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Metadata-based Needs Assessment for Emergency Transportation Operations with a Focus on an Aging Population: A Case Study in Florida

Author

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  • Eren Erman Ozguven
  • Mark W. Horner
  • Ayberk Kocatepe
  • Jean Michael Marcelin
  • Yassir Abdelrazig
  • Thobias Sando
  • Ren Moses

Abstract

In the aftermath of disasters, evacuating aging victims and maintaining an optimal flow of critical resources in order to serve their needs becomes problematic, especially for Gulf Coast states in the USA such as Florida, where more than 6.9 million (36.9%) of the population are over age 50. Scanning the literature, there is no substantial prior work that has synthesized the requirements for a multi-modal emergency needs assessment that could facilitate the safe and accessible evacuation of aging people, and optimize the flow of resources into the affected region to satisfy the needs of those who remain. This paper presents a review of the aging population-focused emergency literature utilizing a knowledge base development methodology supported with a geographic information system-based case study application set in Florida. Importance is given to both ensuring the resiliency of the transportation infrastructure and meeting the needs of aging populations. As a result of this metadata-based analysis, critical research needs and challenges are presented with planning recommendations and future research directions. Results clearly indicate that transportation agencies should focus on clear and fast dissemination of disaster-related information to the aging populations. The use of paratransit services for evacuating aging people, especially those living independently and/or in rural areas, is also found to be of paramount importance.

Suggested Citation

  • Eren Erman Ozguven & Mark W. Horner & Ayberk Kocatepe & Jean Michael Marcelin & Yassir Abdelrazig & Thobias Sando & Ren Moses, 2016. "Metadata-based Needs Assessment for Emergency Transportation Operations with a Focus on an Aging Population: A Case Study in Florida," Transport Reviews, Taylor & Francis Journals, vol. 36(3), pages 383-412, May.
  • Handle: RePEc:taf:transr:v:36:y:2016:i:3:p:383-412
    DOI: 10.1080/01441647.2015.1082516
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    Cited by:

    1. Stéphane C. K. Tékouabou & Jérôme Chenal & Rida Azmi & Hamza Toulni & El Bachir Diop & Anastasija Nikiforova, 2022. "Identifying and Classifying Urban Data Sources for Machine Learning-Based Sustainable Urban Planning and Decision Support Systems Development," Data, MDPI, vol. 7(12), pages 1-19, November.
    2. Dulebenets, Maxim A. & Abioye, Olumide F. & Ozguven, Eren Erman & Moses, Ren & Boot, Walter R. & Sando, Thobias, 2019. "Development of statistical models for improving efficiency of emergency evacuation in areas with vulnerable population," Reliability Engineering and System Safety, Elsevier, vol. 182(C), pages 233-249.
    3. Abioye, Olumide F. & Dulebenets, Maxim A. & Ozguven, Eren Erman & Moses, Ren & Boot, Walter R. & Sando, Thobias, 2020. "Assessing perceived driving difficulties under emergency evacuation for vulnerable population groups," Socio-Economic Planning Sciences, Elsevier, vol. 72(C).

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