IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v502y2018icp295-314.html
   My bibliography  Save this article

Spatial analysis of bus transport networks using network theory

Author

Listed:
  • Shanmukhappa, Tanuja
  • Ho, Ivan Wang-Hei
  • Tse, Chi Kong

Abstract

In this paper, we analyze the bus transport network (BTN) structure considering the spatial embedding of the network for three cities, namely, Hong Kong (HK), London (LD), and Bengaluru (BL). We propose a novel approach called supernode graph structuring for modeling the bus transport network. A static demand estimation procedure is proposed to assign the node weights by considering the points of interests (POIs) and the population distribution in the city over various localized zones. In addition, the end-to-end delay is proposed as a parameter to measure the topological efficiency of the bus networks instead of the shortest distance measure used in previous works. With the aid of supernode graph representation, important network parameters are analyzed for the directed, weighted and geo-referenced bus transport networks. It is observed that the supernode concept has significant advantage in analyzing the inherent topological behavior. For instance, the scale-free and small-world behavior becomes evident with supernode representation as compared to conventional or regular graph representation for the Hong Kong network. Significant improvement in clustering, reduction in path length, and increase in centrality values are observed in all the three networks with supernode representation. The correlation between topologically central nodes and the geographically central nodes reveals the interesting fact that the proposed static demand estimation method for assigning node weights aids in better identifying the geographically significant nodes in the network. The impact of these geographically significant nodes on the local traffic behavior is demonstrated by simulation using the SUMO (Simulation of Urban Mobility) tool which is also supported by real-world empirical data, and our results indicate that the traffic speed around a particular bus stop can reach a jammed state from a free flow state due to the presence of these geographically important nodes. A comparison of the simulation and the empirical data provides useful information on how bus operators can better plan their routes and deploy stops considering the geographically significant nodes.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:phsmap:v:502:y:2018:i:c:p:295-314
    DOI: 10.1016/j.physa.2018.02.111
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437118302024
    Download Restriction: Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000
    File URL: https://libkey.io/10.1016/j.physa.2018.02.111?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. 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. Chen, Yong-Zhou & Li, Nan & He, Da-Ren, 2007. "A study on some urban bus transport networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 376(C), pages 747-754.
    3. C. von Ferber & T. Holovatch & Yu. Holovatch & V. Palchykov, 2009. "Public transport networks: empirical analysis and modeling," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 68(2), pages 261-275, March.
    4. Daganzo, Carlos F., 2010. "Structure of competitive transit networks," Transportation Research Part B: Methodological, Elsevier, vol. 44(4), pages 434-446, May.
    5. Latora, Vito & Marchiori, Massimo, 2002. "Is the Boston subway a small-world network?," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 314(1), pages 109-113.
    6. Feng, Shumin & Hu, Baoyu & Nie, Cen & Shen, Xianghao, 2016. "Empirical study on a directed and weighted bus transport network in China," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 441(C), pages 85-92.
    7. Xu, Xinping & Hu, Junhui & Liu, Feng & Liu, Lianshou, 2007. "Scaling and correlations in three bus-transport networks of China," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 374(1), pages 441-448.
    8. Leo Katz, 1953. "A new status index derived from sociometric analysis," Psychometrika, Springer;The Psychometric Society, vol. 18(1), pages 39-43, March.
    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. Ruijin Du & Qi Wu & Ziwei Nan & Gaogao Dong & Lixin Tian & Feifan Wu, 2022. "Natural Gas Scarcity Risk in the Belt and Road Economies Based on Complex Network and Multi-Regional Input-Output Analysis," Mathematics, MDPI, vol. 10(5), pages 1-16, March.
    2. Wang, Zhiru & Niu, Fangyan & Yang, Lili & Su, Guofeng, 2020. "Modeling a subway network: A hot-point attraction-driven evolution mechanism," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 537(C).
    3. 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.
    4. Feng, Xiao & He, Shiwei & Li, Guangye & Chi, Jushang, 2021. "Transfer network of high-speed rail and aviation: Structure and critical components," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 581(C).
    5. Meng, Yangyang & Tian, Xiangliang & Li, Zhongwen & Zhou, Wei & Zhou, Zhijie & Zhong, Maohua, 2020. "Exploring node importance evolution of weighted complex networks in urban rail transit," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 558(C).
    6. Tang, Miaohan & Hong, Jingke & Liu, Guiwen & Shen, Geoffrey Qiping, 2019. "Exploring energy flows embodied in China's economy from the regional and sectoral perspectives via combination of multi-regional input–output analysis and a complex network approach," Energy, Elsevier, vol. 170(C), pages 1191-1201.
    7. Sheng Wei & Lei Wang, 2020. "Examining the population flow network in China and its implications for epidemic control based on Baidu migration data," Palgrave Communications, Palgrave Macmillan, vol. 7(1), pages 1-10, December.
    8. Shen, Jingwei & Zong, Huiming, 2023. "Identification of critical transportation cities in the multimodal transportation network of China," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 628(C).
    9. Jia, Tao & Liu, Wenxuan & Liu, Xintao, 2021. "A cross-city exploratory analysis of the robustness of bus transit networks using open-source data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 580(C).
    10. Chen Zhang & Yichen Liang & Tian Tian & Peng Peng, 2024. "Sustainable Transportation: Exploring the Node Importance Evolution of Rail Transit Networks during Peak Hours," Sustainability, MDPI, vol. 16(16), pages 1-22, August.
    11. Szymula, Christopher & Bešinović, Nikola, 2020. "Passenger-centered vulnerability assessment of railway networks," Transportation Research Part B: Methodological, Elsevier, vol. 136(C), pages 30-61.
    12. Manjalavil, Manju Manohar & Ramadurai, Gitakrishnan, 2020. "Topological properties of bus transit networks considering demand and service utilization weight measures," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 555(C).
    13. Wei, Sheng & Zheng, Wei & Wang, Lei, 2021. "Understanding the configuration of bus networks in urban China from the perspective of network types and administrative division effect," Transport Policy, Elsevier, vol. 104(C), pages 1-17.
    14. 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.
    15. Huang, Zhongliang & Weng, Wenguo, 2020. "Analysis of geographical migration networks of bride trafficking crime from 2000 to 2018 in China," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 550(C).
    16. Disheng Yi & Yusi Liu & Jiahui Qin & Jing Zhang, 2020. "Identifying Urban Traveling Hotspots Using an Interaction-Based Spatio-Temporal Data Field and Trajectory Data: A Case Study within the Sixth Ring Road of Beijing," Sustainability, MDPI, vol. 12(22), pages 1-20, November.
    17. Wang, Li-Na & Wang, Kai & Shen, Jiang-Long, 2020. "Weighted complex networks in urban public transportation: Modeling and testing," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 545(C).
    18. Xueyao Ma & Gang Li & Junjun Zhou & Xing Gao & Qifan Nie & Shuyan Xue & Ling Liu & Jiaobei Wang & Tingting Xu & Annan Jin, 2020. "Family-to-family child migration network of informal adoption in China," Palgrave Communications, Palgrave Macmillan, vol. 7(1), pages 1-11, December.
    19. Abdelaty, Hatem & Mohamed, Moataz & Ezzeldin, Mohamed & El-Dakhakhni, Wael, 2022. "Temporal robustness assessment framework for city-scale bus transit networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 606(C).
    20. Shiguang Wang & Dexin Yu & Mei-Po Kwan & Huxing Zhou & Yongxing Li & Hongzhi Miao, 2019. "The Evolution and Growth Patterns of the Road Network in a Medium-Sized Developing City: A Historical Investigation of Changchun, China, from 1912 to 2017," Sustainability, MDPI, vol. 11(19), pages 1-25, September.
    21. Büchel, Beda & Corman, Francesco, 2022. "Modeling conditional dependencies for bus travel time estimation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 592(C).
    22. 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).

    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. Lin Zhang & Jian Lu & Bai-bai Fu & Shu-bin Li, 2018. "A Review and Prospect for the Complexity and Resilience of Urban Public Transit Network Based on Complex Network Theory," Complexity, Hindawi, vol. 2018, pages 1-36, December.
    2. Manjalavil, Manju Manohar & Ramadurai, Gitakrishnan, 2020. "Topological properties of bus transit networks considering demand and service utilization weight measures," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 555(C).
    3. Feng, Shumin & Hu, Baoyu & Nie, Cen & Shen, Xianghao, 2016. "Empirical study on a directed and weighted bus transport network in China," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 441(C), pages 85-92.
    4. Luo, Ding & Cats, Oded & van Lint, Hans & Currie, Graham, 2019. "Integrating network science and public transport accessibility analysis for comparative assessment," Journal of Transport Geography, Elsevier, vol. 80(C).
    5. Rui Ding & Norsidah Ujang & Hussain Bin Hamid & Mohd Shahrudin Abd Manan & Rong Li & Safwan Subhi Mousa Albadareen & Ashkan Nochian & Jianjun Wu, 2019. "Application of Complex Networks Theory in Urban Traffic Network Researches," Networks and Spatial Economics, Springer, vol. 19(4), pages 1281-1317, December.
    6. Dimitrov, Stavri Dimitri & Ceder, Avishai (Avi), 2016. "A method of examining the structure and topological properties of public-transport networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 451(C), pages 373-387.
    7. Derrible, Sybil & Kennedy, Christopher, 2010. "The complexity and robustness of metro networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(17), pages 3678-3691.
    8. Junhong Hu & Mingshu Yang & Yunzhu Zhen, 2024. "A Review of Resilience Assessment and Recovery Strategies of Urban Rail Transit Networks," Sustainability, MDPI, vol. 16(15), pages 1-16, July.
    9. Dong-Joon Kang & Su-Han Woo, 2017. "Liner shipping networks, port characteristics and the impact on port performance," Maritime Economics & Logistics, Palgrave Macmillan;International Association of Maritime Economists (IAME), vol. 19(2), pages 274-295, June.
    10. Moreno-Pulido, Soledad & Pavón-Domínguez, Pablo & Burgos-Pintos, Pedro, 2021. "Temporal evolution of multifractality in the Madrid Metro subway network," Chaos, Solitons & Fractals, Elsevier, vol. 142(C).
    11. Yang, Xu-Hua & Chen, Guang & Chen, Sheng-Yong & Wang, Wan-Liang & Wang, Lei, 2014. "Study on some bus transport networks in China with considering spatial characteristics," Transportation Research Part A: Policy and Practice, Elsevier, vol. 69(C), pages 1-10.
    12. Zhang, Jianhua & Zhao, Mingwei & Liu, Haikuan & Xu, Xiaoming, 2013. "Networked characteristics of the urban rail transit networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(6), pages 1538-1546.
    13. Hu, Baoyu & Feng, Shumin & Li, Jinyang & Zhao, Hu, 2018. "Statistical analysis of passenger-crowding in bus transport network of Harbin," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 490(C), pages 426-438.
    14. Zhang, Yanjie & Ayyub, Bilal M. & Saadat, Yalda & Zhang, Dongming & Huang, Hongwei, 2020. "A double-weighted vulnerability assessment model for metrorail transit networks and its application in Shanghai metro," International Journal of Critical Infrastructure Protection, Elsevier, vol. 29(C).
    15. Hong, Liu & Ouyang, Min & Xu, Min & Hu, Peipei, 2020. "Time-varied accessibility and vulnerability analysis of integrated metro and high-speed rail systems," Reliability Engineering and System Safety, Elsevier, vol. 193(C).
    16. Psaltoglou, Artemis & Calle, Eusebi, 2018. "Enhanced connectivity index – A new measure for identifying critical points in urban public transportation networks," International Journal of Critical Infrastructure Protection, Elsevier, vol. 21(C), pages 22-32.
    17. Sun, Yeran & Mburu, Lucy & Wang, Shaohua, 2016. "Analysis of community properties and node properties to understand the structure of the bus transport network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 450(C), pages 523-530.
    18. Zhang, Hui & Zhuge, Chengxiang & Yu, Xiaohua, 2018. "Identifying hub stations and important lines of bus networks: A case study in Xiamen, China," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 502(C), pages 394-402.
    19. Wang, Zhiru & Niu, Fangyan & Yang, Lili & Su, Guofeng, 2020. "Modeling a subway network: A hot-point attraction-driven evolution mechanism," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 537(C).
    20. Peixin Dong & Dongyuan Li & Jianping Xing & Haohui Duan & Yong Wu, 2019. "A Method of Bus Network Optimization Based on Complex Network and Beidou Vehicle Location," Future Internet, MDPI, vol. 11(4), pages 1-12, 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:eee:phsmap:v:502:y:2018:i:c:p:295-314. 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.journals.elsevier.com/physica-a-statistical-mechpplications/ .

    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.