Leveraging social networks for efficient hurricane evacuation

One of the important factors affecting evacuation performance is the departure time choices made by evacuees. Simultaneous departures of evacuees can lead to overloading of road networks causing congestion. We are especially interested in cases when evacuees subject to little or no risk of exposure evacuate along with evacuees subject to higher risk of threat (also known as shadow evacuation). One of the reasons for correlated evacuee departures is higher perceived risk of threat spread through social contacts. In this work, we study an evacuation scenario consisting of a high risk region and a surrounding low risk area. We propose a probabilistic evacuee departure time model incorporating both evacuee individual characteristics and the underlying evacuee social network. We find that the performance of an evacuation process can be improved by forcing a small subset of evacuees (inhibitors) in the low risk area to delay their departure. The performance of an evacuation is measured by both average travel time of the population and total evacuation time of the high risk evacuees. We derive closed form expressions for average travel time for ER random network. A detailed experimental analysis of various inhibitor selection strategies and their effectiveness on different social network topologies and risk distribution is performed. Results indicate that significant improvement in evacuation performance can be achieved in scenarios where evacuee social networks have short average path lengths and topologically influential evacuees do not belong to the high risk regions. Additionally, communities with stronger ties improve evacuation performance.     

Joint modeling of evacuation departure and travel times in hurricanes

Hurricanes are costly natural disasters periodically faced by households in coastal and to some extent, inland areas. A detailed understanding of evacuation behavior is fundamental to the development of efficient emergency plans. Once a household decides to evacuate, a key behavioral issue is the time at which individuals depart to reach their destination. An accurate estimation of evacuation departure time is useful to predict evacuation demand over time and develop effective evacuation strategies. In addition, the time it takes for evacuees to reach their preferred destinations is important. A holistic understanding of the factors that affect travel time is useful to emergency officials in controlling road traffic and helps in preventing adverse conditions like traffic jams. Past studies suggest that departure time and travel time can be related. Hence, an important question arises whether there is an interdependence between evacuation departure time and travel time? Does departing close to the landfall increases the possibility of traveling short distances? Are people more likely to depart early when destined to longer distances? In this study, we present a model to jointly estimate departure and travel times during hurricane evacuations. Empirical results underscore the importance of accommodating an inter-relationship among these dimensions of evacuation behavior. This paper also attempts to empirically investigate the influence of social ties of individuals on joint estimation of evacuation departure and travel times. Survey data from Hurricane Sandy is used for computing empirical results. Results indicate significant role of social networks in addition to other key factors on evacuation departure and travel times during hurricanes.

     

Evaluation of different contraflow strategies for hurricane evacuation in Charleston,South Carolina

Abstract

Limited specific evidence is available on the effectiveness of using contraflow as an evacuation traffic management tool. This study was conducted to determine the best combination of strategy options for evacuating Charleston, SC, along route I-26 during the event of a hurricane or other events. PARAMICS microscopic traffic simulator was used to evaluate the impact of each combination of evacuee response timing and traffic control strategy, such as contraflow, with respect to average vehicular travel time and evacuation duration. Analysis revealed the combination of management strategies that created the lowest evacuation durations and travel times for several types of anticipated evacuee responses. Furthermore, a proposed reconfiguration of the I-526/I-26 interchange for contraflow operations produced additional savings in travel times and evacuation durations. These findings support the use of all lanes for contraflow during all evacuations and provide justification to examine a possible reconfiguration of the I-526/I-26 interchange for use during evacuations.     

Evacuation traffic dynamics

Historically, evacuation models have relied on values of road capacity that are estimated based on Highway Capacity Manual methods or those observed during routine non-emergency conditions. The critical assumption in these models is that capacity values and traffic dynamics do not differ between emergency and non-emergency conditions. This study utilized data collected during Hurricanes Ivan (2004), Katrina (2005) and Gustav (2008) to compare traffic characteristics during mass evacuations with those observed during routine non-emergency operations. From these comparisons it was found that there exists a consistent and fundamental difference between traffic dynamics under evacuation conditions and those under routine non-emergency periods. Based on the analysis, two quantities are introduced: “maximum evacuation flow rates” (MEFR) and “maximum sustainable evacuation flow rates” (MSEFR). Based on observation, the flow rates during evacuations were found to reach a maximum value of MEFR followed by a drop in flow rate to a MSEFR that was able to be sustained over several hours, or until demand dropped below that necessary to completely saturate the section. It is suggested that MEFR represents the true measure of the “capacity”. These findings are important to a number of key policy-shaping factors that are critical to evacuation planning. Most important among these is the strong suggestion of policy changes that would shift away from the use of traditional capacity estimation techniques and toward values based on direct observation of traffic under evacuation conditions.     

A conflict-based path-generation heuristic for evacuation planning

Evacuation planning and scheduling is a critical aspect of disaster management and national security applications. This paper proposes a conflict-based path-generation approach for evacuation planning. Its key idea is to decompose the evacuation planning problem into a master and a subproblem. The subproblem generates new evacuation paths for each evacuated area, while the master problem optimizes the flow of evacuees and produce an evacuation plan. Each new path is generated to remedy conflicts in the evacuation flows and adds new columns and a new row in the master problem. The algorithm is applied to a set of large-scale evacuation scenarios ranging from the Hawkesbury-Nepean flood plain (West Sydney, Australia) which require evacuating in the order of 70,000 persons, to the New Orleans metropolitan area and its 1,000,000 residents. Experiments illustrate the scalability of the approach which is able to produce evacuation for scenarios with more than 1200 nodes, while a direct Mixed Integer Programming formulation becomes intractable for instances with more than 5 nodes. With this approach, realistic evacuations scenarios can be solved near-optimally in reasonable time, supporting both evacuation planning in strategic, tactical, and operational environments.     

Evacuee behaviors and factors affecting the tsunami trip generation model: A case study in phang‐nga,Thailand

A significant amount of research has focused on various types of evacuations, but little attention has been given to tsunami evacuation in the past. The purpose of this study was to investigate evacuee behaviors and factors affecting tsunami evacuation. The intention was also to analyze tsunami trip generation models. A data set of evacuation behavior was collected in an affected area, Baan Namkhem, Phang‐Nga Province, Thailand, following the Indian Ocean tsunami of December 26, 2004. The study was undertaken to determine evacuee response patterns in different conditions. Tsunami trip generation models were employed, using a binary logistic regression technique, to estimate the likelihood of evacuees being involved in each response pattern. It was found that the patterns of evacuee response to an emergency are different among the three conditions. Six factors (education level, ownership of the residence, distance to nearest seashore, disaster knowledge, number of household members, and status of respondent — permanent or transient) were found to be statistically significant. The results of this study can be used to improve the efficiency and effectiveness of future evacuation systems in Thailand.     

Compromising system and user interests in shelter location and evacuation planning

Traffic management during an evacuation and the decision of where to locate the shelters are of critical importance to the performance of an evacuation plan. From the evacuation management authority’s point of view, the desirable goal is to minimize the total evacuation time by computing a system optimum (SO). However, evacuees may not be willing to take long routes enforced on them by a SO solution; but they may consent to taking routes with lengths not longer than the shortest path to the nearest shelter site by more than a tolerable factor. We develop a model that optimally locates shelters and assigns evacuees to the nearest shelter sites by assigning them to shortest paths, shortest and nearest with a given degree of tolerance, so that the total evacuation time is minimized. As the travel time on a road segment is often modeled as a nonlinear function of the flow on the segment, the resulting model is a nonlinear mixed integer programming model. We develop a solution method that can handle practical size problems using second order cone programming techniques. Using our model, we investigate the importance of the number and locations of shelter sites and the trade-off between efficiency and fairness.     

Effects of income inequality on evacuation,reentry and segregation after disasters

Large-scale disasters often trigger mass evacuation due to significant damages to urban systems. Understanding the evacuation and reentry (return) process of affected individuals is crucial for disaster management. Moreover, measuring the heterogeneity in the individuals' post-disaster behavior with respect to their socio-economic characteristics is essential for policy making. Recent studies have used large-scale location datasets collected from mobile devices to analyze post-disaster mobility patterns. Despite the availability of such data and the societal importance of the problem, no studies have focused on how income inequality affects the equity in post-disaster mobility. To overcome these research gaps, we overlay mobility data with income information from census to quantify the effects of income inequality on evacuation and reentry behavior after disasters, and the resulting spatial income segregation. Spatio-temporal analysis using location data of more than 1.7 million mobile phone users from Florida affected by Hurricane Irma reveal significant effects of income inequality on evacuation behavior. Evacuees with higher income were more likely to evacuate from affected areas and reach safer locations with less damage on housing and infrastructure. These differences were common among evacuees from both inside and outside mandatory evacuation zones. As a result of such effects of inequality, significant spatial income segregation was observed in the affected areas. Insights on the effects of income inequality on post-disaster mobility and spatial segregation could contribute to policies that better address social equity in pre-disaster preparation and post-disaster relief.     

Location planning for transit-based evacuation under the risk of service disruptions

The effectiveness of transit-based emergency evacuation highly depends on the location of pick-up facilities, resource allocation, and management. These facilities themselves are often subject to service disruptions during or after the emergency. This paper proposes a reliable emergency facility location model that determines both pre-emergency facility location planning and the evacuation operations afterwards, while facilities are subject to the risk of disruptions. We analyze how evacuation resource availability leverages individual evacuees’ response to service disruptions, and show how equilibrium of the evacuee arrival process could be reached at a functioning pick-up facility. Based on this equilibrium, an optimal resource allocation strategy is found to balance the tradeoff between the evacuees’ risks and the evacuation agency’s operation costs. This leads to the development of a compact polynomial-size linear integer programming formulation that minimizes the total expected system cost from both pre-emergency planning (e.g., facility set-up) and the evacuation operations (e.g., fleet management, transportation, and exposure to hazardous surroundings) across an exponential number of possible disruption scenarios. We also show how the model can be flexibly used to plan not only pre-disaster evacuation but also post-disaster rescue actions. Numerical experiments and an empirical case study for three coastal cities in the State of Mississippi (Biloxi, Gulfport, and D’lberville) are conducted to study the performance of the proposed models and to draw managerial insights.     

Pedestrian crowd tactical‐level decision making during emergency evacuations

This paper investigates pedestrian crowd tactical‐level decision making during emergency evacuations. Of particular interest is crowd exit‐choice behaviour. Two sources of stated choice data are collected and combined. One data set is derived from an experiment linked to a real‐life exit choice experience of participants (in a non‐evacuation setting). We examine aspects that have often been taken for granted in the literature in connection with egress behaviour of crowds during emergencies. We quantify evacuees' trade‐off between “distance”, “density”, “exit visibility” and “directional density” as well as the interactive effect between exit visibility and tendency to follow others. A comprehensive random‐utility analysis is conducted ranging from traditionally practiced models to the state‐of‐the‐practice methods such as random‐coefficient nested logit. Our findings suggest that (i) unless evacuees face certain levels of uncertainty in the escape environment; flows of crowd are unlikely to be followed. Otherwise, most evacuees perceive other individuals as potential sources of congestion and extra delay (generalisation to situations where crowd is completely unfamiliar with the egress geometry, however, may require careful scrutiny). (ii) Evacuees mostly prefer visible exits over the exits whose congestion level is unknown to them (i.e. the tendency to minimise ambiguity). (iii) The presence of attribute uncertainty (e.g. exit visibility) significantly changes the impact of observing decisions of others on each individual choice maker. We also found out that (iv) spatial distribution of exits has a significant influence on evacuees' decisions (presenting itself in the form of violating the IIA assumption). (v) The marginal weights that different individuals place upon attributes of exits are significantly heterogeneous. (vi) There is meaningful correlation between certain utility weights of individual evacuees. These behavioural findings can provide significant behavioural insight essential for safe evacuation planning and accurate forecast of evacuees' behaviour. Copyright © 2016 John Wiley & Sons, Ltd.     

Analysis of hurricane evacuee mode choice behavior

The purpose of this study is to explain the evacuee mode choice behavior of Miami Beach residents using survey data from a hypothetical category four hurricane to reveal different evacuees’ plans. Evacuation logistics should incorporate the needs of transit users and car-less populations with special attention and proper treatment. A nested logit model has been developed to explain the mode choice decisions for evacuees’ from Miami Beach who use non-household transportation modes, such as special evacuation bus, taxi, regular bus, riding with someone from another household and another type of mode denoted and aggregated as other. Specifically, the model explains that the mode choice decisions of evacuees’, who are likely to use different non-household transportation modes, are influenced by several determining factors related to evacuees’ socio-demographics, household characteristics, evacuation destination and previous experience. The findings of this study will help emergency planners and policy-makers to develop better evacuation plans and strategies for evacuees depending on others for their evacuation transportation.     

A mixed logit model for predicting exit choice during building evacuations

Knowledge on human behaviour in emergency is crucial to increase the safety of buildings and transportation systems. Decision making during evacuations implies different choices, of which one of the most important concerns is the escape route. The choice of a route may involve local decisions on alternative exits from an enclosed environment. This study investigates the effect of environmental (presence of smoke, emergency lighting and distance of exit) and social factors (interaction with evacuees close to the exits and with those near the decision-maker) on local exit choice. This goal is pursued using an online stated preference survey carried out making use of non-immersive virtual reality. A sample of 1503 participants is obtained and a mixed logit model is calibrated using these data. The model shows that the presence of smoke, emergency lighting, distance of exit, number of evacuees near the exits and the decision-maker and flow of evacuees through the exits significantly affect local exit choice. Moreover, the model indicates that decision making is affected by a high degree of behavioural uncertainty. Our findings support the improvement of evacuation models and the accuracy of their results, which can assist in designing and managing building and transportation systems. The main aim of this study is to enrich the understanding of how local exit choices are made and how behavioural uncertainty affects these choices.     

Estimating road network accessibility during a hurricane evacuation: A case study of hurricane Irma in Florida

Understanding the spatio-temporal road network accessibility during a hurricane evacuation—the level of ease of residents in an area in reaching evacuation destination sites through the road network—is a critical component of emergency management. While many studies have attempted to measure road accessibility (either in the scope of evacuation or beyond), few have considered both dynamic evacuation demand and characteristics of a hurricane. This study proposes a methodological framework to achieve this goal. In an interval of every six hours, the method first estimates the evacuation demand in terms of number of vehicles per household in each county subdivision (sub-county) by considering the hurricane’s wind radius and track. The closest facility analysis is then employed to model evacuees’ route choices towards the predefined evacuation destinations. The potential crowdedness index (PCI), a metric capturing the level of crowdedness of each road segment, is then computed by coupling the estimated evacuation demand and route choices. Finally, the road accessibility of each sub-county is measured by calculating the reciprocal of the sum of PCI values of corresponding roads connecting evacuees from the sub-county to the designated destinations. The method is applied to the entire state of Florida during Hurricane Irma in September 2017. Results show that I-75 and I-95 northbound have a high level of congestion, and sub-counties along the northbound I-95 suffer from the worst road accessibility. In addition, this research performs a sensitivity analysis for examining the impacts of different choices of behavioral response curves on accessibility results.     

Optimal control strategies with an extended cell transmission model for massive vehicular‐pedestrian mixed flows in the evacuation zone

This paper presents an integrated model to design routing and signal plans for massive mixed pedestrian‐vehicle flows within the evacuation zone. The proposed model, with its embedded formulations for pedestrians and vehicles in the same evacuation network, can effectively take their potential conflicts into account and generate the optimal routing strategies to guide evacuees toward either the pickup locations or their parking areas during an evacuation. The proposed model, enhancing the cell transmission model with the notion of sub‐cells, mainly captures the complex movements in the vehicle‐pedestrian flows and can concurrently optimizes both the signals for pedestrian‐vehicle flows and the movement paths for evacuees. An illustrating example concerning the evacuation around the M&T Bank Stadium area has been used to demonstrate the application potential of the proposed model. Copyright © 2013 John Wiley & Sons, Ltd.     

Fleeing from Hurricane Irma: Empirical Analysis of Evacuation Behavior Using Discrete Choice Theory

This paper analyzes the observed decision-making behavior of a sample of individuals impacted by Hurricane Irma in 2017 (n = 645) by applying advanced methods based in discrete choice theory. Our first contribution is identifying population segments with distinct behavior by constructing a latent class choice model for the choice whether to evacuate or not. We find two latent segments distinguished by demographics and risk perception that tend to be either evacuation-keen or evacuation-reluctant and respond differently to mandatory evacuation orders.Evacuees subsequently face a multi-dimensional choice composed of concurrent decisions of their departure day, departure time of day, destination, shelter type, transportation mode, and route. While these concurrent decisions are often analyzed in isolation, our second contribution is the development of a portfolio choice model (PCM), which captures decision-dimensional dependency (if present) without requiring choices to be correlated or sequential. A PCM reframes the choice set as a bundle of concurrent decision dimensions, allowing for flexible and simple parameter estimation. Estimated models reveal subtle yet intuitive relations, creating new policy implications based on dimensional variables, secondary interactions, demographics, and risk-perception variables. For example, we find joint preferences for early-nighttime evacuations (i.e., evacuations more than three days before landfall and between 6:00 pm and 5:59 am) and early-highway evacuations (i.e., evacuations more than three days before landfall and on a route composed of at least 50% highways). These results indicate that transportation agencies should have the capabilities and resources to manage significant nighttime traffic along highways well before hurricane landfall.     

Validation of an agent-based building evacuation model with a school drill

An effective evacuation of buildings is critical to minimize casualties due to natural or anthropogenic hazards. Building evacuation models help in preparing for future events and shed light on possible shortcomings of current evacuation designs. However, such models are seldom compared or validated with real evacuations, which is a critical step in assessing their predictive capacities. This research focuses on the evacuation of a K-12 (kindergarten to 12th grade) school located within the tsunami inundation zone of Iquique, Chile. An agent-based evacuation model was developed to simulate the evacuation of approximately 1500 children and staff from the school during a global evacuation drill carried out for the entire city. The model simulates the motions of heterogeneous human agents, and the simulations were validated using video analysis of the real event. Resulting error estimations between predicted versus measured flow rates and evacuation times are 13.5% and 5.9%, respectively. The good agreement between the simulated and measured values can be attributed to the known distribution of students and staff at the start of the drill, and their known exposure to emergency preparedness protocols. However, the results presented herein show that this mathematical evacuation model can be used for logistical changes in the emergency planning.     

A dynamic evacuation model for pedestrian–vehicle mixed-flow networks

In urban emergency evacuation, a potentially large number of evacuees may depend either on transit or other modes, or need to walk a long distance, to access their passenger cars. In the process of approaching the designated pick-up points or parking areas for evacuation, the massive number of pedestrians may cause tremendous burden to vehicles in the roadway network. Responsible agencies often need to contend with congestion incurred by massive vehicles emanating from parking garages, evacuation buses generated from bus stops, and the conflicts between evacuees and vehicles at intersections. Hence, an effective plan for such evacuation needs to concurrently address both the multi-modal traffic route assignment and the optimization of network signal controls for mixed traffic flows. This paper presents an integrated model to produce the optimal distribution of vehicle and pedestrian flows, and the responsive network signal plan for massive mixed pedestrian–vehicle flows within the evacuation zone. The proposed model features its effectiveness in accounting for multiple types of evacuation vehicles, the interdependent relations between pedestrian and vehicle flows via some conversion locations, and the inevitable conflicts between intersection turning vehicle and pedestrian flows. An illustrating example concerning an evacuation around the M&T stadium area has been presented, and the results indicate the promising properties of our proposed model, especially on reflecting the complex interactions between vehicle and pedestrian flows and the favorable use of high-occupancy vehicles for evacuation operations.     

Pick‐up locations and bus allocation for transit‐based evacuation planning with demand uncertainty

This paper develops a decision‐support model for transit‐based evacuation planning under demand uncertainty. Demand uncertainty refers to the uncertainty associated with the number of transit‐dependent evacuees. A robust optimization model is proposed to determine the optimal pick‐up points for evacuees to assemble, and allocate available buses to transport the assembled evacuees between the pick‐up locations and different public shelters. The model is formulated as a mixed‐integer linear program and is solved via a cutting plane scheme. The numerical example based on the Sioux Falls network demonstrates that the robust plan yields lower total evacuation time and is reliable in serving the realized evacuee demand. Copyright © 2012 John Wiley & Sons, Ltd.     

Analysis of child pick-up during daily routines and for daytime no-notice evacuations

In a no-notice disaster (e.g., nuclear explosion, terrorist attack, or hazardous materials release), an evacuation may start immediately after the disaster strikes. When a no-notice evacuation occurs during the daytime, household members are scattered throughout the regional network, and some family members (e.g., children) may need to be picked up. This household pick-up and gathering behavior was seldom investigated in previous work due to insufficient data; this gap in our understanding about who within families handles child-gathering is addressed here. Three hundred fifteen interviews were conducted in the Chicago metropolitan area to ascertain how respondents planned their response to hypothetical no-notice emergency evacuation orders. This paper presents the influencing factors that affect household pick-up and gathering behavior/expectations and the logistic regression models developed to predict the probability that parents pick up a child in three situations: a normal weekday and two hypothetical emergency scenarios. The results showed that both mothers and fathers were more likely to pick up a child under emergency conditions than they were on a normal weekday. For a normal weekday, increasing the distance between parents and children decreased the probability of parents picking up children; in other words, the farther parents are from their children, the less likely they will pick them up. In an emergency, effects of distance on pick-up behavior were significant for women, but not significant for men; that is, increasing the distance between parents and children decreased the probability that mothers pick up a child, but had a less significant effect on the fathers’ probability. Another significant factor affecting child pick-up behavior/expectations was household income when controlling for distance. The results of this study confirm that parents expect to gather children under emergency conditions, which needs to be accounted for in evacuation planning; failure to do so could cause difficulties in executing the pick-ups, lead to considerable queuing and rerouting, and extend the time citizens are exposed to high levels of risk.     

A review on travel behaviour modelling in dynamic traffic simulation models for evacuations

Dynamic traffic simulation models are frequently used to support decisions when planning an evacuation. This contribution reviews the different (mathematical) model formulations underlying these traffic simulation models used in evacuation studies and the behavioural assumptions that are made. The appropriateness of these behavioural assumptions is elaborated on in light of the current consensus on evacuation travel behaviour, based on the view from the social sciences as well as empirical studies on evacuation behaviour. The focus lies on how travellers’ decisions are predicted through simulation regarding the choice to evacuate, departure time choice, destination choice, and route choice. For the evacuation participation and departure time choice we argue in favour of the simultaneous approach to dynamic evacuation demand prediction using the repeated binary logit model. For the destination choice we show how further research is needed to generalize the current preliminary findings on the location-type specific destination choice models. For the evacuation route choice we argue in favour of hybrid route choice models that enable both following instructed routes and en-route switches. Within each of these discussions, we point at current limitations and make corresponding suggestions on promising future research directions.