IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v17y2025i9p3909-d1643138.html
   My bibliography  Save this article

Towards Sustainable and Resilient Infrastructure: Hurricane-Induced Roadway Closure and Accessibility Assessment in Florida Using Machine Learning

Author

Listed:
  • Samuel Takyi

    (Department of Civil and Environmental Engineering, FAMU–FSU College of Engineering, 2525 Pottsdamer Street, Tallahassee, FL 32310, USA)

  • Richard Boadu Antwi

    (Department of Civil and Environmental Engineering, FAMU–FSU College of Engineering, 2525 Pottsdamer Street, Tallahassee, FL 32310, USA)

  • Eren Erman Ozguven

    (Department of Civil and Environmental Engineering, FAMU–FSU College of Engineering, 2525 Pottsdamer Street, Tallahassee, FL 32310, USA)

  • Leslie Okine

    (Department of Civil and Environmental Engineering, FAMU–FSU College of Engineering, 2525 Pottsdamer Street, Tallahassee, FL 32310, USA)

  • Ren Moses

    (Department of Civil and Environmental Engineering, FAMU–FSU College of Engineering, 2525 Pottsdamer Street, Tallahassee, FL 32310, USA)

Abstract

Natural disasters like hurricanes can severely disrupt transportation systems, leading to roadway closures and limiting accessibility, which has extreme economic, social, and sustainability implications. This study investigates the impact of hurricanes Ian and Idalia on roadway accessibility in Florida using machine learning techniques. High-resolution satellite imagery, combined with demographic and hurricane-related roadway data, was used to assess the extent of road closures in southeast Florida (Hurricane Ian) and northwest Florida (Hurricane Idalia). The model detected roadway segments as open, partially closed, or fully closed, achieving an overall accuracy of 89%, with confidence levels of 92% and 85% for the two hurricanes, respectively. The results showed that heavily populated coastal regions experienced the most significant disruptions, with more extensive closures and reduced accessibility. This research demonstrates how machine learning can enhance disaster recovery efforts by identifying critical infrastructure in need of immediate attention, supporting sustainable resilience in post-hurricane recovery. The findings suggest that integrating such methods into disaster planning can improve the efficiency and sustainability of recovery operations, helping to allocate resources more effectively in future disaster events.

Suggested Citation

  • Samuel Takyi & Richard Boadu Antwi & Eren Erman Ozguven & Leslie Okine & Ren Moses, 2025. "Towards Sustainable and Resilient Infrastructure: Hurricane-Induced Roadway Closure and Accessibility Assessment in Florida Using Machine Learning," Sustainability, MDPI, vol. 17(9), pages 1-31, April.
    • Handle: RePEc:gam:jsusta:v:17:y:2025:i:9:p:3909-:d:1643138
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/17/9/3909/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/17/9/3909/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Kim, Jooho & Bae, Juhee & Hastak, Makarand, 2018. "Emergency information diffusion on online social media during storm Cindy in U.S," International Journal of Information Management, Elsevier, vol. 40(C), pages 153-165.
    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. Grover, Purva & Kar, Arpan Kumar & Davies, Gareth, 2018. "“Technology enabled Health” – Insights from twitter analytics with a socio-technical perspective," International Journal of Information Management, Elsevier, vol. 43(C), pages 85-97.
    2. Abhinav Kumar & Jyoti Prakash Singh & Yogesh K. Dwivedi & Nripendra P. Rana, 2022. "A deep multi-modal neural network for informative Twitter content classification during emergencies," Annals of Operations Research, Springer, vol. 319(1), pages 791-822, December.
    3. Cole Vaughn & Kathleen Sherman-Morris & Philip Poe, 2023. "Factors influencing retweeting of local news media tweets during Hurricane Irma," 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. 119(1), pages 583-611, October.
    4. Turgut Acikara & Bo Xia & Tan Yigitcanlar & Carol Hon, 2023. "Contribution of Social Media Analytics to Disaster Response Effectiveness: A Systematic Review of the Literature," Sustainability, MDPI, vol. 15(11), pages 1-50, May.
    5. Nisar, Tahir M. & Prabhakar, Guru & Patil, Pushp P., 2018. "Sports clubs’ use of social media to increase spectator interest," International Journal of Information Management, Elsevier, vol. 43(C), pages 188-195.
    6. Martínez-Rojas, María & Pardo-Ferreira, María del Carmen & Rubio-Romero, Juan Carlos, 2018. "Twitter as a tool for the management and analysis of emergency situations: A systematic literature review," International Journal of Information Management, Elsevier, vol. 43(C), pages 196-208.
    7. Xinxin Guo & Benyong Wei & Gaozhong Nie & Guiwu Su, 2022. "Application of Mobile Signaling Data in Determining the Seismic Influence Field: A Case Study of the 2017 Mw 6.5 Jiuzhaigou Earthquake, China," IJERPH, MDPI, vol. 19(17), pages 1-23, August.
    8. Jamali, Mehdi & Nejat, Ali & Ghosh, Souparno & Jin, Fang & Cao, Guofeng, 2019. "Social media data and post-disaster recovery," International Journal of Information Management, Elsevier, vol. 44(C), pages 25-37.
    9. Bin Wei & Yike Bai & Liying Liang & Shunqing Yang, 2024. "Research Status and Evolutionary Trends of Digitally Empowered Community Emergency Management: Knowledge Graph Analysis Based on CiteSpace," Sustainability, MDPI, vol. 16(20), pages 1-22, October.

    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:17:y:2025:i:9:p:3909-:d:1643138. 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.