IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v16y2024i23p10440-d1532037.html
   My bibliography  Save this article

Failure-Resistant Path Selection Considering Netizens’ Sentiment Orientation Under Typhoon Disasters

Author

Listed:
  • Zhenning Zhou

    (Faculty of Maritime and Transportation, Ningbo University, Ningbo 315000, China)

  • Jiaqi Yu

    (Faculty of Maritime and Transportation, Ningbo University, Ningbo 315000, China)

  • Gao Gao

    (Faculty of Maritime and Transportation, Ningbo University, Ningbo 315000, China)

  • Zhengfeng Huang

    (Faculty of Maritime and Transportation, Ningbo University, Ningbo 315000, China)

  • Jintao Han

    (Faculty of Maritime and Transportation, Ningbo University, Ningbo 315000, China)

  • Pengjun Zheng

    (Faculty of Maritime and Transportation, Ningbo University, Ningbo 315000, China)

Abstract

After a typhoon disaster, selecting effective paths is crucial for ensuring the efficiency of emergency rescue operations and the distribution of essential supplies, which are vital for sustainable disaster response and community resilience. Current research into disaster scenarios is less aligned with actual scenarios as road conditions are hard to predict. This paper, set against the backdrop of typhoon disasters, employs netizens’ sentiment data to indirectly assess post-disaster road conditions and refine the calculation formula for road failure probabilities. This approach aims to identify failure-resistant paths to guide disaster relief decisions, thereby supporting sustainable disaster relief operations and minimizing resource expenditure. First, an expression form for road segment failure probability is established, considering factors such as tree falls, landslides, and waterlogging. Second, negative sentiment coefficients, derived from social media data analysis, are used to adjust road failure probabilities, reflecting the sentiments of affected communities. Then, a failure-resistant path selection model based on these adjusted road failure probabilities is proposed to enhance the resilience and sustainability of emergency transport paths. Finally, the model’s effectiveness is validated using Typhoon “In-Fa” in Ningbo as a case study.

Suggested Citation

  • Zhenning Zhou & Jiaqi Yu & Gao Gao & Zhengfeng Huang & Jintao Han & Pengjun Zheng, 2024. "Failure-Resistant Path Selection Considering Netizens’ Sentiment Orientation Under Typhoon Disasters," Sustainability, MDPI, vol. 16(23), pages 1-20, November.
  • Handle: RePEc:gam:jsusta:v:16:y:2024:i:23:p:10440-:d:1532037
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/16/23/10440/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/16/23/10440/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Peng Xiao & Yinfeng Xu & Bing Su, 2009. "Finding an anti-risk path between two nodes in undirected graphs," Journal of Combinatorial Optimization, Springer, vol. 17(3), pages 235-246, April.
    2. Wu, Yangyang & Hou, Guangyang & Chen, Suren, 2021. "Post-earthquake resilience assessment and long-term restoration prioritization of transportation network," Reliability Engineering and System Safety, Elsevier, vol. 211(C).
    3. Guangyang Hou & Suren Chen, 2020. "Probabilistic modeling of disrupted infrastructures due to fallen trees subjected to extreme winds in urban community," 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. 102(3), pages 1323-1350, July.
    Full references (including those not matched with items on IDEAS)

    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. Hou, Guangyang & Muraleetharan, Kanthasamy K. & Panchalogaranjan, Vinushika & Moses, Paul & Javid, Amir & Al-Dakheeli, Hussein & Bulut, Rifat & Campos, Richard & Harvey, P. Scott & Miller, Gerald & Bo, 2023. "Resilience assessment and enhancement evaluation of power distribution systems subjected to ice storms," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    2. Shang, Qingxue & Guo, Xiaodong & Li, Jichao & Wang, Tao, 2022. "Post-earthquake health care service accessibility assessment framework and its application in a medium-sized city," Reliability Engineering and System Safety, Elsevier, vol. 228(C).
    3. Huili Zhang & Yinfeng Xu & Xingang Wen, 2015. "Optimal shortest path set problem in undirected graphs," Journal of Combinatorial Optimization, Springer, vol. 29(3), pages 511-530, April.
    4. Wang, Hongping & Fang, Yi-Ping & Zio, Enrico, 2022. "Resilience-oriented optimal post-disruption reconfiguration for coupled traffic-power systems," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
    5. Liu, Qiang & Huang, Delong & Zhang, Bin & Tang, Aiping & Xu, Xiuchen, 2024. "Developing a probability-based technique to improve the measurement of landslide vulnerability on regional roads," Reliability Engineering and System Safety, Elsevier, vol. 244(C).
    6. Aghababaei, Mohammad T. (Siavash) & Costello, Seosamh B. & Ranjitkar, Prakash, 2021. "Measures to evaluate post-disaster trip resilience on road networks," Journal of Transport Geography, Elsevier, vol. 95(C).
    7. Amoozad Mahdiraji, Hannan & Yaftiyan, Fatemeh & Abbasi-Kamardi, Aliasghar & Vrontis, Demetris & Gong, Yu, 2024. "Disentangling the resiliency of international transportation systems under uncertainty by a novel multi-layer spherical fuzzy decision-making framework: Evidence from an emerging economy," Transportation Research Part A: Policy and Practice, Elsevier, vol. 186(C).
    8. Zhou, Xinxin & Huang, Yun & Bai, Guanghan & Xu, Bei & Tao, Junyong, 2024. "The resilience evaluation of unmanned autonomous swarm with informed agents under partial failure," Reliability Engineering and System Safety, Elsevier, vol. 244(C).
    9. Yangyang Wu & Suren Chen, 2023. "Transportation Resilience Modeling and Bridge Reconstruction Planning Based on Time-Evolving Travel Demand during Post-Earthquake Recovery Period," Sustainability, MDPI, vol. 15(17), pages 1-26, August.
    10. Pan, Xing & Dang, Yuheng & Wang, Huixiong & Hong, Dongpao & Li, Yuehong & Deng, Hongxu, 2022. "Resilience model and recovery strategy of transportation network based on travel OD-grid analysis," Reliability Engineering and System Safety, Elsevier, vol. 223(C).
    11. Andrea Miano & Marco Civera & Fabrizio Aloschi & Valerio De Biagi & Bernardino Chiaia & Fulvio Parisi & Andrea Prota, 2024. "Efficiency Assessment of Urban Road Networks Connecting Critical Node Pairs under Seismic Hazard," Sustainability, MDPI, vol. 16(17), pages 1-18, August.
    12. Liquan Xu & Zhentian Zhang & Gangyi Tan & Junqing Zhou & Yang Wang, 2022. "Analysis on the Evolution and Resilience of Ecological Network Structure in Wuhan Metropolitan Area," Sustainability, MDPI, vol. 14(14), pages 1-16, July.
    13. Wang, Feng & Tian, Jin & Shi, Chenli & Ling, Jiamu & Chen, Zian & Xu, Zhengguo, 2024. "A multi-stage quantitative resilience analysis and optimization framework considering dynamic decisions for urban infrastructure systems," Reliability Engineering and System Safety, Elsevier, vol. 243(C).
    14. Geng, Sunyue & Liu, Sifeng & Fang, Zhigeng, 2022. "A demand-based framework for resilience assessment of multistate networks under disruptions," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
    15. Zhang, Mingyuan & Yang, Xiangjie & Zhang, Juan & Li, Gang, 2022. "Post-earthquake resilience optimization of a rural “road-bridge†transportation network system," Reliability Engineering and System Safety, Elsevier, vol. 225(C).
    16. Liu, Meili & Qi, Xiaogang & Pan, Hao, 2022. "Optimizing communication network geodiversity for disaster resilience through shielding approach," Reliability Engineering and System Safety, Elsevier, vol. 228(C).
    17. Liu, Qiang & Tang, Aiping & Huang, Delong & Huang, Ziyuan & Zhang, Bin & Xu, Xiuchen, 2022. "Total probabilistic measure for the potential risk of regional roads exposed to landslides," Reliability Engineering and System Safety, Elsevier, vol. 228(C).
    18. Li, Yulong & Lin, Jie & Zhang, Chi & Zhu, Huaxing & Zeng, Saixing & Sun, Chengshaung, 2022. "Joint optimization of structure and protection of interdependent infrastructure networks," Reliability Engineering and System Safety, Elsevier, vol. 218(PB).
    19. Jia, Chuanzhou & Zhang, Chi & Li, Yan-Fu & Li, Quan-Lin, 2023. "Joint pre- and post-disaster planning to enhance the resilience of critical infrastructures," Reliability Engineering and System Safety, Elsevier, vol. 231(C).
    20. Jay Mahadeokar & Sanjeev Saxena, 2014. "Faster algorithm to find anti-risk path between two nodes of an undirected graph," Journal of Combinatorial Optimization, Springer, vol. 27(4), pages 798-807, May.

    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:16:y:2024:i:23:p:10440-:d:1532037. 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.