IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v11y2019i19p5404-d272119.html
   My bibliography  Save this article

Exploring the Topological Characteristics of Complex Public Transportation Networks: Focus on Variations in Both Single and Integrated Systems in the Seoul Metropolitan Area

Author

Listed:
  • Jungyeol Hong

    (Department of Transportation Engineering, University of Seoul, Seoul 02504, Korea)

  • Reuben Tamakloe

    (Department of Transportation Engineering, University of Seoul, Seoul 02504, Korea)

  • Soobeom Lee

    (Department of Transportation Engineering, University of Seoul, Seoul 02504, Korea)

  • Dongjoo Park

    (Department of Transportation Engineering, University of Seoul, Seoul 02504, Korea)

Abstract

Many cities have integrated their public transportation modes to provide increased accessibility and reduced commute times. However, current transport network topology studies have focused on unimodal networks. Therefore, it is of significant interest for policymakers to examine the topology of integrated public transportation networks and to assess strategies for improving them. The objective of this study was to discuss a comprehensive analysis of an integrated public transportation network using graph theory, compare its characteristics to unimodal networks, and draw insights for improving their performance. Results demonstrate pertinent information concerning the structural composition of the Seoul Metropolitan Area’s (SMA) public transportation network. Despite the integration, the spatial configuration of the network was found to have low fault tolerance. However, the highly agglomerated community structure validated the robustness of integrated networks. Network centrality measures confirmed that integration improves connectivity and spatial accessibility to suburbs within the city. The study found that the SMA’s current public transportation network possesses structural defects that need to be addressed to improve its resilience and performance. Based on the outcomes of this study, the strategic creation or relocation of stations, and the construction of more links, is imperative for the enhancement of mobility.

Suggested Citation

  • Jungyeol Hong & Reuben Tamakloe & Soobeom Lee & Dongjoo Park, 2019. "Exploring the Topological Characteristics of Complex Public Transportation Networks: Focus on Variations in Both Single and Integrated Systems in the Seoul Metropolitan Area," Sustainability, MDPI, vol. 11(19), pages 1-26, September.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:19:p:5404-:d:272119
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/11/19/5404/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/11/19/5404/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Soh, Harold & Lim, Sonja & Zhang, Tianyou & Fu, Xiuju & Lee, Gary Kee Khoon & Hung, Terence Gih Guang & Di, Pan & Prakasam, Silvester & Wong, Limsoon, 2010. "Weighted complex network analysis of travel routes on the Singapore public transportation system," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(24), pages 5852-5863.
    2. Jiang, Ruoyun & Lu, Qing-Chang & Peng, Zhong-Ren, 2018. "A station-based rail transit network vulnerability measure considering land use dependency," Journal of Transport Geography, Elsevier, vol. 66(C), pages 10-18.
    3. Liu, Jian-Guo & Ren, Zhuo-Ming & Guo, Qiang, 2013. "Ranking the spreading influence in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(18), pages 4154-4159.
    4. Yasir Tariq Mohmand & Aihu Wang, 2014. "Complex Network Analysis of Pakistan Railways," Discrete Dynamics in Nature and Society, Hindawi, vol. 2014, pages 1-5, March.
    5. Seongman Jang & Youngsoo An & Changhyo Yi & Seungil Lee, 2017. "Assessing the spatial equity of Seoul’s public transportation using the Gini coefficient based on its accessibility," International Journal of Urban Sciences, Taylor & Francis Journals, vol. 21(1), pages 91-107, January.
    6. Zhuangbin Shi & Ning Zhang & Yang Liu & Wei Xu, 2018. "Exploring Spatiotemporal Variation in Hourly Metro Ridership at Station Level: The Influence of Built Environment and Topological Structure," Sustainability, MDPI, vol. 10(12), pages 1-16, December.
    7. Du, Yuxian & Gao, Cai & Hu, Yong & Mahadevan, Sankaran & Deng, Yong, 2014. "A new method of identifying influential nodes in complex networks based on TOPSIS," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 399(C), pages 57-69.
    8. Shanmukhappa, Tanuja & Ho, Ivan Wang-Hei & Tse, Chi Kong, 2018. "Spatial analysis of bus transport networks using network theory," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 502(C), pages 295-314.
    9. Mishra, Sabyasachee & Welch, Timothy F. & Jha, Manoj K., 2012. "Performance indicators for public transit connectivity in multi-modal transportation networks," Transportation Research Part A: Policy and Practice, Elsevier, vol. 46(7), pages 1066-1085.
    10. Yang, Yu & He, Ze & Song, Zouying & Fu, Xin & Wang, Jianwei, 2018. "Investigation on structural and spatial characteristics of taxi trip trajectory network in Xi’an, China," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 506(C), pages 755-766.
    11. Qing-Chang Lu & Shan Lin, 2019. "Vulnerability Analysis of Urban Rail Transit Network within Multi-Modal Public Transport Networks," Sustainability, MDPI, vol. 11(7), pages 1-14, April.
    12. Zhang, Lin & Lu, Jian & Fu, Bai-bai & Li, Shu-bin, 2019. "Dynamics analysis for the hour-scale based time-varying characteristic of topology complexity in a weighted urban rail transit network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 527(C).
    13. Liu, Jun & Xiong, Qingyu & Shi, Weiren & Shi, Xin & Wang, Kai, 2016. "Evaluating the importance of nodes in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 452(C), pages 209-219.
    14. Chen, Duanbing & Lü, Linyuan & Shang, Ming-Sheng & Zhang, Yi-Cheng & Zhou, Tao, 2012. "Identifying influential nodes in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(4), pages 1777-1787.
    15. Jiangang Shi & Shiping Wen & Xianbo Zhao & Guangdong Wu, 2019. "Sustainable Development of Urban Rail Transit Networks: A Vulnerability Perspective," Sustainability, MDPI, vol. 11(5), pages 1-24, March.
    16. Cervero, Robert & Kang, Chang Deok, 2011. "Bus rapid transit impacts on land uses and land values in Seoul, Korea," Transport Policy, Elsevier, vol. 18(1), pages 102-116, January.
    17. Sybil Derrible, 2012. "Network Centrality of Metro Systems," PLOS ONE, Public Library of Science, vol. 7(7), pages 1-10, July.
    18. John C. Sutton, 2005. "GIS Applications in Transit Planning and Operations: A Review of Current Practice, Effective Applications and Challenges in the USA," Transportation Planning and Technology, Taylor & Francis Journals, vol. 28(4), pages 237-250, July.
    19. Bae, Joonhyun & Kim, Sangwook, 2014. "Identifying and ranking influential spreaders in complex networks by neighborhood coreness," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 395(C), pages 549-559.
    20. Aziz, A. & Nawaz, M.S. & Nadeem, M. & Afzal, L., 2018. "Examining suitability of the integrated public transport system: A case study of Lahore," Transportation Research Part A: Policy and Practice, Elsevier, vol. 117(C), pages 13-25.
    21. Andrea Landherr & Bettina Friedl & Julia Heidemann, 2010. "A Critical Review of Centrality Measures in Social Networks," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 2(6), pages 371-385, December.
    22. Wei Yu & Jun Chen & Xingchen Yan, 2019. "Space‒Time Evolution Analysis of the Nanjing Metro Network Based on a Complex Network," Sustainability, MDPI, vol. 11(2), pages 1-17, January.
    23. Jingyi Lin & Yifang Ban, 2013. "Complex Network Topology of Transportation Systems," Transport Reviews, Taylor & Francis Journals, vol. 33(6), pages 658-685, November.
    24. Rui Ding & Norsidah Ujang & Hussain bin Hamid & Jianjun Wu, 2015. "Complex Network Theory Applied to the Growth of Kuala Lumpur’s Public Urban Rail Transit Network," PLOS ONE, Public Library of Science, vol. 10(10), pages 1-22, October.
    25. Anthony Chen & Chao Yang & Sirisak Kongsomsaksakul & Ming Lee, 2007. "Network-based Accessibility Measures for Vulnerability Analysis of Degradable Transportation Networks," Networks and Spatial Economics, Springer, vol. 7(3), pages 241-256, September.
    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. Kopsidas, Athanasios & Kepaptsoglou, Konstantinos, 2022. "Identification of critical stations in a Metro System: A substitute complex network analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 596(C).
    2. Yangyang Meng & Qingjie Qi & Jianzhong Liu & Wei Zhou, 2022. "Dynamic Evolution Analysis of Complex Topology and Node Importance in Shenzhen Metro Network from 2004 to 2021," Sustainability, MDPI, vol. 14(12), pages 1-19, June.
    3. Kim, Minjun & Cho, Gi-Hyoug, 2021. "Analysis on bike-share ridership for origin-destination pairs: Effects of public transit route characteristics and land-use patterns," Journal of Transport Geography, Elsevier, vol. 93(C).
    4. Mingming Zheng & Hanzhang Zuo & Zitong Zhou & Yuhan Bai, 2023. "Recovery Strategies for Urban Rail Transit Network Based on Comprehensive Resilience," Sustainability, MDPI, vol. 15(20), pages 1-17, October.
    5. Yajun Xiong & Hui Tang & Xiaobo Tian, 2022. "Research on Structural Toughness of Railway City Network in Yellow River Basin and Case Study of Zhengzhou 7–20 Rainstorm Disaster," Sustainability, MDPI, vol. 14(19), pages 1-17, September.
    6. Carlos Rosa-Jiménez & Sergio Gutiérrez-Coronil & María José Márquez-Ballesteros & Alberto E. García-Moreno, 2023. "Relating Spatial Quality of Public Transportation and the Most Visited Museums: Revisiting Sustainable Mobility of Waterfronts and Historic Centers in International Cruise Destinations," Sustainability, MDPI, vol. 15(3), pages 1-23, January.
    7. Qingjie Qi & Yangyang Meng & Xiaofei Zhao & Jianzhong Liu, 2022. "Resilience Assessment of an Urban Metro Complex Network: A Case Study of the Zhengzhou Metro," Sustainability, MDPI, vol. 14(18), pages 1-19, September.
    8. Yuchen Qin & Yikang Zhang & Minfeng Yao & Qiwei Chen, 2023. "How to Measure the Impact of Walking Accessibility of Suburban Rail Station Catchment Areas on the Commercial Premium Benefits of Joint Development," Sustainability, MDPI, vol. 15(6), pages 1-29, March.

    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. Caterina Malandri & Luca Mantecchini & Filippo Paganelli & Maria Nadia Postorino, 2021. "Public Transport Network Vulnerability and Delay Distribution among Travelers," Sustainability, MDPI, vol. 13(16), pages 1-14, August.
    2. Wei, Bo & Liu, Jie & Wei, Daijun & Gao, Cai & Deng, Yong, 2015. "Weighted k-shell decomposition for complex networks based on potential edge weights," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 420(C), pages 277-283.
    3. Lu, Qing-Chang & Zhang, Lei & Xu, Peng-Cheng & Cui, Xin & Li, Jing, 2022. "Modeling network vulnerability of urban rail transit under cascading failures: A Coupled Map Lattices approach," Reliability Engineering and System Safety, Elsevier, vol. 221(C).
    4. Elisa Frutos Bernal & Angel Martín del Rey, 2019. "Study of the Structural and Robustness Characteristics of Madrid Metro Network," Sustainability, MDPI, vol. 11(12), pages 1-24, June.
    5. Zhang, Mengyao & Huang, Tao & Guo, Zhaoxia & He, Zhenggang, 2022. "Complex-network-based traffic network analysis and dynamics: A comprehensive review," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 607(C).
    6. Yangyang Meng & Qingjie Qi & Jianzhong Liu & Wei Zhou, 2022. "Dynamic Evolution Analysis of Complex Topology and Node Importance in Shenzhen Metro Network from 2004 to 2021," Sustainability, MDPI, vol. 14(12), pages 1-19, June.
    7. Hu, Jiantao & Du, Yuxian & Mo, Hongming & Wei, Daijun & Deng, Yong, 2016. "A modified weighted TOPSIS to identify influential nodes in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 444(C), pages 73-85.
    8. Mahyar, Hamidreza & Hasheminezhad, Rouzbeh & Ghalebi K., Elahe & Nazemian, Ali & Grosu, Radu & Movaghar, Ali & Rabiee, Hamid R., 2018. "Compressive sensing of high betweenness centrality nodes in networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 497(C), pages 166-184.
    9. Namtirtha, Amrita & Dutta, Animesh & Dutta, Biswanath, 2018. "Identifying influential spreaders in complex networks based on kshell hybrid method," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 499(C), pages 310-324.
    10. Yu, Senbin & Gao, Liang & Xu, Lida & Gao, Zi-You, 2019. "Identifying influential spreaders based on indirect spreading in neighborhood," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 523(C), pages 418-425.
    11. Wang, Zhixiao & Zhao, Ya & Xi, Jingke & Du, Changjiang, 2016. "Fast ranking influential nodes in complex networks using a k-shell iteration factor," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 461(C), pages 171-181.
    12. Zareie, Ahmad & Sheikhahmadi, Amir, 2019. "EHC: Extended H-index Centrality measure for identification of users’ spreading influence in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 514(C), pages 141-155.
    13. Fu, Yu-Hsiang & Huang, Chung-Yuan & Sun, Chuen-Tsai, 2015. "Using global diversity and local topology features to identify influential network spreaders," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 433(C), pages 344-355.
    14. Liu, Jun & Xiong, Qingyu & Shi, Weiren & Shi, Xin & Wang, Kai, 2016. "Evaluating the importance of nodes in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 452(C), pages 209-219.
    15. Ma, Qian & Ma, Jun, 2017. "Identifying and ranking influential spreaders in complex networks with consideration of spreading probability," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 465(C), pages 312-330.
    16. Du, Zhouyang & Tang, Jinjun & Qi, Yong & Wang, Yiwei & Han, Chunyang & Yang, Yifan, 2020. "Identifying critical nodes in metro network considering topological potential: A case study in Shenzhen city—China," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 539(C).
    17. Calzada-Infante, L. & Adenso-Díaz, B. & García Carbajal, S., 2020. "Analysis of the European international railway network and passenger transfers," Chaos, Solitons & Fractals, Elsevier, vol. 141(C).
    18. Bao, Zhong-Kui & Ma, Chuang & Xiang, Bing-Bing & Zhang, Hai-Feng, 2017. "Identification of influential nodes in complex networks: Method from spreading probability viewpoint," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 468(C), pages 391-397.
    19. Wang, Junyi & Hou, Xiaoni & Li, Kezan & Ding, Yong, 2017. "A novel weight neighborhood centrality algorithm for identifying influential spreaders in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 475(C), pages 88-105.
    20. Teqi Dai & Tiantian Ding & Qingfang Liu & Bingxin Liu, 2022. "Node Centrality Comparison between Bus Line and Passenger Flow Networks in Beijing," Sustainability, MDPI, vol. 14(22), pages 1-14, November.

    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:11:y:2019:i:19:p:5404-:d:272119. 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.