IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i8p6450-d1120340.html
   My bibliography  Save this article

Urban Agglomeration High-Speed Railway Backbone Network Planning: A Case Study of Beijing-Tianjin-Hebei Region, China

Author

Listed:
  • Jun Zhao

    (School of Transportation Engineering, Dalian Jiaotong University, Dalian 116028, China)

  • Wenyu Rong

    (School of Transportation Engineering, Dalian Jiaotong University, Dalian 116028, China)

  • Di Liu

    (School of Transportation Engineering, Dalian Jiaotong University, Dalian 116028, China)

Abstract

In order to optimize the network layout of urban agglomerations, improve the comprehensive benefits of transportation networks and promote the sustainable development of urban agglomerations, this paper studies the main trunk line selection model of the Beijing–Tianjin–Hebei high-speed railway (HSR). Firstly, the characteristics of cities in urban agglomeration are analyzed, and the economic capacity, transportation capacity, passenger turnover and network characteristics of urban nodes are selected as evaluation indexes. A node importance model and a line urgency model were established to obtain the value of the importance of urban nodes and the urgency of each line in the urban agglomeration. Secondly, the DBSCAN is used to cluster the city nodes, and the city nodes are divided into four grades. With the goal of maximizing the urgency of the lines and considering the constraints of the urban node level, the optimization model of the Beijing–Tianjin–Hebei backbone network selection is constructed. The backbone lines of the Beijing–Tianjin–Hebei urban agglomeration are obtained, and the selection results of backbone lines are analyzed, which lays a foundation for the design and optimization of the HSR operation scheme in urban agglomeration. The planned backbone network can basically realize the commuting between the important urban nodes in the Beijing–Tianjin–Hebei urban agglomeration to achieve the goal of driving and alleviating the operation of the branch line. It can accelerate the development of the internal traffic of the urban agglomeration. In addition, it has certain practical significance and practical value.

Suggested Citation

  • Jun Zhao & Wenyu Rong & Di Liu, 2023. "Urban Agglomeration High-Speed Railway Backbone Network Planning: A Case Study of Beijing-Tianjin-Hebei Region, China," Sustainability, MDPI, vol. 15(8), pages 1-22, April.
  • Handle: RePEc:gam:jsusta:v:15:y:2023:i:8:p:6450-:d:1120340
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/8/6450/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/15/8/6450/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Su, Qingyu & Chen, Cong & Huang, Xin & Li, Jian, 2022. "Interval TrendRank method for grid node importance assessment considering new energy," Applied Energy, Elsevier, vol. 324(C).
    2. Xu, Fei & Li, Qiangyi & Yang, Mian, 2022. "The impacts of high-speed rail on the transformation of resource-based cities in China: A market segmentation perspective," Resources Policy, Elsevier, vol. 78(C).
    3. Danyu Liu & Ke Zhang, 2022. "Analysis of Spatial Differences and the Influencing Factors in Eco-Efficiency of Urban Agglomerations in China," Sustainability, MDPI, vol. 14(19), pages 1-21, October.
    4. Peiqing Li & Longlong Jiang & Shunfeng Zhang & Xi Jiang, 2022. "Demand Response Transit Scheduling Research Based on Urban and Rural Transportation Station Optimization," Sustainability, MDPI, vol. 14(20), pages 1-17, October.
    5. Attila Mester & Andrei Pop & Bogdan-Eduard-Mădălin Mursa & Horea Greblă & Laura Dioşan & Camelia Chira, 2021. "Network Analysis Based on Important Node Selection and Community Detection," Mathematics, MDPI, vol. 9(18), pages 1-16, September.
    6. Rui Ding & Jun Fu & Yiming Du & Linyu Du & Tao Zhou & Yilin Zhang & Siwei Shen & Yuqi Zhu & Shihui Chen, 2022. "Structural Evolution and Community Detection of China Rail Transit Route Network," Sustainability, MDPI, vol. 14(19), pages 1-19, September.
    7. Gu, Hongyi & Wan, Yulai, 2022. "Airline reactions to high-speed rail entry: Rail quality and market structure," Transportation Research Part A: Policy and Practice, Elsevier, vol. 165(C), pages 511-532.
    8. Claessens, M. T. & van Dijk, N. M. & Zwaneveld, P. J., 1998. "Cost optimal allocation of rail passenger lines," European Journal of Operational Research, Elsevier, vol. 110(3), pages 474-489, November.
    9. Shalvi Sharma & Sewa Ram, 2023. "Investigation of Road Network Connectivity and Accessibility in Less Accessible Airport Regions: The Case of India," Sustainability, MDPI, vol. 15(7), pages 1-15, March.
    10. Xuyang Han & Costas Armenakis & Mojgan Jadidi, 2021. "Modeling Vessel Behaviours by Clustering AIS Data Using Optimized DBSCAN," Sustainability, MDPI, vol. 13(15), pages 1-22, July.
    11. Yafei Xu & Guoli Ou, 2022. "Does High-Speed Railway Promote the Level of Human Capital? An Empirical Analysis Based on Three Urban Agglomerations in China," Sustainability, MDPI, vol. 14(19), pages 1-17, October.
    12. Congmou Zhu & Lixia Yang & Qiuyu Xu & Jinwei Fu & Yue Lin & Le Sun & Shan He & Shaofeng Yuan, 2022. "A Comparative Analysis of Farmland Occupation by Urban Sprawl and Rural Settlement Expansion in China," Land, MDPI, vol. 11(10), pages 1-16, October.
    13. Cuiping Ren & Bianbian Chen & Fengjie Xie & Xuan Zhao & Jiaqian Zhang & Xueyan Zhou, 2022. "Understanding Hazardous Materials Transportation Accidents Based on Higher-Order Network Theory," IJERPH, MDPI, vol. 19(20), pages 1-13, October.
    14. Jian Wang & Yuzhou Deng & Sonia Kumari & Zhihui Song, 2023. "Research on the Spatial Spillover Effect of Transportation Infrastructure on Urban Resilience in Three Major Urban Agglomerations in China," Sustainability, MDPI, vol. 15(6), pages 1-21, March.
    Full references (including those not matched with items on IDEAS)

    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. Li, Mengxu & Liu, Jianghua & Chen, Yang & Yang, Zhijiu, 2023. "Can sustainable development strategy reduce income inequality in resource-based regions? A natural resource dependence perspective," Resources Policy, Elsevier, vol. 81(C).
    2. Bugarinovic, Mirjana & Boskovic, Branislav, 2015. "A systems approach to access charges in unbundling railways," European Journal of Operational Research, Elsevier, vol. 240(3), pages 848-860.
    3. Eliasson, Jonas & Börjesson, Maria, 2014. "On timetable assumptions in railway investment appraisal," Transport Policy, Elsevier, vol. 36(C), pages 118-126.
    4. Eisuke Watanabe & Ryuichi Shibasaki, 2023. "Extraction of Bunkering Services from Automatic Identification System Data and Their International Comparisons," Sustainability, MDPI, vol. 15(24), pages 1-19, December.
    5. Masing, Berenike & Lindner, Niels & Borndörfer, Ralf, 2022. "The price of symmetric line plans in the Parametric City," Transportation Research Part B: Methodological, Elsevier, vol. 166(C), pages 419-443.
    6. Mathias Michaelis & Anita Schöbel, 2009. "Integrating line planning, timetabling, and vehicle scheduling: a customer-oriented heuristic," Public Transport, Springer, vol. 1(3), pages 211-232, August.
    7. Xu, Fei & Liu, Qian & Zheng, Xingdong & Cao, Luqi & Yang, Mian, 2022. "Research on the impact of China's high-speed rail opening on enterprise market power: Based on the perspective of market segmentation," Transport Policy, Elsevier, vol. 128(C), pages 121-137.
    8. Huan Lu & Ruiyang Wang & Rong Ye & Jinzhao Fan, 2023. "Monitoring Long-Term Spatiotemporal Dynamics of Urban Expansion Using Multisource Remote Sensing Images and Historical Maps: A Case Study of Hangzhou, China," Land, MDPI, vol. 12(1), pages 1-23, January.
    9. Wenliang Zhou & Mehdi Oldache, 2021. "Integrated Optimization of Line Planning, Timetabling and Rolling Stock Allocation for Urban Railway Lines," Sustainability, MDPI, vol. 13(23), pages 1-32, November.
    10. Guan, J.F. & Yang, Hai & Wirasinghe, S.C., 2006. "Simultaneous optimization of transit line configuration and passenger line assignment," Transportation Research Part B: Methodological, Elsevier, vol. 40(10), pages 885-902, December.
    11. Pu, Song & Zhan, Shuguang, 2021. "Two-stage robust railway line-planning approach with passenger demand uncertainty," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 152(C).
    12. Su, Yifan & Xu, Guanghua, 2022. "Low-carbon transformation of natural resource industry in China: Determinants and policy implications to achieve COP26 targets," Resources Policy, Elsevier, vol. 79(C).
    13. Evelien van der Hurk & Haris N. Koutsopoulos & Nigel Wilson & Leo G. Kroon & Gábor Maróti, 2016. "Shuttle Planning for Link Closures in Urban Public Transport Networks," Transportation Science, INFORMS, vol. 50(3), pages 947-965, August.
    14. Philine Gattermann & Jonas Harbering & Anita Schöbel, 2017. "Line pool generation," Public Transport, Springer, vol. 9(1), pages 7-32, July.
    15. Jorge Ramos & Benjamin Drakeford & Ana Madiedo & Joana Costa & Francisco Leitão, 2024. "A Bayesian Approach to Infer the Sustainable Use of Artificial Reefs in Fisheries and Recreation," Sustainability, MDPI, vol. 16(2), pages 1-16, January.
    16. Chengqing Liu & Dan Yang & Jun Sun & Yu Cheng, 2023. "The Impact of Environmental Regulations on Pollution and Carbon Reduction in the Yellow River Basin, China," IJERPH, MDPI, vol. 20(3), pages 1-21, January.
    17. Wang, Kai & Chen, Xi & Wang, Chenye, 2023. "The impact of sustainable development planning in resource-based cities on corporate ESG–Evidence from China," Energy Economics, Elsevier, vol. 127(PA).
    18. Zhang, Yixiang & Fu, Bowen, 2023. "Social trust contributes to the reduction of urban carbon dioxide emissions," Energy, Elsevier, vol. 279(C).
    19. Huang, Yingshan & Ouyang, Haiqin & Pan, Weihua & He, Xiaogang, 2023. "Role of high-speed rail services in China’s economic recovery: Evidence from manufacturing firm inventories," Economic Analysis and Policy, Elsevier, vol. 78(C), pages 389-405.
    20. Xudong Li & Zhongzhen Yang & Feng Lian, 2023. "Optimizing On-Demand Bus Services for Remote Areas," Sustainability, MDPI, vol. 15(9), pages 1-20, April.

    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:gam:jsusta:v:15:y:2023:i:8:p:6450-:d:1120340. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.