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Many coastal communities around the world are threatened by a near-field (or local) tsunami that could inundate the low-lying areas in a matter of minutes after generation. The universal consensus amongst emergency agencies and academic researchers is that a safe evacuation requires an effective response, which is typically assessed by the evacuation time estimate (ETE). ETE is an integral component of community emergency evacuation planning, especially areas prone to tsunamis. This paper aims to investigate the ETE for pedestrian evacuation during a tsunami through two different approaches: (1) the deterministic Least-Cost Distance (LCD) model; and (2) the dynamic Agent-Based Model (ABM). Then, the comparison of the two models in their intrinsic characteristics, strengths and weaknesses, and its applicability was discussed based on methodology behind of the LCD model and ABM. The LCD model was conducted to generate a spatially distributed ETE map, visualizing vulnerable areas where the evacuation time would be insufficient for individuals to reach safety. The ABM investigated uncertainty during tsunami evacuations, such as population distribution, walking speed, and milling time. This paper provides insights into the differences between the LCD model and ABM in terms of methodology and application. It assists the academic researchers and emergency managers, evacuation planners, and decision makers to choose an appropriate method for modeling pedestrian evacuation during tsunami. 

Despite the increase in frequency and intensity of wildfires around the world, little research has examined households’ expectations of evacuation logistics and evacuation time estimate (ETE) components during such rapid-onset disasters. To address this gap, this study analyzes data from 152 household responses affected by the devastating 2018 wildfire in Mati, Greece where the second-deadliest wildfire of the 21st century took place. The questionnaire measured residents’ expectations of how they would respond to a future wildfire. This includes the number of vehicles they would take, their evacuation destination and route choices, and their expected evacuation preparation and travel times. Explanatory variables include risk perceptions, wildfire preparedness, wildfire experience, and demographic characteristics. The univariate results reveal some similarities to, but also some differences from, expected evacuation logistics and ETE components in other natural hazards. Moreover, correlation and regression analyses show that expected evacuation logistics and ETE components are primarily related to wildfire preparedness actions. Comparison of this study’s results with other rapid onset events such as tsunamis and hazardous material incidents, as well as longer onset events such as hurricanes, sheds light on household responses to wildfires. Emergency managers can use the similarities in results across studies to better prepare for wildfire evacuations.