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Development of statistical models for improving efficiency of emergency evacuation in areas with vulnerable population

Author

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

Abstract

Different parts of the world are characterized by frequent occurrences of natural hazards. As such, evacuation planning is an essential part of the natural hazard preparedness, especially in hazard-prone areas. Numerous research efforts have been directed towards improving the efficiency of the evacuation process. However, only a limited number of studies have specifically aimed to identify factors, influencing the driving ability of individuals under emergency evacuation and the occurrence of crashes along the evacuation routes. Furthermore, previous research efforts have focused on a relatively narrow range of factors (primarily driver and traffic flow characteristics). This study aims to fill the existing gap in the state-of-the-art by investigating the effects of a wide range of different factors (including driver characteristics, evacuation route characteristics, driving conditions, and traffic characteristics) on the major driving performance indicators under emergency evacuation. The considered driving performance indicators include travel time, lane deviation, crash occurrence, collision speed, average acceleration pedal pressure, and average braking pedal pressure. A set of statistical models is developed to identify the most significant factors that influence the major driving performance indicators. These models are tested using the data collected from the driving simulator and participants with various socio-demographic characteristics. The results indicate that age, gender, visual disorders, number of lanes, and space headway may substantially impact the driving ability of individuals throughout the emergency evacuation process. Findings from this research can be incorporated within the existing transportation planning models to facilitate the natural hazard preparedness, ensure safety of evacuees, including vulnerable populations, and reduce or even prevent the occurrence of crashes along the evacuation routes.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:reensy:v:182:y:2019:i:c:p:233-249
    DOI: 10.1016/j.ress.2018.09.021
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    References listed on IDEAS

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    Cited by:

    1. 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.
    2. Wang, Xinjian & Liu, Zhengjiang & Loughney, Sean & Yang, Zaili & Wang, Yanfu & Wang, Jin, 2022. "Numerical analysis and staircase layout optimisation for a Ro-Ro passenger ship during emergency evacuation," Reliability Engineering and System Safety, Elsevier, vol. 217(C).
    3. Ying Yao & Xiaohua Zhao & Hongji Du & Yunlong Zhang & Guohui Zhang & Jian Rong, 2019. "Classification of Fatigued and Drunk Driving Based on Decision Tree Methods: A Simulator Study," IJERPH, MDPI, vol. 16(11), pages 1-17, May.
    4. 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.
    5. Liu, Enze & Barker, Kash & Chen, Hong, 2022. "A multi-modal evacuation-based response strategy for mitigating disruption in an intercity railway system," Reliability Engineering and System Safety, Elsevier, vol. 223(C).
    6. 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).
    7. Liu, Zhichen & Li, Ying & Zhang, Zhaoyi & Yu, Wenbo, 2022. "A new evacuation accessibility analysis approach based on spatial information," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
    8. 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).
    9. 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).
    10. Teichmann, Dusan & Dorda, Michal & Sousek, Radovan, 2021. "Creation of preventive mass evacuation plan with the use of public transport," Reliability Engineering and System Safety, Elsevier, vol. 210(C).
    11. Zhou, Jianfeng & Reniers, Genserik, 2022. "Petri-net based cooperation modeling and time analysis of emergency response in the context of domino effect prevention in process industries," Reliability Engineering and System Safety, Elsevier, vol. 223(C).

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