Effects of Data Resolution and Human Behavior on Large Scale Evacuation Simulations

Traffic Analysis Zones (TAZ) based macroscopic simulation studies are mostly applied in evacuation planning and operation areas. The large size in TAZ and aggregated information of macroscopic simulation underestimate the real evacuation performance. To take advantage of the high resolution demographic data LandScan USA (the zone size is much smaller than TAZ) and agent-based microscopic traffic simulation models, many new problems appeared and novel solutions are needed. A series of studies are conducted using LandScan USA Population Cells (LPC) data for evacuation assignments with different network configurations, travel demand models, and travelers compliance behavior. First, a new Multiple Source Nearest Destination Shortest Path (MSNDSP) problem is defined for generating Origin Destination matrix in evacuation assignments when using LandScan dataset. Second, a new agent-based traffic assignment framework using LandScan and TRANSIMS modules is proposed for evacuation planning and operation study. Impact analysis on traffic analysis area resolutions (TAZ vs LPC), evacuation start times (daytime vs nighttime), and departure time choice models (normal S shape model vs location based model) are studied. Third, based on the proposed framework, multi-scale network configurations (two levels of road networks and two scales of zone sizes) and three routing schemes (shortest network distance, highway biased, and shortest straight-line distance routes) are implemented for the evacuation performance comparison studies. Fourth, to study the impact of human behavior under evacuation operations, travelers compliance behavior with compliance levels from total complied to total non-complied are analyzed.Comment: PhD dissertation. UT Knoxville. 130 pages, 37 figures, 8 tables. University of Tennessee, 2013. http://trace.tennessee.edu/utk_graddiss/259

The contribution of tsunami evacuation analysis to evacuation planning in Chile: Applying a multi-perspective research design

Research on evacuation behavior in natural disasters provides a valuable contribution in the development of effective short- and long-term strategies in disaster risk management (DRM). Many studies address evacuation simulation utilizing mathematical modeling approaches or GIS-based simulation. In this contribution, we perform a detailed analysis of an entire evacuation process from the decision to evacuate right up to the arrival at a safe zone. We apply a progressive research design in the community of Talcahuano, Chile by means of linking a social science approach, deploying standardized questionnaires for the tsunami affected population, and a GIS-based simulation. The questionnaire analyzes evacuation behavior in both an event-based historical scenario and a hypothetical future scenario. Results reveal three critical issues: evacuation time, distance to the evacuation zone, and method of transportation. In particular, the excessive use of cars has resulted in congestion of street sections in past evacuations, and will most probably also pose a problem in a future evacuation event. As evacuation by foot is generally recommended by DRM, the results are extended by a GIS-based modeling simulating evacuation by foot. Combining the findings of both approaches allows for added value, providing more comprehensive insights into evacuation planning. Future research may take advantage of this multi-perspective research design, and integrate social science findings in a more detailed manner. Making use of invaluable local knowledge and past experience of the affected population in evacuation planning is likely to help decrease the magnitude of a disaster, and, ultimately, save lives

Hurricane Evacuation Modeling Using Behavior Models and Scenario-Driven Agent-Based Simulations

Transportation modeling and simulation play an important role in the planning and management of emergency evacuation. It is often indispensable for the preparedness and timely response to extreme events occurring in highly populated areas. Reliable and robust agent-based evacuation models are of great importance to support evacuation decision making. Nevertheless, these models rely on numerous hypothetical causal relationships between the evacuation behavior and a variety of factors including socio-economic characteristics and storm intensity. Understanding the impacts of these factors on evacuation behaviors (e.g., destination and route choices) is crucial in preparing optimal evacuation plans. This paper aims to contribute to the literature by integrating well-calibrated behavior models with an agent-based evacuation simulation model in the context of hurricane evacuation. Specifically, discrete choice models were developed to estimate the evacuation behaviors based on large-scale survey data in Northern New Jersey. Monte-Carlo Markov Chain (MCMC) sampling method was used to estimate evacuation propensity and destination choices for the whole population. Finally, evacuation of over a million residents in the study area was simulated using agent-based simulation built in MATSim. The agent-based modeling framework proposed in this paper provides an integrated methodology for evacuation simulation with specific consideration of agents’ behaviors. The simulation results need to be further validated and verified using real-world evacuation data

Three Essays on the Behavioral Responses to Coastal Hazards and Vulnerability

This dissertation consists of three papers in environmental and natural resource economics. The first paper estimates the value of statistical lives (VSL) from hurricane evacuation behavior through an empirical analysis. I present empirical models that predict individuals\u27 willingness to pay (WTP) for avoiding hurricane risks revealed through their evacuation behavior. Using survey data from Texas residents (who were affected by Hurricane Ike), I analyze the individuals’ hurricane evacuation decisions and their corresponding WTP for evacuation. I also estimate the individuals\u27 WTP for avoiding hurricane risks under both voluntary and mandatory evacuation orders and calculate the associated VSL. The findings can be useful to emergency management agencies for evacuation planning. In the second paper, I study market responses to multiple hurricanes based on evidence from real estate sales data. Unlike earlier studies that examined the effect of hurricane exposures on property value, the present study considers how multiple hurricane hits affect the home value. I use repeat sales data from three counties in Florida from 2000 to 2010 and develop a hedonic price model. The findings identify the determinants that influence the property value and provide valuable insights for homebuyers and sellers. The study also provides useful insights regarding the benefits of hurricane mitigations to Florida residents and beyond. The third paper investigates the time preference and the dynamics of evacuation behavior based on evidence from Hurricane Ike and Hurricane Sandy. This paper contributes to the literature on households’ evacuation timing decisions by investigating the factors influencing people’s time preference for evacuation behavior. Unlike other studies, I examine the residents’ evacuation behavior across the Gulf coast as well as the Northeast and Mid-Atlantic coasts from a comparative perspective. I use one survey dataset from Texas residents who experienced Hurricane Ike and another survey dataset from the Northeastern and Mid-Atlantic US states that were affected by Hurricane Sandy. The results provide insights for future hurricane evacuation planning and emergency management

Many-particle simulation of the evacuation process from a room without visibility

We study the evacuation process from a smoky room by means of experiments and simulations. People in a dark or smoky room are mimicked by ``blind'' students wearing eye masks. The evacuation of the disoriented students from the room is observed by video cameras, and the escape time of each student is measured. We find that the disoriented students exhibit a distinctly different behavior compared with a situation in which people can see and orient themselves. Our experimental results are reproduced by an extended lattice gas model taking into account the empirically observed behavior. Our particular focus is on the mean value and distribution of escape times. For a large number of people in the room, the escape time distribution is wide because of jamming. Surprisingly, adding more exits does not improve the situation in the expected way, since most people use the exit that is discovered first, which may be viewed as ``herding effect'' based on accoustic interactions. Moreover, the average escape time becomes minimal for a certain finite number of people in the dark or smoky room. These non-linear effects have practical implications for emergency evacuation and the planning of safer buildings.Comment: For related work see http://www.helbing.or

Multi-scale Models for Transportation Systems Under Emergency Conditions

The purpose of this study is to investigate human behavior in emergencies. More specifically, agent-based simulation and social force models were developed to examine the impact of various human and environmental factors on the efficiency of the evacuation process, through a series of case studies. The independent variables of the case studies include the number of exits, the number of passengers, the evacuation policies, and instructions, as well as the queue configuration and wall separators. The results revealed the location of the exits, number of exits, evacuation strategies, and group behaviors all significantly impact the total time of the evacuation. For the queue configuration, short aisles lower infection spread when rope separators were used. The findings provide new insights in designing layout, planning, practice, and training strategies for improving the effectiveness of the pedestrian evacuation process under emergency

Towards modeling civilian behavior during a natural disaster

This work presents a literature review on civilian evacuation behavior and agent based modeling for disaster planning. The aim is to understand a regional response to a natural disaster. We propose a study to investigate our study area's disaster plan and civilian response at large during such an event. The implications of our proposed work include improved disaster planning and a framework for modeling civilian response to real government disaster plans