Journal of Transport Geography, 19, 670–679.

    Article  Google Scholar 

  • Zhang, Y., Marshall, S., & Manley, E. (2019). Network criticality and the node-place-design model: Classifying metro station areas in Greater London. Journal of Transport Geography, 79, 102485.

    Article  Google Scholar 

  • Zheng, L., Long, F., Chang, Z., & Ye, J. (2019). Ghost town or city of hope? The spatial spillover effects of high-speed rail stations in China. Transport Policy, 81, 230–241.

    Article  Google Scholar 

  • Zhou, J., Yang, Y., & Webster, C. (2020). Using Big and Open Data to Analyze Transit-Oriented Development. Journal of the American Planning Association, 86, 364–376.

    Article  Google Scholar 

  • Zhu, P., Yu, T., & Chen, Z. (2015). High-Speed Rail and Urban Decentralization in China. Transportation Research Record, 2475, 16–26.

    Article  Google Scholar 

  • Zhu, Y., Diao, M., & Fu, G. (2016). The evolution of accessibility surface of China in the high-speed-rail era. Environment and Planning a: Economy and Space, 48, 2108–2111.

    Article  Google Scholar 

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Funding

This research is funded by and Natural Science Foundation of Jiangsu Province, China (BK20200109 and BK20211371) and China Postdoctoral Science Foundation (2022M710669).

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Authors and Affiliations

  1. Jiangsu Provincial Planning and Design Group Co., Ltd., Nanjing, China

    Sheng Wei

  2. Key Laboratory of Watershed Geographic Sciences, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing, China

    Lei Wang

  3. Planning and Environmental Management, School of Environment, Education and Development, University of Manchester, Manchester, UK

    Lei Wang

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  1. Sheng Wei
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Wei, S., Wang, L. Classifying High-Speed Rail Stations in the Yangtze River Delta, China: The Node-Place Modelling Approach. Appl. Spatial Analysis 16, 625–646 (2023). https://doi.org/10.1007/s12061-022-09492-w

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