Large-Scale Evacuation

The Analysis, Modeling, and Management of Emergency Relocation from Hazardous Areas

Michael K. Lindell, Pamela Murray-Tuite, Brian Wolshon, Earl J. Baker

• Publisher: CRC Press
• Year: 2018
• eBook ISBN: 9781351645324
• Topic: Politics & International Relations
• Subtopic: Politics
• Index: Politics & International Relations

Large-Scale Evacuation introduces the reader to the steps involved in evacuation modelling for towns and cities, from understanding the hazards that can require large-scale evacuations, through understanding how local officials decide to issue evacuation advisories and households decide whether to comply, to transportation simulation and traffic management strategies. The author team has been recognized internationally for their research and consulting experience in the field of evacuations. Collectively, they have 125 years of experience in evacuation, including more than 140 projects for federal and state agencies.

The text explains how to model evacuations that use the road transportation network by combining perspectives from social scientists and transportation engineers, fields that have commonly approached evacuation modelling from distinctly different perspectives. In doing so, it offers a step-by-step guide through the key questions needed to model an evacuation and its impacts to the evacuation route system as well as evacuation management strategies for influencing demand and expanding capacity. The authors also demonstrate how to simulate the resulting traffic and evacuation management strategies that can be used to facilitate evacuee movement and reduce unnecessary demand. Case studies, which identify key points to analyze in an evacuation plan, discuss evacuation termination and re-entry, and highlight challenges that someone developing an evacuation plan or model should expect, are also included.

This textbook will be of interest to researchers, practitioners, and advanced students.

Chapter 1

Introduction and Overview

Evacuation is a protective action that involves people relocating from a threatened area to a safer area. As Perry (1978) noted, evacuations can differ with respect to a number of different dimensions. These include their timing in relation to disaster impact (pre-impact or post-impact) and their duration—ranging from perhaps a few hours to permanent relocation. In addition, they differ in their degree of pre-impact planning (from completely improvised to substantially planned), the number of people involved (ranging from one person to millions of people), and the distance to safety (ranging from a few feet to many miles). The simplest evacuations—such as well practiced building fire evacuations—involve only a few people, require walking only a short distance, are well planned and exercised, take place pre-impact, and last only a short time.

At the other extreme are mass evacuations that involve millions of people evacuating tens or hundreds of miles in vehicles, require a significant amount of improvisation despite a substantial amount of planning, take place before or after disaster impact has disrupted communication and transportation systems, and displace people for weeks, months, years, or even permanently. It is these mass evacuations that are the focus of this book, particularly the need to develop evacuation plans that are based on empirical data about how households respond to environmental threats coupled with engineering models of traffic flows.

For many decades, practitioners and researchers have sought new techniques and systems to move people faster and more safely during evacuations. Some of these methods and strategies have focused on evacuees directly—using improved methods of communication to help them make faster and better informed decisions. Others have focused on transportation systems to better utilize personnel, modal, technological, and infrastructure resources to move people. Over time, this evolution has brought about major changes in the way evacuations are planned and implemented. It has also resulted in the emergence of specialized areas of emergency management study in the physical and social sciences, engineering, planning, and public administration. This book summarizes the current state of knowledge in many of these fields, with a particular focus on the practical application of this knowledge. It also highlights many of the latest emerging topics that have been identified for needed study in the aftermath of recent high profile evacuations.

Many people are surprised to learn that mass evacuations are quite common. A study of emergencies over a 10-year period showed that evacuations involving 1,000 or more persons occur, on average, about every two weeks somewhere in the United States (Dotson and Jones 2005). However, the large scale attention-grabbing evacuations that capture news headlines are considerably less frequent. In fact, of the events studied, only about 25% of them involved more than 5,000 people and only about 5% of them included 100,000 or more people. Because of their infrequent occurrence, large-scale evacuations can be extremely challenging to implement, so that is why they are the main focus of this book.

Decades of operational experience have shown that when a mass evacuation of an urban area is needed, the methods used to move people become quite complex and can require travel over long distances and over extended periods of time. Not only do such conditions increase the risk of harm in an evacuation zone, they also affect much larger areas. In extreme cases, evacuations can have regional impacts. Past hurricane evacuations in Miami and New Orleans, for example, have impacted travel conditions statewide throughout Florida and Louisiana (Wolshon 2007) and even affected bordering states.

Despite the multitude of conditions that can influence any specific evacuation, the history of prior evacuations indicates that there is actually a small set of key variables and fundamental relationships that govern all evacuation processes. These variables can be expressed in spatial and temporal terms and quantified. This book examines these concepts, describes a theoretical foundation of evacuation processes, and shows how emergency management and transportation professionals can apply evolving scientific and engineering knowledge to improve the practice of large scale mass evacuations.

1.1 Evacuation Fundamentals

The goal of an evacuation is to avoid injuries, loss of life and, to a lesser extent, property damage and economic loss. Thus, a primary objective is to move all evacuees outside of a threat area as safely and as quickly as possible. The time it takes to clear the last person from a danger zone after the recognition of a threat is commonly referred to as clearance time, which is also referred to as an evacuation time estimate (ETE). Clearance times for mass evacuations vary widely based on the

• characteristics of a hazard,
• size and response of the evacuating population,
• road network through which evacuees must move,
• adverse travel conditions such as heat, darkness, and precipitation.

The characteristics of these four variables effectively dictate the clearance times of all evacuations. And, although evacuations vary widely in terms of the specific attributes and scope of these four variables, they can be scaled up or down to describe, quantify, and assess all evacuations within a spatiotemporal framework. Ultimately, these four variables are used to define the demand and supply conditions of all evacuation processes.

Evacuation demand is, fundamentally, the number of people—and more specifically, the number of vehicles—that seek to use an evacuation route system (ERS)—the portion of the road network that authorities encourage people to use for their trips to safety. Evacuation demand is more precisely described as the number of vehicles per hour that attempt to depart from each origin via each path to each destination. Conversely, evacuation supply is the ability of the ERS to serve the demand placed upon it. Supply, in an evacuation context, may be described in a number of ways but, fundamentally, it is the ERS’s outflow capacity in terms of the number of vehicles per hour that can exit the risk area. More specifically, supply is a function of link capacity and network geometry. Link capacity can be defined simply in terms of the number of vehicles per hour that can move through a given section of the ERS. Consequently, local authorities typically designate the highways with the greatest capacities as the ERS. However, network geometry is also an important determinant of evacuation supply because total ERS capacity is equal to the sum of the individual link capacities only if the links are parallel to each other. For example, if an ERS consisted of two parallel evacuation links, each with a capacity of 800 vph, it would have a capacity of 1,600 vehicles per hour (vph). However, total ERS capacity will be the smaller of the individual link capacities if the links are serial. For example, if an ERS consisted of two serial evacuation links, one with a capacity of 800 vph and the other with a capacity of 400 vph, it would only have a capacity of 400 vph. Transportation networks are typically more complex than this example as a given route consists of a series of links and nodes (e.g., intersections). Multiple routes need to have no common links in order for capacity to be additive across the routes.

Another important consideration in evacuation analysis is that neither demand nor supply variables remain static throughout an evacuation. Both are influenced by spatial and temporal conditions that vary during an emergency. In most emergencies, evacuation traffic demand rises over time until it reaches a peak. For example, information about changing threat conditions and phased evacuation notices produce different evacuee departure times from different origins travelling to different destinations via different paths. Evacuation supply can decrease due to bottlenecks at merging highways, lanes blocked by vehicle breakdowns, and hazards such as flooding. The dynamic nature of evacuation demand and supply adds an additional layer of complexity to evacuation planning and management.

In summary, clearance time is estimated as a function of evacuation demand and supply. When supply exceeds demand, vehicles can evacuate at the rate defined by the level of evacuation demand. However, when demand exceeds supply, the situation becomes more complex because queues will form that can decrease link capacities below their nominal values and, thus, increase clearance time—sometimes dramatically. Thus, the challenge for emergency managers and transportation officials is to employ demand management techniques such as phased evacuations (Zhang, Spansel, and Wolshon 2014b) and supply management techniques such as contraflow (Wolshon 2001) to balance demand and supply and, thus, reduce clearance time. These techniques are described in detail later in this book.

1.2 Evacuation Modeling

Among the most significant advances in evacuation analysis and planning over the past four decades has been the development of quantitative models of evacuation processes (see Murray-Tuite and Wolshon 2013b; Lindell 2013). One contribution has been the development of mathematical models of evacuee demand and another contribution has been the development of simulation and optimization models for computing clearance times. Mathematical models of evacuation demand have taken two forms, aggregate and microscopic. The aggregate models have been used to characterize evacuation model variables such as average evacuation rates (Baker 1991), average percentage of evacuees seeking accommodations in public shelters (Mileti, Sorensen, and O’Brien 1992), and the distributions of warning reception times (Lindell and Perry 1987). However, microscopic models are increasingly being used to predict these evacuation model variables. There has been an extensive line of research on the prediction of households’ evacuation decisions with models ranging from the simple cross-tabulation of evacuation rates by hurricane category and risk area (Lindell and Prater 2007) to multi-stage, multi-equation models involving social/environmental cues; warning source, channel, and message; previous experience, social and environmental context, psychological variables, and demographic variables (see Huang et al. 2016a for an example and Huang et al. 2016b for a review). There has also been research on models to predict other evacuation model variables such as departure time (Hasan, Mesa-Arango, and Ukkusuri 2013) and evacuation destination (Mesa-Arango, Hasan, Ukkusuri, and Murray-Tuite 2013).

There has also been substantial development of simulation and optimization models that can integrate data from evacuee demand models with increasingly detailed ERS models to generate ETEs. As noted by Davidson and Nozick (2017), optimization models define a problem in terms of decision variables (controllable variables whose optimal values are to be determined), an objective function (the overall measure of performance to be minimized or maximized), and constraints (restrictions on the permissible values of the decision variables). By contrast, simulation models define a problem in terms of causal relationships among variables. Moreover, evacuation models are typically stochastic (having some element of randomness to their inputs) and dynamic (modeling the system’s evolution over time).

Evacuation modeling serves numerous purposes, the most important of which is to estimate the number of people reaching safety by a given time and, conversely, to determine the time by which authorities need to issue evacuation notices in order for everyone to reach safety prior to a hazard’s arrival. In addition, these models can identify traffic congestion locations, estimate the demand for space in public shelters, test scenarios that have not occurred previously, evaluate strategies that could facilitate evacuee movement, and assess the sensitivity of ETEs to plausible variations in the input parameters.

Evacuation models should take into consideration the interactions of the hazard, population, evacuation management agencies (emergency management, transportation, police, and transit agencies), and the transportation infrastructure, as displayed in Figure 1.1. This figure not only represents the process as it unfolds in an actual evacuation, but also as it is simulated. For example, the dashed lines connecting the hazard event to evacuation management agencies and local households represent information that these community stakeholders obtain about the unfolding event. The solid lines represent impacts that the hazard can have on the ability of these community stakeholders to respond.

Figure 1.1 General Evacuation Modeling and Planning Framework

The dashed lines between the two stakeholder groups reflect the exchange of information between them, with evacuation management agencies seeking to influence households indirectly through the news media, but also directly through agency Internet sites and social media accounts. Households provide feedback by accessing agency rumor control centers and posting on social media. Both stakeholder groups obtain information about the transportation infrastructure—the evacuation management agencies through infrastructure monitoring devices such as CCTV and the households through the news media. In turn, the local population can degrade the transportation infrastructure through traffic incidents such as lane-blocking collisions, whereas evacuation management agencies can enhance the transportation infrastructure through supply management actions such as contraflow. The solid line from the hazard event to infrastructure represents adverse impacts that reduce ERS capacity whereas the solid line from evacuation management agencies to infrastructure represents interventions that maintain or increase ERS capacity. The solid line from households to simulated or actual evacuation represents the demand model and the solid line from the transportation infrastructure to the simulated or actual evacuation represents the supply model. Finally, the dashed line from the simulated or actual evacuation to the evacuation management agencies represents the feedback to them about clearance times and other measures of effectiveness (for actual evacuations) and ETEs (for simulations).

A long history of research in the social sciences has explored the relationships among the hazard, population (and their preferences and constraints), and warning messages. This social science research has informed further research into the development of spatiotemporal travel demand models that can be used with traffic simulation tools to produce ETEs that predict network clearance time. Assessing demand requires addressing the following questions:

• How many vehicles are entering the ERS?
• When are they entering the ERS?
• Where are they entering the ERS (i.e., what are their origins)?
• Wha...

Table of contents

• Cover
• Half Title
• Title Page
• Copyright Page
• Table of Contents
• Detailed Contents
• List of Figures
• List of Tables
• Acknowledgements
• List of Acronyms
• Chapter 1. Introduction and Overview
• Chapter 2. Natural and Technological Hazards Requiring Evacuation Management
• Chapter 3. Protective Actions and Protective Action Decision Making
• Chapter 4. Who Leaves and Who Does Not
• Chapter 5. When Do Evacuees Leave?
• Chapter 6. Managing Evacuation Logistics
• Chapter 7. Evacuation Behavioral Forecasts
• Chapter 8. Strategies for Managing Evacuation Demand and Capacity
• Chapter 9. Evacuation Traffic Modeling and Simulation
• Chapter 10. Evacuation Termination and Reentry
• Chapter 11. Case Studies
• Glossary
• Index