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Analysis of Public Bus Transportation of a Brazilian City Based on the Theory of Complex Networks Using the P-Space

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Listed:
  • A. A. De Bona
  • K. V. O. Fonseca
  • M. O. Rosa
  • R. Lüders
  • M. R. B. S. Delgado

Abstract

The city of Curitiba, located at Southern Brazil, is recognized by its urban planning structured on three pillars: land use, collective transportation, and traffic. With 3.8 million people in its metropolitan area, the public transport system deals with approximately 2.5 million passengers daily. The structure and properties of such a transportation system have substantial implications for the urban planning and public politics for sustainable development of Curitiba. Therefore, this paper analyzes the structure of the public transportation system of Curitiba through the theory of complex networks in a static approach of network topology and presents a comparative analysis of the results from Curitiba, three cities from China (Shanghai, Beijing, and Guangzhou), and three cities from Poland (GOP, Warszawa, and Łódź). The transportation network was modeled as a complex network with exact geographical coordinates of its bus stops. In all bus lines, the method used was the P-Space. The results show that this bus network has characteristics of both small-world and scale-free networks.

Suggested Citation

  • A. A. De Bona & K. V. O. Fonseca & M. O. Rosa & R. Lüders & M. R. B. S. Delgado, 2016. "Analysis of Public Bus Transportation of a Brazilian City Based on the Theory of Complex Networks Using the P-Space," Mathematical Problems in Engineering, Hindawi, vol. 2016, pages 1-12, June.
    • Handle: RePEc:hin:jnlmpe:3898762
    DOI: 10.1155/2016/3898762
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    Cited by:

    1. Li, Tao & Rong, Lili, 2022. "Spatiotemporally complementary effect of high-speed rail network on robustness of aviation network," Transportation Research Part A: Policy and Practice, Elsevier, vol. 155(C), pages 95-114.
    2. 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).
    3. Qian, Peipei & Yang, Zhongzhen & Lian, Feng, 2024. "The structural and spatial evolution of the China Railway Express network," Research in Transportation Economics, Elsevier, vol. 103(C).
    4. 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.
    5. Ma, Min & Hu, Dawei & Chien, Steven I-Jy & Liu, Jie & Yang, Xing & Ma, Zhuanglin, 2022. "Evolution assessment of urban rail transit networks: A case study of Xi’an, China," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 603(C).
    6. 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.
    7. De Bona, Anderson Andrei & de Oliveira Rosa, Marcelo & Ono Fonseca, Keiko Verônica & Lüders, Ricardo, 2021. "A reduced model for complex network analysis of public transportation systems," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 567(C).
    8. Xu, Xiaohan & Huang, Ailing & Shalaby, Amer & Feng, Qian & Chen, Mingyang & Qi, Geqi, 2024. "Exploring cascading failure processes of interdependent multi-modal public transit networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 638(C).
    9. Lei, Da & Cheng, Long & Wang, Pengfei & Chen, Xuewu & Zhang, Lin, 2024. "Identifying service bottlenecks in public bikesharing flow networks," Journal of Transport Geography, Elsevier, vol. 116(C).

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