Integrating Evacuation and Storm Surge Modeling Considering Potential Hurricane Tracks: The Case of Hurricane Irma in Southeast Florida

Hurricane Irma, in 2017, made an unusual landfall in South Florida and the unpredictability of the hurricane’s path challenged the evacuation process seriously and left many evacuees clueless. It was likely to hit Southeast Florida but suddenly shifted its path to the west coast of the peninsula, where the evacuation process had to change immediately without any time for individual decision-making. As such, this study aimed to develop a methodology to integrate evacuation and storm surge modeling with a case study analysis of Irma hitting Southeast Florida. For this purpose, a coupled storm surge and wave finite element model (ADCIRC+SWAN) was used to determine the inundation zones and roadways with higher inundation risk in Broward, Miami-Dade, and Palm Beach counties in Southeast Florida. This was fed into the evacuation modeling to estimate the regional clearance times and shelter availability in the selected counties. Findings show that it takes approximately three days to safely evacuate the populations in the study area. Modeling such integrated simulations before the hurricane hit the state could provide the information people in hurricane-prone areas need to decide to evacuate or not before the mandatory evacuation order is given.

Evacuation Behavior in a Subway Train Emergency: A Video-based Analysis

How do people behave in the seconds after they become aware they have been caught up in a real-life transport emergency? This paper presents the first micro-behavioral, video-based analysis of the behavior of passengers during a small explosion and subsequent fire on a subway train. We analyzed the behavior of 40 passengers present in the same carriage as the explosion. We documented the first action of the passengers following the onset of the emergency and described evidence of pro- and anti-social behavior. Passengers’ first actions varied widely. Moreover, anti-social behavior was rare and displays of pro-sociality were more common. In a quantitative analysis, we examined spatial clustering of running behavior and patterns in passenger exit choices. We found both homogeneity and heterogeneity in the running behavior and exiting choices of passengers. We discuss the implications of these findings for the mass emergency literature and for evacuation modeling.

Using Evacuation Drills to Improve Tsunami Evacuation Preparedness and Resilience

This paper presents the use of tsunami evacuation drills within a coastal community in the Cascadia Subduction Zone (CSZ) to better understand evacuation behaviors and thus to improve tsunami evacuation preparedness and resilience. Evacuees' spatial trajectory data were collected by Global Navigation Satellite System (GNSS) embedded mobile devices. Based on the empirical trajectory data, probability functions were employed to model people's walking speed during the evacuation drills. An Evacuation Hiking Function (EHF) was established to depict the speed-slope relationship and to inform evacuation modeling and planning. The regression analysis showed that evacuees' speed was significantly negatively associated with slope, time spent during evacuation, rough terrain surface, walking at night, and distance to destination. We also demonstrated the impacts of milling time on mortality rate based on participants' empirical evacuation behavior and a state-of-the-art CSZ tsunami inundation model. Post-drill surveys revealed the importance of the drill as an educational and assessment tool. The results of this study can be used for public education, evacuation plan assessment, and evacuation simulation models. The drill procedures, organization, and the use of technology in data collection provide evidence-driven solutions to tsunami preparedness and inspire the use of drills in other types of natural disasters such as wildfires, hurricanes, volcanoes, and flooding.

Integrated IEW-TOPSIS and Fire Dynamics Simulation for Agent-Based Evacuation Modeling in Industrial Safety

Emergency events in the industrial sector have been increasingly reported during the past decade. However, studies that focus on emergency evacuation to improve industrial safety are still scarce. Existing evacuation-related studies also lack a perspective of fire assembly point’s analysis. In this research, location of assembly points is analyzed using the multi-criteria decision analysis (MCDA) technique based on the integrated information entropy weight (IEW) and techniques for order preference by similarity to ideal solution (TOPSIS) to support the fire evacuation plan. Next, we propose a novel simulation model that integrates fire dynamics simulation coupled with agent-based evacuation simulation to evaluate the impact of smoke and visibility from fire on evacuee behavior. Factors related to agent and building characteristics are examined for fire perception of evacuees, evacuees with physical disabilities, escape door width, fire location, and occupancy density. Then, the proposed model is applied to a case study of a home appliance factory in Chachoengsao, Thailand. Finally, results for the total evacuation time and the number of remaining occupants are statistically examined to suggest proper evacuation planning.

Computational modeling of fire safety in metro-stations

This research investigates and attempts to quantify the hazards associated with fire in metrostations. The use of numerical simulations for the analysis of fire safety within metro-stations allows for the prediction and analysis of hazards within the built environment. Such approaches form the growing basis of performance based design (PBD), which can optimize design solutions. The simulations utilize Fire Dynamics Simulator (FDS), a Computational Fluid Dynamics (CFD) model and Pathfinder, an evacuation modeling software. The safety of underground metro-stations is analyzed through the simulation of smoke spread and egress modelling. CFD models of TTC’s Union Station and TransLink’s Yaletown Station are developed to allow for simulations of smoke spread scenarios. These models are evaluated in regards to the preservation of tenability and influence on the Available Safe Egress Time (ASET). The egress of metro-stations is modelled and analyzed to determine the Required Safe Egress Time (RSET).

Computational modeling of fire safety in metro-stations

This research investigates and attempts to quantify the hazards associated with fire in metrostations. The use of numerical simulations for the analysis of fire safety within metro-stations allows for the prediction and analysis of hazards within the built environment. Such approaches form the growing basis of performance based design (PBD), which can optimize design solutions. The simulations utilize Fire Dynamics Simulator (FDS), a Computational Fluid Dynamics (CFD) model and Pathfinder, an evacuation modeling software. The safety of underground metro-stations is analyzed through the simulation of smoke spread and egress modelling. CFD models of TTC’s Union Station and TransLink’s Yaletown Station are developed to allow for simulations of smoke spread scenarios. These models are evaluated in regards to the preservation of tenability and influence on the Available Safe Egress Time (ASET). The egress of metro-stations is modelled and analyzed to determine the Required Safe Egress Time (RSET).