IDEAS home Printed from https://ideas.repec.org/a/eee/trapol/v116y2022icp315-326.html
   My bibliography  Save this article

Understanding the formation of City-HSR network: A case study of Yangtze River Delta, China

Author

Listed:
  • Guo, Ying
  • Cao, Lingyan
  • Song, Ying
  • Wang, Yan
  • Li, Yongkui

Abstract

As an essential transportation infrastructure, high-speed rail (HSR) plays an important role in improving connections and economic efficiency among cities which has been proved by numerous studies. In turn, the cities’ economic development levels also influence the planning and construction of HSR. Several qualitative studies pointed out that some failed projects of HSR investment were attributed by the neglect of urban economic factors. However, there lacks a quantitative understanding of which specific city economic factors influence the formation of HSR networks and to what extent. This study intends to answer the questions by analyzing the formation process of HSR network under the influence of city economic development using a generative network model (GNM) and social network analysis. It focused on 20 cities in the Yangtze River Delta region of China and constructed a City-HSR network that coupled the city economic network and HSR network. Then, a GNM method considering transitivity, distance, boundary effect, and population, was used to generate simulated networks similar to the City-HSR network. The similarities in network metrics between the City-HSR network and the GNM networks were compared by the quadratic assignment procedure (QAP) method to determine the optimal GNM network. A sensitivity analysis was further conducted to investigate the impacts of the factors on the formation of HSR network. The results showed that GNM could simulate the observed City-HSR network with satisfactory performance, and the city population should be given priority when planning new HSR lines. This study proposed a research framework for exploring the interaction between the city economic network and HSR network and provided data-driven analyses for regional transport policy.

Suggested Citation

  • Guo, Ying & Cao, Lingyan & Song, Ying & Wang, Yan & Li, Yongkui, 2022. "Understanding the formation of City-HSR network: A case study of Yangtze River Delta, China," Transport Policy, Elsevier, vol. 116(C), pages 315-326.
  • Handle: RePEc:eee:trapol:v:116:y:2022:i:c:p:315-326
    DOI: 10.1016/j.tranpol.2021.12.014
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0967070X2100367X
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.tranpol.2021.12.014?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Bueno, Gorka & Hoyos, David & Capellán-Pérez, Iñigo, 2017. "Evaluating the environmental performance of the high speed rail project in the Basque Country, Spain," Research in Transportation Economics, Elsevier, vol. 62(C), pages 44-56.
    2. Cascetta, Ennio & Coppola, Pierluigi, 2016. "Assessment of schedule-based and frequency-based assignment models for strategic and operational planning of high-speed rail services," Transportation Research Part A: Policy and Practice, Elsevier, vol. 84(C), pages 93-108.
    3. Begoña Guirao & Natalia Casado-Sanz & Juan Luis Campa, 2020. "Labour opportunities provided by Spanish high-speed rail (HSR) commuting services in a period of financial crisis: an approach based on regional wage disparities and housing rental prices," Regional Studies, Taylor & Francis Journals, vol. 54(4), pages 539-549, April.
    4. Zhang, Anming & Wan, Yulai & Yang, Hangjun, 2019. "Impacts of high-speed rail on airlines, airports and regional economies: A survey of recent research," Transport Policy, Elsevier, vol. 81(C), pages 1-19.
    5. Yang, Haoran & Dobruszkes, Frédéric & Wang, Jiaoe & Dijst, Martin & Witte, Patrick, 2018. "Comparing China's urban systems in high-speed railway and airline networks," Journal of Transport Geography, Elsevier, vol. 68(C), pages 233-244.
    6. Jiao, Jingjuan & Wang, Jiaoe & Jin, Fengjun, 2017. "Impacts of high-speed rail lines on the city network in China," Journal of Transport Geography, Elsevier, vol. 60(C), pages 257-266.
    7. Graziano Abrate & Giampaolo Viglia & Javier Sanchez García & Santiago Forgas-Coll, 2016. "Price Competition within and between Airlines and High-Speed Trains: The Case of the Milan—Rome Route," Tourism Economics, , vol. 22(2), pages 311-323, April.
    8. Jia, Shanming & Zhou, Chunyu & Qin, Chenglin, 2017. "No difference in effect of high-speed rail on regional economic growth based on match effect perspective?," Transportation Research Part A: Policy and Practice, Elsevier, vol. 106(C), pages 144-157.
    9. Li, Yan & Chen, Zhenhua & Wang, Peng, 2020. "Impact of high-speed rail on urban economic efficiency in China," Transport Policy, Elsevier, vol. 97(C), pages 220-231.
    10. Rank, Olaf N., 2008. "Formal structures and informal networks: Structural analysis in organizations," Scandinavian Journal of Management, Elsevier, vol. 24(2), pages 145-161, June.
    11. Gao, Yanyan & Zheng, Jianghuai, 2020. "The impact of high-speed rail on innovation: An empirical test of the companion innovation hypothesis of transportation improvement with China’s manufacturing firms," World Development, Elsevier, vol. 127(C).
    12. Zhenhua Chen & Kingsley E. Haynes, 2015. "Impact of high-speed rail on international tourism demand in China," Applied Economics Letters, Taylor & Francis Journals, vol. 22(1), pages 57-60, January.
    13. Marie Delaplace & Frédéric Dobruszkes, 2015. "From low-cost airlines to low-cost high-speed rail? The French case," ULB Institutional Repository 2013/186381, ULB -- Universite Libre de Bruxelles.
    14. Morley, Clive & Rosselló, Jaume & Santana-Gallego, Maria, 2014. "Gravity models for tourism demand: theory and use," Annals of Tourism Research, Elsevier, vol. 48(C), pages 1-10.
    15. Amparo Moyano & Ana Rivas & Jose M. Coronado, 2019. "Business and tourism high-speed rail same-day trips: factors influencing the efficiency of high-speed rail links for Spanish cities," European Planning Studies, Taylor & Francis Journals, vol. 27(3), pages 533-554, March.
    16. Short, Jack & Kopp, Andreas, 2005. "Transport infrastructure: Investment and planning. Policy and research aspects," Transport Policy, Elsevier, vol. 12(4), pages 360-367, July.
    17. Leheis, Stéphanie, 2012. "High-speed train planning in France: Lessons from the Mediterranean TGV-line," Transport Policy, Elsevier, vol. 21(C), pages 37-44.
    18. Delaplace, Marie & Dobruszkes, Frédéric, 2015. "From low-cost airlines to low-cost high-speed rail? The French case," Transport Policy, Elsevier, vol. 38(C), pages 73-85.
    19. Malighetti, Paolo & Martini, Gianmaria & Redondi, Renato & Scotti, Davide, 2019. "Air transport networks of global integrators in the more liberalized Asian air cargo industry," Transport Policy, Elsevier, vol. 80(C), pages 12-23.
    20. Haoran Yang & Frédéric Dobruszkes & Jiaoe Wang & Martin Dijst & Patrick Wiik, 2018. "Comparing China's urban systems in high-speed railway and airline networks," ULB Institutional Repository 2013/269363, ULB -- Universite Libre de Bruxelles.
    21. Alireza Abbasi & Liaquat Hossain & Shahadat Uddin & Kim J. R. Rasmussen, 2011. "Evolutionary dynamics of scientific collaboration networks: multi-levels and cross-time analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 89(2), pages 687-710, November.
    22. Wang, Lvhua & Liu, Yongxue & Sun, Chao & Liu, Yahui, 2016. "Accessibility impact of the present and future high-speed rail network: A case study of Jiangsu Province, China," Journal of Transport Geography, Elsevier, vol. 54(C), pages 161-172.
    23. Xingjian Liu & Ben Derudder & Yaolin Liu & Frank Witlox & Wei Shen, 2013. "A Stochastic Actor-Based Modelling of the Evolution of an Intercity Corporate Network," Environment and Planning A, , vol. 45(4), pages 947-966, April.
    24. Sandra Vinciguerra & Koen Frenken & Marco Valente, 2010. "The Geography of Internet Infrastructure: An Evolutionary Simulation Approach Based on Preferential Attachment," Urban Studies, Urban Studies Journal Limited, vol. 47(9), pages 1969-1984, August.
    25. Shiwei Lu & Yaping Huang & Zhiyuan Zhao & Xiping Yang, 2018. "Exploring the Hierarchical Structure of China’s Railway Network from 2008 to 2017," Sustainability, MDPI, vol. 10(9), pages 1-15, September.
    26. Liang, Yutian & Zhou, Keyang & Li, Xun & Zhou, Zhengke & Sun, Wei & Zeng, Jiaqi, 2020. "Effectiveness of high-speed railway on regional economic growth for less developed areas," Journal of Transport Geography, Elsevier, vol. 82(C).
    27. Guirao, Begoña, 2013. "Spain: highs and lows of 20years of HSR operation," Journal of Transport Geography, Elsevier, vol. 31(C), pages 201-206.
    28. Moshe Givoni, 2006. "Development and Impact of the Modern High‐speed Train: A Review," Transport Reviews, Taylor & Francis Journals, vol. 26(5), pages 593-611, January.
    29. (Ato) Xu, Wangtu & Zhou, Jiangping & Yang, Linchuan & Li, Ling, 2018. "The implications of high-speed rail for Chinese cities: Connectivity and accessibility," Transportation Research Part A: Policy and Practice, Elsevier, vol. 116(C), pages 308-326.
    30. (Ato) Xu, Wangtu & Huang, Ying, 2019. "The correlation between HSR construction and economic development – Empirical study of Chinese cities," Transportation Research Part A: Policy and Practice, Elsevier, vol. 126(C), pages 24-36.
    31. Derudder, Ben & Witlox, Frank, 2008. "Mapping world city networks through airline flows: context, relevance, and problems," Journal of Transport Geography, Elsevier, vol. 16(5), pages 305-312.
    32. Wang, Feng & Wei, Xianjin & Liu, Juan & He, Lingyun & Gao, Mengnan, 2019. "Impact of high-speed rail on population mobility and urbanisation: A case study on Yangtze River Delta urban agglomeration, China," Transportation Research Part A: Policy and Practice, Elsevier, vol. 127(C), pages 99-114.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Lili Xu & Fanrui Su & Jie Zhang & Na Zhang, 2022. "High-Speed Rail Network Structural Characteristics and Evolution in China," Mathematics, MDPI, vol. 10(18), pages 1-17, September.
    2. Jinxing Hu & Guojie Ma & Chaohai Shen & Xiaolan Zhou, 2022. "Impact of Urbanization through High-Speed Rail on Regional Development with the Interaction of Socioeconomic Factors: A View of Regional Industrial Structure," Land, MDPI, vol. 11(10), pages 1-21, October.
    3. Junhui Shi & Fang Wang, 2022. "The Effect of High-Speed Rail on Cropland Abandonment in China," Land, MDPI, vol. 11(7), pages 1-16, July.
    4. Wen Yang & Quanliang Chen & Jing Yang, 2022. "Factors Affecting Travel Mode Choice between High-Speed Railway and Road Passenger Transport—Evidence from China," Sustainability, MDPI, vol. 14(23), pages 1-17, November.
    5. Minghua Chen & Tengwen Zhang & Qinru Chu & Linxiao Xie & Jianxu Liu & Roengchai Tansuchat & You Geng, 2023. "Convergence Analysis of Inclusive Green Growth in China Based on the Spatial Correlation Network," Sustainability, MDPI, vol. 15(16), pages 1-21, August.
    6. Chen, Fanglin & Chen, Zhongfei, 2023. "High-speed rail and happiness," Transportation Research Part A: Policy and Practice, Elsevier, vol. 170(C).
    7. Guojie Ma & Jinxing Hu & Riquan Zhang, 2023. "Spatial-Temporal Distribution and Coupling Relationship of High-Speed Railway and Economic Networks in Metropolitan Areas of China," Land, MDPI, vol. 12(6), pages 1-23, June.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Huang, Yan & Zong, Huiming, 2020. "The spatial distribution and determinants of China’s high-speed train services," Transportation Research Part A: Policy and Practice, Elsevier, vol. 142(C), pages 56-70.
    2. Mohsen Momenitabar & Raj Bridgelall & Zhila Dehdari Ebrahimi & Mohammad Arani, 2021. "Literature Review of Socioeconomic and Environmental Impacts of High-Speed Rail in the World," Sustainability, MDPI, vol. 13(21), pages 1-27, November.
    3. Chen, Fanglin & Hao, Xinyue & Chen, Zhongfei, 2021. "Can high-speed rail improve health and alleviate health inequality? Evidence from China," Transport Policy, Elsevier, vol. 114(C), pages 266-279.
    4. Mohsen Momenitabar & Zhila Dehdari Ebrahimi & Mohammad Arani, 2020. "A Systematic and Analytical Review of the Socioeconomic and Environmental Impact of the Deployed High-Speed Rail (HSR) Systems on the World," Papers 2003.04452, arXiv.org, revised Mar 2020.
    5. Liu, Shuli & Wan, Yulai & Zhang, Anming, 2020. "Does China’s high-speed rail development lead to regional disparities? A network perspective," Transportation Research Part A: Policy and Practice, Elsevier, vol. 138(C), pages 299-321.
    6. Yanan Jin & Guoli Ou, 2023. "The Impacts of High-Speed Rail on Producer Service Industry Agglomeration: Evidence from China’s Yangtze River Delta Urban Agglomeration," Sustainability, MDPI, vol. 15(4), pages 1-16, February.
    7. Huang, Yan & Zong, Huiming, 2022. "The intercity railway connections in China: A comparative analysis of high-speed train and conventional train services," Transport Policy, Elsevier, vol. 120(C), pages 89-103.
    8. Huang, Ying & Xu, Wangtu (Ato), 2021. "Spatial and temporal heterogeneity of the impact of high-speed railway on urban economy: Empirical study of Chinese cities," Journal of Transport Geography, Elsevier, vol. 91(C).
    9. (Ato) Xu, Wangtu & Huang, Ying, 2019. "The correlation between HSR construction and economic development – Empirical study of Chinese cities," Transportation Research Part A: Policy and Practice, Elsevier, vol. 126(C), pages 24-36.
    10. Wang, Jiaoe & Du, Delin & Huang, Jie, 2020. "Inter-city connections in China: High-speed train vs. inter-city coach," Journal of Transport Geography, Elsevier, vol. 82(C).
    11. Yang, Zhiwei & Li, Can & Jiao, Jingjuan & Liu, Wei & Zhang, Fangni, 2020. "On the joint impact of high-speed rail and megalopolis policy on regional economic growth in China," Transport Policy, Elsevier, vol. 99(C), pages 20-30.
    12. (Ato) Xu, Wangtu & Zhou, Jiangping & Yang, Linchuan & Li, Ling, 2018. "The implications of high-speed rail for Chinese cities: Connectivity and accessibility," Transportation Research Part A: Policy and Practice, Elsevier, vol. 116(C), pages 308-326.
    13. Li, Hui & Dong, Xiucheng & Jiang, Qingzhe & Dong, Kangyin, 2021. "Policy analysis for high-speed rail in China: Evolution, evaluation, and expectation," Transport Policy, Elsevier, vol. 106(C), pages 37-53.
    14. Yang, Haoran & Dobruszkes, Frédéric & Wang, Jiaoe & Dijst, Martin & Witte, Patrick, 2018. "Comparing China's urban systems in high-speed railway and airline networks," Journal of Transport Geography, Elsevier, vol. 68(C), pages 233-244.
    15. Asep Yayat Nurhidayat & Hera Widyastuti & Sutikno & Dwi Phalita Upahita, 2023. "Research on Passengers’ Preferences and Impact of High-Speed Rail on Air Transport Demand," Sustainability, MDPI, vol. 15(4), pages 1-26, February.
    16. Wen Yang & Quanliang Chen & Jing Yang, 2022. "Factors Affecting Travel Mode Choice between High-Speed Railway and Road Passenger Transport—Evidence from China," Sustainability, MDPI, vol. 14(23), pages 1-17, November.
    17. Zhang, Anming & Wan, Yulai & Yang, Hangjun, 2019. "Impacts of high-speed rail on airlines, airports and regional economies: A survey of recent research," Transport Policy, Elsevier, vol. 81(C), pages 1-19.
    18. Zhang, Fangni & Yang, Zhiwei & Jiao, Jingjuan & Liu, Wei & Wu, Wenjie, 2020. "The effects of high-speed rail development on regional equity in China," Transportation Research Part A: Policy and Practice, Elsevier, vol. 141(C), pages 180-202.
    19. Yu, Danlin & Murakami, Daisuke & Zhang, Yaojun & Wu, Xiwei & Li, Ding & Wang, Xiaoxi & Li, Guangdong, 2020. "Investigating high-speed rail construction's support to county level regional development in China: An eigenvector based spatial filtering panel data analysis," Transportation Research Part B: Methodological, Elsevier, vol. 133(C), pages 21-37.
    20. Haoran Zhang & Ying Chai & Xuyu Yang & Wenli Zhao, 2022. "High-Speed Rail and Urban Growth Disparity: Evidence from China," Sustainability, MDPI, vol. 14(13), pages 1-13, July.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:trapol:v:116:y:2022:i:c:p:315-326. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/30473/description#description .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.