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Assessing perceived driving difficulties under emergency evacuation for vulnerable population groups

Author

Listed:
  • Abioye, Olumide F.
  • Dulebenets, Maxim A.
  • Ozguven, Eren Erman
  • Moses, Ren
  • Boot, Walter R.
  • Sando, Thobias

Abstract

The devastating impacts of natural hazards, including loss of lives and properties, underline the importance of efficient hazard preparedness, especially in the areas with frequent hazard occurrence. Several studies indicated that driving during emergency evacuation is quite challenging due to dense traffic flow, inclement weather conditions, and unexpected maneuvers of other evacuees. However, limited research has been directed towards assessing the perceived driving difficulties of individuals, including vulnerable population, under emergency evacuation. This study deploys a driving simulator in order to emulate realistic emergency evacuation scenarios and to quantify the perceived driving difficulties of individuals under emergency evacuation. Based on the data, collected using a driving simulator, a number of statistical models are proposed to determine a set of performance indicators, including the mental demand, physical demand, temporal demand, performance, effort, and frustration, experienced by individuals as a result of emergency evacuation. The statistical models also capture a variety of different driver characteristics, traffic characteristics, driving conditions, and evacuation route characteristics. The analysis results suggest that the considered performance indicators are significantly influenced with a number of factors, including age, gender, education, race, presence of chronic diseases, and self-reported driving ability. The insights from the conducted research can be applied at the hazard preparedness stage to mitigate the perceived driving difficulties of individuals under emergency evacuation and ensure their safety.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:soceps:v:72:y:2020:i:c:s0038012119305178
    DOI: 10.1016/j.seps.2020.100878
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    References listed on IDEAS

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    1. 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.
    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. Swamy, Rahul & Kang, Jee Eun & Batta, Rajan & Chung, Younshik, 2017. "Hurricane evacuation planning using public transportation," Socio-Economic Planning Sciences, Elsevier, vol. 59(C), pages 43-55.
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    Cited by:

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    2. Feng, Xinhang & Jiang, Yanli & Gai, Wenmei, 2024. "Rural community response to accidental toxic gas release: An individual emergency response model during self-organized evacuations," Reliability Engineering and System Safety, Elsevier, vol. 248(C).
    3. Song, Chengcheng & Shao, Quan & Zhu, Pei & Dong, Min & Yu, Wenfei, 2023. "An emergency aircraft evacuation simulation considering passenger overtaking and luggage retrieval," Reliability Engineering and System Safety, Elsevier, vol. 229(C).
    4. Qiong Bao & Hanrun Tang & Yongjun Shen, 2021. "Driving Behavior Based Relative Risk Evaluation Using a Nonparametric Optimization Method," IJERPH, MDPI, vol. 18(23), pages 1-15, November.
    5. Juan F. Dols & Jaime Molina & F. Javier Camacho-Torregrosa & David Llopis-Castelló & Alfredo García, 2021. "Development of Driving Simulation Scenarios Based on Building Information Modeling (BIM) for Road Safety Analysis," Sustainability, MDPI, vol. 13(4), pages 1-21, February.

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