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Statistical Characteristics and Community Analysis of Urban Road Networks

Author

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  • Wen-Long Shang
  • Yanyan Chen
  • Huibo Bi
  • Haoran Zhang
  • Changxi Ma
  • Washington Y. Ochieng

Abstract

Urban road networks are typical complex systems, which are crucial to our society and economy. In this study, topological characteristics of a number of urban road networks purely based on physical roads rather than routes of vehicles or buses are investigated in order to discover underlying unique structural features, particularly compared to other types of transport networks. Based on these topological indices, correlations between topological indices and small-worldness of urban road networks are also explored. The finding shows that there is no significant small-worldness for urban road networks, which is apparently different from other transport networks. Following this, community detection of urban road networks is conducted. The results reveal that communities and hierarchy of urban road networks tend to follow a general nature rule.

Suggested Citation

  • Wen-Long Shang & Yanyan Chen & Huibo Bi & Haoran Zhang & Changxi Ma & Washington Y. Ochieng, 2020. "Statistical Characteristics and Community Analysis of Urban Road Networks," Complexity, Hindawi, vol. 2020, pages 1-21, September.
    • Handle: RePEc:hin:complx:6025821
    DOI: 10.1155/2020/6025821
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    Cited by:

    1. Luo, Xi & Gao, Yaru & Liu, Xiaojun & Sun, Yongkai & Li, Na & Liu, Jianghua, 2023. "ACHRA: A novel model to study the propagation of clean heating acceptance among rural residents based on social networks," Applied Energy, Elsevier, vol. 333(C).
    2. Wenjing Wang & Yanyan Chen & Haodong Sun & Yusen Chen, 2021. "Multiple Binary Classification Model of Trip Chain Based on the Fusion of Internet Location Data and Transport Data," Sustainability, MDPI, vol. 13(21), pages 1-15, November.
    3. Dai, Rongjian & Ding, Chuan & Gao, Jian & Wu, Xinkai & Yu, Bin, 2022. "Optimization and evaluation for autonomous taxi ride-sharing schedule and depot location from the perspective of energy consumption," Applied Energy, Elsevier, vol. 308(C).
    4. Chen, Haoqian & Sui, Yi & Shang, Wen-long & Sun, Rencheng & Chen, Zhiheng & Wang, Changying & Han, Chunjia & Zhang, Yuqian & Zhang, Haoran, 2022. "Towards renewable public transport: Mining the performance of electric buses using solar-radiation as an auxiliary power source," Applied Energy, Elsevier, vol. 325(C).
    5. Geoff Boeing & Jaehyun Ha, 2024. "Resilient by Design: Simulating Street Network Disruptions across Every Urban Area in the World," Papers 2403.10636, arXiv.org.
    6. Shang, Wen-Long & Chen, Yishui & Yu, Qing & Song, Xuewang & Chen, Yanyan & Ma, Xiaolei & Chen, Xiqun & Tan, Zhijia & Huang, Jianling & Ochieng, Washington, 2023. "Spatio-temporal analysis of carbon footprints for urban public transport systems based on smart card data," Applied Energy, Elsevier, vol. 352(C).
    7. Yu, Qing & Li, Weifeng & Zhang, Haoran & Chen, Jinyu, 2022. "GPS data in taxi-sharing system: Analysis of potential demand and assessment of fuel consumption based on routing probability model," Applied Energy, Elsevier, vol. 314(C).
    8. Qiu, Dawei & Wang, Yi & Sun, Mingyang & Strbac, Goran, 2022. "Multi-service provision for electric vehicles in power-transportation networks towards a low-carbon transition: A hierarchical and hybrid multi-agent reinforcement learning approach," Applied Energy, Elsevier, vol. 313(C).
    9. Zhou, Junfeng & Zhang, Yanhui & Zhang, Yubo & Shang, Wen-Long & Yang, Zhile & Feng, Wei, 2022. "Parameters identification of photovoltaic models using a differential evolution algorithm based on elite and obsolete dynamic learning," Applied Energy, Elsevier, vol. 314(C).
    10. Bi, Huibo & Shang, Wen-Long & Chen, Yanyan & Wang, Kezhi & Yu, Qing & Sui, Yi, 2021. "GIS aided sustainable urban road management with a unifying queueing and neural network model," Applied Energy, Elsevier, vol. 291(C).

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