An Agent-Based Exploration of the Hurricane Forecast-Evacuation System Dynamics

In the mainland US, the hurricane-forecast-evacuation system is uncertain, dynamic, and complex. As a result, it is difficult to know whether to issue warnings, implement evacuation management strategies, or how to make forecasts more useful for evacuations. This dissertation helps address these needs, by holistically exploring the system’s complex dynamics from a new perspective. Specifically, by developing – and using – an empirically informed, agent-based modeling framework called FLEE (Forecasting Laboratory for Exploring the Evacuation-system). The framework represents the key, interwoven elements to hurricane evacuations: the natural hazard (hurricane), the human system (information flow, evacuation decisions), the built environment (road infrastructure), and connections between systems (forecasts and warning information, traffic). The dissertation’s first article describes FLEE’s conceptualization, implementation, and validation, and presents proof-of-concept experiments illustrating its behaviors when key parameters are modified. In the second article, sensitivity analyses are conducted on FLEE to assess how evacuations change with evacuation management strategies and policies (public transportation, contraflow, evacuation order timing), evolving population characteristics (population growth, urbanization), and real and synthetic forecast scenarios impacting the Florida peninsula (Irma, Dorian, rapid-onset version of Irma). The third article begins to explore how forecast elements (e.g., track and intensity) contribute to evacuation success, and whether improved forecast accuracy over time translates to improved evacuations outcomes. In doing so, we demonstrate how coupled natural-human models – including agent-based models –can be a societally-relevant alternative to traditional metrics of forecast accuracy. Lastly, the fourth article contains a brief literature review of inequities in transportation access and their implication on evacuation modeling. Together, the articles demonstrate how modeling frameworks like FLEE are powerful tools capable of studying the hurricane-forecast-evacuation system across many real and hypothetical forecast-population-infrastructure scenarios. The research compliments, and builds-upon empirical work, and supports researchers, practitioners, and policy-makers in hazard risk management, meteorology, and related disciplines, thereby offering the promise of direct applications to mitigate hurricane losses

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

Supporting Post-disaster Recovery with Agent-based Modeling in Multilayer Socio-physical Networks

The examination of post-disaster recovery (PDR) in a socio-physical system enables us to elucidate the complex relationships between humans and infrastructures. Although existing studies have identified many patterns in the PDR process, they fall short of describing how individual recoveries contribute to the overall recovery of the system. To enhance the understanding of individual return behavior and the recovery of point-of-interests (POIs), we propose an agent-based model (ABM), called PostDisasterSim. We apply the model to analyze the recovery of five counties in Texas following Hurricane Harvey in 2017. Specifically, we construct a three-layer network comprising the human layer, the social infrastructure layer, and the physical infrastructure layer, using mobile phone location data and POI data. Based on prior studies and a household survey, we develop the ABM to simulate how evacuated individuals return to their homes, and social and physical infrastructures recover. By implementing the ABM, we unveil the heterogeneity in recovery dynamics in terms of agent types, housing types, household income levels, and geographical locations. Moreover, simulation results across nine scenarios quantitatively demonstrate the positive effects of social and physical infrastructure improvement plans. This study can assist disaster scientists in uncovering nuanced recovery patterns and policymakers in translating policies like resource allocation into practice.Comment: 28 pages, 10 figure

The MATSim Network Flow Model for Traffic Simulation Adapted to Large-Scale Emergency Egress and an Application to the Evacuation of the Indonesian City of Padang in Case of a Tsunami Warning

The evacuation of whole cities or even regions is an important problem, as demonstrated by recent events such as evacuation of Houston in the case of Hurricane Rita or the evacuation of coastal cities in the case of Tsunamis. This paper describes a complex evacuation simulation framework for the city of Pandang, with approximately 1,000,000 inhabitants. Padang faces a high risk of being inundated by a tsunami wave. The evacuation simulation is based on the MATSim framework for large-scale transport simulations. Different optimization parameters like evacuation distance, evacuation time, or the variation of the advance warning time are investigated. The results are given as overall evacuation times, evacuation curves, an detailed GIS analysis of the evacuation directions. All these results are discussed with regard to their usability for evacuation recommendations.BMBF, 03G0666E, Verbundprojekt FW: Last-mile Evacuation; Vorhaben: Evakuierungsanalyse und Verkehrsoptimierung, Evakuierungsplan einer Stadt - Sonderprogramm GEOTECHNOLOGIENBMBF, 03NAPAI4, Transport und Verkehr: Verbundprojekt ADVEST: Adaptive Verkehrssteuerung; Teilprojekt Verkehrsplanung und Verkehrssteuerung in Megacitie

An Integrated Agent-Based Microsimulation Model for Hurricane Evacuation in New Orleans

Mass evacuation of urban areas due to hurricanes is a critical problem that requires extensive basic and applied research. Knowing the accurate evacuation time needed for the entire region in advance such that the evacuation order can be issued on a timely basis is crucial for the officials. Microsimulation modeling, which focuses on the characteristics of individual motorists and travel behavior, has been used widely in traffic simulation as it can lead to the most accurate result. However, because detailed driver response modeling and path processing must be incorporated, vehicle-based microscopic models have always been used only to simulate small to medium sized urban areas. Few studies have attempted to address problems associated with mass evacuations using vehicle-based microsimulation at a regional scale. This study develops an integrated two-level approach by separating the entire road network of the study area into two components, highways (i.e., interstate highways and causeways) and local roads. A vehicle-based microsimulation model was used to simulate the highway part of the road traffic, whereas the local part of the road traffic simulation utilized an agent-based model. The integrated microsimulation model was used to simulate hurricane evacuation in New Orleans. Validation results confirm that the proposed model performs well in terms of high model accuracy (i.e., close agreement between the real and simulated traffic patterns) and short model running time. Sufficient evacuation time is a premise to protect people’s life safety when an area is threatened by a deadly disaster. To decrease the network clearance time, this study also examined the effectiveness of three evacuation strategies for disaster evacuation, including a) simultaneous evacuation strategy, b) staged evacuation strategy based on spatial vulnerabilities, and c) staged evacuation strategy based on social vulnerabilities. The simulation results showed that both staged evacuation strategies can decrease the network clearance time over the simultaneous evacuation strategy. Specifically, the spatial vulnerability-based staged evacuation strategy can decrease the overall network clearance time by about four hours, while the social vulnerability-based staged evacuation strategy can decrease the network clearance time by about 2.5 hours

Capturing Pre-evacuation Trips and Associative Delays: A Case Study of the Evacuation of Key West, Florida for Hurricane Wilma

The time it takes for the residents to evacuate an area is calculated as an evacuation time estimate (ETE). In theory, these time estimates are calculated based on a number of inputs, including clearance time, the impact of traffic management techniques, and the time for the public to prepare to evacuate (Dow, 2000). Evacuation models can calculate clearance times, as well as incorporate the temporal impact of traffic management techniques, like contra-flow traffic. However, these models do not include delays associated with pre-evacuation trips. Because these trips are not well represented in hurricane evacuation models, the evacuation time estimate may be miscalculated (Wilmot and Mei, 2004). In order to capture pre-evacuation trip behavior, an online survey of residents’ responses to the evacuation order associated with Hurricane Wilma in 2005 was conducted. Survey data gathered from the residents of Key West, Florida indicate seven important aspects of preevacuation trip making behavior: (1) Socio-demographic variables, which have some association with evacuation behavior, were found to have a very weak to no association with pre-evacuation trip-making; (2) Socio-demographic variables were found to have an association with predicting evacuation behavior for respondents making pre-evacuation trips, and these associations are consistent to what has been found in other studies of evacuation behavior; (3) Delays at stops are longer than delays on links; (4) Trip delays are associated with trip purpose; (5) Residents did not travel from a single origin point directly to an evacuation point, but made various preevacuation trips, often exhibiting trip chaining that included traveling toward as well as away from the city; (6) Though the Key West mandatory evacuation for Hurricane Wilma consisted of a phased evacuation based on housing types, the residents did not evacuate based on housing type, and (7) the personal stories offered by respondents indicate that evacuating or not is often related to job requirements, economic opportunity, previous evacuation experience, and evacuation burnout

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