the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
An Interdisciplinary Agent-based Evacuation Model: Integrating Natural Environment, Built environment, and Social System for Community Preparedness and Resilience
- 1School of Civil and Construction Engineering, Oregon State University, Corvallis, OR 97331
- 2School of Electrical Engineering and Computer Science, Oregon State University, Corvallis, OR 97331
- 3Department of Urban Design and Planning, University of Washington, Seattle, WA 98195
- 1School of Civil and Construction Engineering, Oregon State University, Corvallis, OR 97331
- 2School of Electrical Engineering and Computer Science, Oregon State University, Corvallis, OR 97331
- 3Department of Urban Design and Planning, University of Washington, Seattle, WA 98195
Abstract. Previous tsunami evacuation simulations have mostly been based on arbitrary assumptions or inputs adapted from non-emergency situations, but a few studies have used empirical behavior data. This study bridges this gap by integrating empirical decision data from local evacuation expectations surveys and evacuation drills into an agent-based model of evacuation behavior for a Cascadia Subduction Zone community. The model also considers the impacts of liquefaction and landslides from the earthquake on tsunami evacuation. Furthermore, we integrate the slope-speed component from Least-cost-distance to build the simulation model that better represents the complex nature of evacuations. The simulation results indicate that milling time and evacuation participation rate have significant non-linear impacts on tsunami mortality estimates. When people walk faster than 1 m/s, evacuation by foot is more effective because it avoids traffic congestion when driving. We also find that evacuation results are more sensitive to walking speed, milling time, evacuation participation, and choosing the closest safe location than to other behavioral variables. Minimum tsunami mortality results from maximizing the evacuation participation rate, minimizing milling time, and choosing the closest safe destination outside of the inundation zone. This study's comparison of the agent-based model and BtW model finds consistency between the two models' results. By integrating the natural system, built environment, and social system, this interdisciplinary model incorporates substantial aspects of the real world into the multi-hazard agent-based platform. This model provides a unique opportunity for local authorities to prioritize their resources for hazard education, community disaster preparedness, and resilience plans.
Chen Chen et al.
Status: final response (author comments only)
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RC1: 'Comment on nhess-2021-370', Erick Mas, 05 Jan 2022
This is a well-written and easy-to-understand paper, however, with limited novelty and contribution, at least from the way it is presented.
The main contribution flagged by the authors is the use of ‘empirical data’ to feed agent behaviors in the model. I found that using local evacuation expectations surveys is not a new approach, so I consider the gap is not being filled here.
In contrast, evacuation drill data can be an essential source to elucidate evacuee behavior, however in some cases also inaccurate compared to the actual behavior in an emergency.
It is not clear how the evacuation drill data is leveraged in the study. Only travel speed is adjusted based on the data gathered from the drill, and a modified hiking function is proposed. Changing the hiking function with empirical data from the drill is understandable. Still, the applicability of such a function holds the same uncertainty as the original function since both come from physical experiments and not from a real tsunami situation.
I think the authors should not stress the use of evacuation drill data (empirical data) as a novelty since this is another non-emergency-related behavior, and its superiority compared to standard physical experiments cannot be proved.
On the other hand, site-specific analysis becomes helpful in a particular area. The authors have made an excellent effort to explore the effect of walking speeds on mortality rate.
Overall the manuscript can be considered a valuable resource for Coos Bay authorities, though very limited in scientific advancement in the field.
Authors can find further comments in the attached document.- AC1: 'Reply on RC1', Chen Chen, 21 Apr 2022
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RC2: 'Comment on nhess-2021-370', Anonymous Referee #2, 09 Mar 2022
This is an interesting study that explored evacuation efficiencies of a coastal community in face of tsunami threats. It considered natural environment, built environment, and social systems that have an impact on the emergency evacuations. Though I have some concerns on specific contents of the model, I suggest to consider it for publication after major revisions. Please see my detailed comments below.
- The BtW model in abstract shall be spelled out. So is the LCD in introduction.
- I see there is a Tsunami inundation layer in the model but no simulation of tsunami process. Is the tsunami inundation considered stable from the beginning to the end of the model? I mean do you consider the tsunami process from the start of the tsunami from the coast, the rising of water depth, the extension of inundation areas to inland and the decline of tsunami water? It can make big difference if the tsunami inundation is dynamic or stable.
- The authors always stress the unique use of empirical data and evacuation drilling data in this study. But using different data only is not sufficient to be an innovative study. Could you clarify other innovative aspects of the study, e.g. in terms of methodology, evacuation theory or others?
- Tsunami is not as flood water that may rise over a time period (e.g. in several hours), but likely occurs and threats people in minutes or seconds. Every second matters in such a tsunami triggered by earthquake. So it is important to know what kind of tsunami is simulated in the study, better with more details of the tsunami scenario.
- Figure 1, please enlarge the map, while the curve plots can be smaller. The pedestrians and cars can not be seen. And, what do the colors in the map mean?
- It is still hard to understand the process of people evacuation from receiving warnings to being evacuated. How do people make decisions and how much time do each activity take? I suppose a flow chart would be helpful to illustrate the decision behaviors, process and their time needs. The authors may want to refer to the daily routine chart in the study: An agent-based modeling framework for simulating human exposure to environmental stresses in urban areas. Urban Science 2 (2), 36. https://doi.org/10.3390/urbansci2020036
- what does the equation 1 mean? What is x and f(x), and why is it this equation but not others?
- In figure 3b, why is it more percentage of people evacuating by foot when the distance is longer?
- Section 2.3.2. Built Environment shall better be introduced as traffic environment. There is only roads and bridges considered but no buildings at all.
- Figure 6, when milling time is 50 minutes, mortality rate is 100%, which means all people died. This is not realistic unless you assume all people in all areas of the study region will all be in the tsunami water. This requires a sound explanation or major update.
- I assume the very important factors shall include warning time in advance and the location of shelter destinations that could more significantly affect the mortality rate. It would be great if the authors can run the model with some longer warning time and more or less shelter destinations, and to compare the mortality rates. I suppose the result would be more significant than walk speed or travel mode.
- In conclusion, you wrote “Three distinct contributions of this study …” but you actually listed four contributions.
- AC2: 'Reply on RC2', Chen Chen, 21 Apr 2022
Chen Chen et al.
Chen Chen et al.
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