Environmental factors in tsunami evacuation simulation: topography, traffic jam, human behaviour
Azin Fathianpour (),
Barry Evans (),
Mostafa Babaeian Jelodar () and
Suzanne Wilkinson ()
Additional contact information
Azin Fathianpour: Massey University
Barry Evans: University of Exeter
Mostafa Babaeian Jelodar: Massey University
Suzanne Wilkinson: Massey University
Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2024, vol. 120, issue 14, No 11, 12797-12815
Abstract:
Abstract The risk a tsunami, a high-rise wave, poses to coastal cities has been highlighted in recent years. Emergency management agencies have become more prepared, and new policies and strategies are in place to strengthen the city's resiliency to such events. Evacuation is a highly effective response to tsunamis, and recent models and simulations have provided valuable insights into mass evacuation scenarios. However, the accuracy of these simulations can be improved by accounting for additional environmental factors that affect the impact of a tsunami event. To this end, this study has been conducted to enhance an evacuation simulation model by considering topography that impacts traffic mobility and speed, traffic congestion, and human behaviour. The updated model was employed to evaluate the effectiveness of Napier City's current evacuation plan, as it can realistically simulate both pedestrian and vehicular traffic movements simultaneously. The simulation demonstrated in this paper was based on a scenario involving an 8.4 Mw earthquake from the Hikurangi subduction interface, which would trigger a tsunami risk in the area. Based on this event, the final evacuation time (time between after the shake is felt and the arrival of the tsunami wave at the shoreline of Napier City) is considered to be 50 min. The results of the MSEM model are presented within two categories, (1) survival rate and (2) safe zone capacity. The evacuation simulation model used to examine the environmental factors in this study is the Micro-Simulation Evacuation Model (MSEM), an agent-based model capable of considering both pedestrian and vehicular interactions. The results showed that the steep pathway to the safe zone would markedly decrease the moving speed and reduce the survival rate, highlighting the need to have supporting vertical evacuation to reduce the number of evacuees heading to steep routes. Additionally, the modelling and assessment of mass evacuation by vehicles has highlighted regions of severe congestion due to insufficient network capacity. Through highlighting such regions, the model aid policy makers with a more targeted approach to infrastructure investment to improve flows of traffic in mass evacuation scenarios and increase survival rates.
Keywords: Emergency management; Evacuation modelling; Pedestrian evacuation (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:
Downloads: (external link)
http://link.springer.com/10.1007/s11069-024-06714-x Abstract (text/html)
Access to the full text of the articles in this series is restricted.
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:spr:nathaz:v:120:y:2024:i:14:d:10.1007_s11069-024-06714-x
Ordering information: This journal article can be ordered from
http://www.springer.com/economics/journal/11069
DOI: 10.1007/s11069-024-06714-x
Access Statistics for this article
Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards is currently edited by Thomas Glade, Tad S. Murty and Vladimír Schenk
More articles in Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards from Springer, International Society for the Prevention and Mitigation of Natural Hazards
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().