IDEAS home Printed from https://ideas.repec.org/a/kap/transp/v46y2019i4d10.1007_s11116-017-9814-y.html
   My bibliography  Save this article

The role of travel demand and network centrality on the connectivity and resilience of an urban street system

Author

Listed:
  • Meisam Akbarzadeh

    (Isfahan University of Technology)

  • Soroush Memarmontazerin

    (Isfahan University of Technology)

  • Sybil Derrible

    (University of Illinois at Chicago
    University of Illinois at Chicago)

  • Sayed Farzin Salehi Reihani

    (Isfahan University of Technology)

Abstract

In the transportation literature, two major and parallel approaches exist to identify the critical elements of a transportation system. On the one hand, conventional transportation engineering emphasizes travel demand, often in terms of traffic volume (i.e., demand side). On the other hand, newer techniques from Network Science emphasize network topology (i.e., supply side). To better understand the relationship between the two approaches, we first investigate whether they correlate by comparing traffic volume and node centrality. Second, we assess the impact of the two approaches on the connectivity and resilience of a transportation network; connectivity is measured by the relative size of the giant component, and resilience is measured by the network’s adaptive capacity (the amount of extra flow it can handle). The urban road system of Isfahan (Iran) is used as a practical case study. Overall, we find that traffic volume indeed correlates with node centrality. In addition, we find that the weighted degree of a node, i.e., the sum of the capacities of its incident links (for small disruptions) and node betweenness (for large disruptions), best captures node criticality. Nodes with high weighted degree and betweenness should therefore be given higher priority to enhance connectivity and resilience in urban street systems. Regarding link criticality, roads with higher capacities showed a more important role as opposed to betweenness, flow, and congestion.

Suggested Citation

  • Meisam Akbarzadeh & Soroush Memarmontazerin & Sybil Derrible & Sayed Farzin Salehi Reihani, 2019. "The role of travel demand and network centrality on the connectivity and resilience of an urban street system," Transportation, Springer, vol. 46(4), pages 1127-1141, August.
    • Handle: RePEc:kap:transp:v:46:y:2019:i:4:d:10.1007_s11116-017-9814-y
    DOI: 10.1007/s11116-017-9814-y
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11116-017-9814-y
    File Function: Abstract
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1007/s11116-017-9814-y?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. Wang, Xiangrong & Koç, Yakup & Derrible, Sybil & Ahmad, Sk Nasir & Pino, Willem J.A. & Kooij, Robert E., 2017. "Multi-criteria robustness analysis of metro networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 474(C), pages 19-31.
    2. Réka Albert & Hawoong Jeong & Albert-László Barabási, 2000. "Error and attack tolerance of complex networks," Nature, Nature, vol. 406(6794), pages 378-382, July.
    3. Gao, Cai & Wei, Daijun & Hu, Yong & Mahadevan, Sankaran & Deng, Yong, 2013. "A modified evidential methodology of identifying influential nodes in weighted networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(21), pages 5490-5500.
    4. 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.
    5. Caitlin D. Cottrill & Sybil Derrible, 2015. "Leveraging Big Data for the Development of Transport Sustainability Indicators," Journal of Urban Technology, Taylor & Francis Journals, vol. 22(1), pages 45-64, January.
    6. Tsekeris, Theodore & Geroliminis, Nikolas, 2013. "City size, network structure and traffic congestion," Journal of Urban Economics, Elsevier, vol. 76(C), pages 1-14.
    7. Suhyung Yoo & Hwasoo Yeo, 2016. "Evaluation of the resilience of air transportation network with adaptive capacity," International Journal of Urban Sciences, Taylor & Francis Journals, vol. 20(sup1), pages 38-49, July.
    8. Dunn, Sarah & Wilkinson, Sean M., 2016. "Increasing the resilience of air traffic networks using a network graph theory approach," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 90(C), pages 39-50.
    9. Flaviano Morone & Hernán A. Makse, 2015. "Influence maximization in complex networks through optimal percolation," Nature, Nature, vol. 524(7563), pages 65-68, August.
    10. Sybil Derrible & Nasir Ahmad, 2015. "Network-Based and Binless Frequency Analyses," PLOS ONE, Public Library of Science, vol. 10(11), pages 1-10, November.
    11. 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. Mohammad Zaher Serdar & Sami G. Al-Ghamdi, 2021. "Resiliency Assessment of Road Networks during Mega Sport Events: The Case of FIFA World Cup Qatar 2022," Sustainability, MDPI, vol. 13(22), pages 1-15, November.
    2. Chakrabarti, Sandip & Kushari, Triparnee & Mazumder, Taraknath, 2022. "Does transportation network centrality determine housing price?," Journal of Transport Geography, Elsevier, vol. 103(C).
    3. Boeing, Geoff & Ha, Jaehyun, 2024. "Resilient by Design: Simulating Street Network Disruptions across Every Urban Area in the World," SocArXiv tk93y, Center for Open Science.
    4. Almotahari, Amirmasoud & Yazici, Anil, 2021. "A computationally efficient metric for identification of critical links in large transportation networks," Reliability Engineering and System Safety, Elsevier, vol. 209(C).
    5. Perez, Yuri & Pereira, Fabio Henrique, 2021. "Simulation of traffic light disruptions in street networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 582(C).
    6. Wu, Chao-Yun & Hu, Mao-Bin & Jiang, Rui & Hao, Qing-Yi, 2021. "Effects of road network structure on the performance of urban traffic systems," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 563(C).
    7. 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. 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.
    2. Amirhassan Kermanshah & Sybil Derrible, 2017. "Robustness of road systems to extreme flooding: using elements of GIS, travel demand, and network science," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 86(1), pages 151-164, March.
    3. Wu, Tao & Xian, Xingping & Zhong, Linfeng & Xiong, Xi & Stanley, H. Eugene, 2018. "Power iteration ranking via hybrid diffusion for vital nodes identification," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 506(C), pages 802-815.
    4. Hao, Hongchang & Ma, Zhe & Wang, Anjian & Xing, Wanli & Song, Hao & Zhao, Pei & Wei, Jiangqiao & Zheng, Shuxian, 2023. "Modeling and assessing the robustness of the lithium global trade system against cascading failures," Resources Policy, Elsevier, vol. 85(PB).
    5. Zhai, Li & Yan, Xiangbin & Zhang, Guojing, 2018. "Bi-directional h-index: A new measure of node centrality in weighted and directed networks," Journal of Informetrics, Elsevier, vol. 12(1), pages 299-314.
    6. Elosegui, Pedro & Forte, Federico D. & Montes-Rojas, Gabriel, 2022. "Network structure and fragmentation of the Argentinean interbank markets," Latin American Journal of Central Banking (previously Monetaria), Elsevier, vol. 3(3).
    7. Saxena, Chandni & Doja, M.N. & Ahmad, Tanvir, 2018. "Group based centrality for immunization of complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 508(C), pages 35-47.
    8. Gangwal, Utkarsh & Singh, Mayank & Pandey, Pradumn Kumar & Kamboj, Deepak & Chatterjee, Samrat & Bhatia, Udit, 2022. "Identifying early-warning indicators of onset of sudden collapse in networked infrastructure systems against sequential disruptions," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 591(C).
    9. Wen, Xiangxi & Tu, Congliang & Wu, Minggong, 2018. "Node importance evaluation in aviation network based on “No Return” node deletion method," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 503(C), pages 546-559.
    10. Li Zeng & Changjun Fan & Chao Chen, 2023. "Leveraging Minimum Nodes for Optimum Key Player Identification in Complex Networks: A Deep Reinforcement Learning Strategy with Structured Reward Shaping," Mathematics, MDPI, vol. 11(17), pages 1-13, August.
    11. Sheikhahmadi, Amir & Nematbakhsh, Mohammad Ali & Zareie, Ahmad, 2017. "Identification of influential users by neighbors in online social networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 486(C), pages 517-534.
    12. Liu, Xiaoxiao & Sun, Shiwen & Wang, Jiawei & Xia, Chengyi, 2019. "Onion structure optimizes attack robustness of interdependent networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 535(C).
    13. Pan, Shouzheng & Yan, Hai & He, Jia & He, Zhengbing, 2021. "Vulnerability and resilience of transportation systems: A recent literature review," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 581(C).
    14. Sun, Peng Gang & Che, Wanping & Quan, Yining & Wang, Shuzhen & Miao, Qiguang, 2022. "Random networks are heterogeneous exhibiting a multi-scaling law," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 587(C).
    15. Ballester, Coralio & Calvó-Armengol, Antoni & Zenou, Yves, 2004. "Who's Who in Crime Network. Wanted the Key Player," Working Paper Series 617, Research Institute of Industrial Economics.
    16. Wang, Dong & Small, Michael & Zhao, Yi, 2021. "Exploring the optimal network topology for spreading dynamics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 564(C).
    17. 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).
    18. Chen, Sai & Ding, Yueting & Zhang, Yanfang & Zhang, Ming & Nie, Rui, 2022. "Study on the robustness of China's oil import network," Energy, Elsevier, vol. 239(PB).
    19. 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.
    20. Peng, Peng & Poon, Jessie P.H. & Yang, Yu & Lu, Feng & Cheng, Shifen, 2019. "Global oil traffic network and diffusion of influence among ports using real time data," Energy, Elsevier, vol. 172(C), pages 333-342.

    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:kap:transp:v:46:y:2019:i:4:d:10.1007_s11116-017-9814-y. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.