Evacuation Simulation under Threat of Wildfire: History
View Latest Version
Please note this is an old version of this entry, which may differ significantly from the current revision.
Contributor:
Shahab Mohammad Beyki
,
Aldina Santiago
,
Luís Laím
,
Hélder D. Craveiro

Wildfires have become a common incident, and they have been threatening people’s lives and assets. In the communities close to wildlands or wildland–urban interfaces (WUI), these threats become increasingly serious, and in case of wildfires, people are advised or often have to evacuate the area to save their lives. In order to have a safe and effective evacuation, data on people’s behavior and decisions during wildfires, evacuation modeling, and traffic simulations are required. 

  • evacuation
  • evacuation modeling
  • wildfire
  • wildland urban interface

1. Introduction

Wildfires are often considered a necessary incident in the cycle of an ecosystem or a natural disaster that can harm the life of creatures or worsen the habitat conditions, depending on their intensity, place of occurrence, and impacts. The distinction between these two points of view relies strongly on the main effects of wildfire on human and animal lives, the environment, ecosystem, and economy, and whether it is expected and managed or not [1][2][3].
Wildland fires have been recognized as a crucial field for research by many organizations and associations in academic or governing communities over the past years [4]. Over the past decades, an increased number of severe wildfires have occurred all over the world due to environmental changes, global warming, and droughts, even in areas not exposed to the risk of wildfire, for instance, the Nordic countries [4]. Wildland–urban interface (WUI) communities are defined as places “where humans and their development meet or intermix with wildland fuel” [5], which are the most vulnerable to wildfires, given their proximity. Moreover, other risks usually exist in WUI residencies, such as insufficient transportation systems that do not develop enough in comparison to urban development and an increase in population; for instance, many WUI communities have only one road in and out of them, which can cause difficulties during evacuation [6]. Multiple fatalities have been reported as the consequence of a wildfire or occurred during evacuations due to the inadequacy of the rural road. The inadequacy of the rural roads can cause congestion and trap the evacuees (e.g., Pedrogão Grande wildfire, Portugal, 2017). Moreover, delayed evacuation trigger alarms or delays in evacuation advice implementation are other problems that can cause locals to stay until the last minute and face hazardous situations [7].
Rodrigues et al. [8] investigated the causes of death in wildfires in 2017 in Portugal. In this research, factors such as age, place of death, the distance between the place of death to the place of residence, and the decision to flee or evacuate on the causes of death were investigated. Victims of such incidents are categorized into three groups: individuals who realized the threat to their life and had enough time to take protective measures but failed to choose appropriate protective strategies, individuals who did not realize real threats to their lives, and individuals who were physically unable to protect their lives. The analysis of data shows a very important fact. On average, 65% of the victims were people who fled or evacuated without orders or information from authorities but were killed during the process. This clearly demonstrates the importance of an effective, planned, and in-time evacuation in these incidents. In case of a wildfire, an unorganized evacuation will most likely cost lives.
Essential requirements of new or developing WUI communities must be considered by the authorities to perform an effective evacuation in case of a wildfire. For instance, sufficient and detailed informing of the evacuees before and during the evacuation, increasing the road capacity to avoid congestion, preparing additional transportation equipment, evacuation departure time, destination, and sheltering [9]. In contrast, many WUI communities do not meet these requirements; therefore, challenges arise in case of a wildfire, and developing an effective evacuation plan will save lives.
Wildfires, especially at the WUI, raise various challenges for the residential population and the governing authorities in terms of producing a safe and effective plan to preserve the society, protect the infrastructure and residents’ assets, and, if necessary, evacuation. To ensure life safety and evacuation effectiveness, various social and environmental characteristics of WUI communities, which pose different challenges, must be addressed [10]. Heterogeneity of the household density over the WUI, layout and the positioning of the roads, sufficiency of the roads in the WUI communities, and the topography and geography of the surrounding environment are the physical factors that need to be taken into account to produce an effective and safe evacuation [6]. This is in addition to social factors, e.g., age, sex, income, race, and culture of the residents [11][12][13][14]. Even though it is not yet the standard protocol, evacuation simulation models are increasingly used to develop better evacuation strategies for WUI communities [15][16]. These simulation models enable the authorities to manage and plan an effective evacuation by deciding the evacuation trigger or start time, evacuation roads and routes, and traffic management for different wildfire scenarios. This is achieved by forecasting evacuation-affecting factors, i.e., departure time and pattern, travel duration, the mean speed of the evacuees, the traffic length, and flow rate [15].
Various simulation models have been developed for evacuation planning, including models solely produced for evacuations, e.g., TransCAD (https://www.caliper.com/, accessed on 15 July 2023) and TRANSIMS (https://sourceforge.net/projects/transimsstudio/, accessed on 15 July 2023) [17], a model that tries to simulate the wildfire and evacuation simultaneously, e.g., WUI-NITY 2 (https://www.nfpa.org/News-and-Research/Data-research-and-tools/Wildland-Urban-Interface/WUINITY-a-platform-for-the-simulation-of-wildland-urban-interface-fire-evacuation, accessed on 15 July 2023) [18], and simplified simulations and models to define the evacuation trigger by setting up a parameter that would activate once crossed by fire [19][20], e.g., MedSpread (https://sites.google.com/site/medfireproject/medspread-model, accessed on 15 July 2023) [21], HURREVAC-Extended (HVX) (https://www.hurrevac.com/, accessed on 15 July 2023) [22], and WUIVAC (https://link.springer.com/article/10.1007/s11069-006-9032-y, accessed on 15 July 2023) [23]. Fire danger indices are prognostic tools for the occurrence of wildfires. They are also useful tools for the preparation of operational and management forces, which are mostly local services, for example, the Copernicus Fire Danger Forecast System [24], which provides data on the risk of fire in Europe or the Fire Weather Index for the USA [25].
The mentioned models and simulations have different approaches regarding microscale or macroscale points of view. A microscale approach simulates the evacuation scenarios and manages them in small urban areas, like small villages or communities. This microscale approach analyzes the behavior of each individual in terms of decision-making and evacuation means of transport and handles them on a small scale. On the other hand, a macroscale approach must be considered for larger communities to plan and manage an effective evacuation. Due to herding and the mass movement of the population, these types of evacuations must be handled macroscopically to avoid traffic jams or lack of equipment in different community sectors [17][26].

2. Evacuation Warning and Region Division

Wildfires may involve the evacuation of large groups of people from large locations, often across long distances. The increasing frequency of these catastrophes shows that suitable evacuation strategies for wildfire-prone areas are required.
The ability to communicate evacuation orders clearly and effectively is one of the most important requirements for an evacuation. The initial response to disaster warnings is generally one of skepticism, which must be overcome in this communication [27][28]. The urge with which individuals evacuate, the places from which they go, and the destinations they choose can all be greatly influenced by the wording and content of evacuation instructions (the message), the person providing the message (the source), and the distribution channel [29]. It is interesting how vocabulary varies greatly throughout one location while exhibiting little consistency between regions. The use of acronyms on the Internet and in social media could further undermine consistency [30]. Recent research on evacuation procedures revealed the variety of language used by public officials when issuing evacuation orders and the intended meaning they are attempting to communicate. The terms “Mandatory” and “Voluntary” evacuation were the most popular among survey participants. However, it is noteworthy that the legal definition of “mandatory” is unclear because disaster management and law enforcement organizations know that it would be impossible to execute an order requiring citizens to evacuate. However, when advising potential evacuees to flee, the word “mandatory” carries certain significance [31].
The study area can be divided into geographic zones to help let the public know who must evacuate. Depending on the sort of catastrophic event, the number and size of zones may change [32]. Adequate zoning is also very important for evacuation modeling, especially at macro/meso scale models [33].

3. Evacuation Model Design

The evacuation models need information on individuals’ decisions and behaviors in the evacuation process. These pieces of information can include: how many and what kinds of vehicles were used? What roads were used by individuals to reach a safe point? What regions were chosen as safe points? And many others [16]. There is not much available data on wildfire evacuation behavior to help design evacuation models. Most of the research done in the field of wildfire was mainly concentrated on who would evacuate and how to predict traffic needs; even so, they are very limited [34]. Since there are no such data for wildfire, models mostly consider ideal behavior for individuals, such as Leon and March [35], or merely consider one type of movement, for example, only by car or on foot [36][37]. Because evacuation data from WUI fires is scarce, the evacuation models rely on the user’s judgment, which is not based on wildfire [38][39].
Traditional evacuation simulation models have been demonstrated to be too optimistic regarding clearance timeframes and other results [40]. According to a study by Wu et al. [41], evacuees are unlikely to organize themselves optimally along major corridors during hurricanes. When compared to models using ideal assumptions, simulating actual behavior (e.g., individuals delaying evacuation and/or choosing regular routes) [42] can considerably reduce evacuation “effectiveness” [43]. The incorrect assumptions for evacuation behavior under the threat of wildfire can and will endanger the safety of the community. The models that wrongly assume the behavior and decision of individuals have understated evacuation results, such as longer available time to evacuate, which gives false information to authorities and delayed evacuation alarms.
Multiple time periods are involved in the household evacuation procedure. Ronchi et al. [44] describe a generic WUI fire evacuation timetable, which chronologically lists emergency officials’ and households’/evacuees’ activities. After the evacuation alarm broadcast, the total needed time for a family or a household to evacuate can be the summation of specific time increments, including preparation, walking to the vehicle or other means of transport, traveling with the vehicle, and arriving at the safe point. Determining who and how many individuals will participate in evacuation, choosing the safe point as the destination of evacuation, the vehicles or transportation type used to evacuate, and the road network used for the evacuation are the four main parts of designing an evacuation model, which will be explained in the following sub-sections. Driving parameters (such as speeds and flows) are included in the traffic assignment [34].
The type and structure of data required for each phase vary depending on the modeling approach. Macroscale, microscale, and mesoscale modeling methodologies are employed to simulate household behavior and mobility in evacuation models. Macro models present evacuation behavior at a larger level to recognize large patterns in evacuation. This scale of simulation models needs data on the flow pattern of vehicles and their speed, the road network capacity, and road density. Individuals (agents or vehicles) can be simulated using microscale models, which require data about the decision-making of the individuals, behavior, and movement at a community level. A model at the scale of meso mostly focuses on the interactions of individuals between the two previous models’ scales [45]. All the methods need data on the behavior and decision process of individuals, regardless of the scale of the model to model an evacuation model for a WUI fire scenario. The user or operator of such models uses these data plus a safe point or zone as the evacuation destination to create the simulation. Following that, data on vehicle or transportation choice shows the pattern that evacuees are distributed across various modes of transportation of various capacities and capabilities. As a final point, information on the distribution of the vehicles in the road network is required to inform operators about how various vehicles are dispersed and move along the routes [45].
The evacuation under the threat of wildfire modeling research is still in its early stages, and few are available. An agent-based simulation model (ABM) was developed by Grajdura et al. [46] to study the behavior of evacuees in case of a fast-moving wildfire toward a community. A “Post-disaster Survey” and decision tree methods were used to model agent movements and decisions. The survey was conducted on the evacuees in Red Cross shelters just weeks after the evacuation from the 2018 Camp fire in northern California. Another study modeled awareness of the residents, departure, and preparation time in case of a no-notice wildfire evacuation [47]. The effects of age, race, income, and other characteristics of a resident at the time of being alerted to a wildfire were assessed. It was shown that smartphones and a community evacuation plan significantly and positively affect no-notice fire awareness time [47].
Ronchi et al. [44] focused their study on the modeling of wildland–urban interface fire evacuations. Since the most important “layers” of a WUI wildfire, including wildfire, pedestrians, and traffic, were mainly modeled in isolation beforehand, this research presented a framework for evacuation simulation, including all the layers. This study obtained a more realistic framework to simulate wildfire evacuation.
Cova et al. [22] also developed a new method for delimiting wildfire evacuation trigger points using fire spread modeling and geographic information system (GIS). It was suggested that a trigger buffer could be computed using wind, topography, and fuel data in conjunction with estimated evacuation time. This trigger buffer is for a community whereby an evacuation is recommended if a fire crosses the edge of the buffer. Additionally, in an attempt to couple the fire simulation, pedestrian evacuation, and traffic, Wahlqvist et al. [48] developed the WUI-NITY platform, which simultaneously models fire and evacuation to enhance situational awareness in evacuation scenarios. This model is currently the only evacuation model that considers the effects of wildfire on the evacuation process. Mithchell et al. [49] provided a method for creating triggers by linking models for wildfires and evacuations. They used the fire spread model FARSITE to incorporate the earlier theory of Cova et al. [22] and others on triggers into a tool known as PERIL for establishing trigger perimeters around a community. A safety factor was added to account for errors in the computations for an evacuation or a wildfire.
Dapeng Li [50] developed a data-driven evacuation simulation model for wildfire scenarios using different types of data, evacuation simulation models, and GIS to improve evacuation time in holiday homes and resorts. Car ownership and occupancy rates in second homes were considered two important factors in evacuation times, and results showed that they have high correspondence with evacuation time.
Gwyne et al. [51] made benchmarks for evacuation simulation models under the threat of wildfires. Using observations and questionnaires during evacuation drills in Roxburgh Park, Colorado they collected evacuation-related data such as the initial position of residents, time required before evacuation for preparation, route choice and use, and arrival time at specified locations as evacuation destinations. These data were used as inputs for two evacuation simulation models, WUI-NITY platform and Evacuation Management System, that use different modeling approaches over a variety of assumptions in different scenarios to create benchmarks.
Based on the analyzed literature, to design an evacuation model, initially, four specific sub-models must be created. These sub-models include: pedestrian evacuation modeling (Trip Generation), pedestrian sheltering (Destination), evacuation transportation (Mode Choice), and traffic modeling (evacuation routes selection). The following sub-chapters will discuss the available literature on the mentioned steps of the evacuation model design.

4. Evacuation Modeling Packages

As mentioned in previous sections, there are three main approaches in the simulation of an evacuation. Each approach has its specific use and applies to certain situations. This section introduces software used for certain case studies and research, categorized by their approach. Nevertheless, other software is available, but government organizations use them, and there are not much data available.

4.1. Macroscale

The macro models are mostly used to plan evacuations. These models are suitable for analyzing the evacuation process under the threat of hurricanes or floods affecting large areas. Metropolitan planning organizations use macro models to simulate the evacuation process. On the other hand, these models are unsuitable for implementing traffic strategies or assessing the causes of congestion on highways or roads.
Macro models also include real-time decision support tools that help authorities to take appropriate actions during an evacuation operation.
EMME4.6 is a city transportation planning system that provides planners with a complete range of traffic and transportation modeling capabilities. This model can explain, assess, and compare several suggested scenarios simultaneously. This package is a tool to provide a series of trip forecasts. This software can operate between very simple four-step evacuation plans to more advanced and detailed scenarios and their implementation in the road and traffic networks. It also enables the users to analyze different scenarios by changing the roads, transportation system, or even the community’s economic situation. The user could interactively introduce the new data in dataset methods [52].
The Evacuation Traffic Information System (ETIS) is an Internet-based platform that enables southeastern state authorities to share evacuation and traffic data. The ETIS helps with decisions, including which evacuation style to use (voluntary, obligatory, or staged) and whether to use contraflow or lane-reversal operations. The Federal Highway Administration, the United States Army Corps of Engineers, and the Federal Emergency Management Agency created the ETIS. ETIS is a macro model created to forecast huge cross-state traffic flows. Emergency management personnel can use the travel demand forecasting system to view the model online and add data particular to their location. The technology can predict evacuation traffic bottlenecks as well as cross-state traffic movements [53].
The Oak Ridge National Laboratory (ORNL) created OREMS 2.5 (Oak Ridge Evacuation Modeling System) to predict evacuation timelines and to help design evacuation strategies in different circumstances, for instance, evacuation at various times of the day or under different weather conditions. This software enables the user to analyze different evacuation destinations and different or substitute routes to reach there and assess the evacuees’ response rate and traffic management methods for different conditions. OREMS can compute the required time of evacuation and anticipate traffic factors such as the mean speed of the traffic flow, bottlenecks, and other required data to design an evacuation plan. The researchers at ORNL deem it necessary to have this decision tool that includes OREM, and with its output data, the user can analyze the risk areas, provide substitute strategies, assess the traffic operations, and make them more efficient by suggesting methods such as contraflow or use of the road shoulders [54].
TransCAD is an operational package that includes GIS and a macro-level simulation platform. For network evacuation simulation, TransCAD additionally contains a dedicated Evacuation Analysis Procedure. The traffic assessment technique shows how the traffic patterns change through time and space during an evacuation and also gives the clearance time to the operator. The dynamic data on vehicle flow during an evacuation can be used to recognize the bottlenecks. This feature enables the operator to reassess the evacuation plan and analyze the efficiency of other traffic strategies, such as specific signaling of the routes, contraflow, and use of high-capacity vehicles [55].

4.2. Mesoscale

Mesoscale traffic simulation packages arose from a requirement for the amount of detail required by microscale simulation programs as well as the analytical fidelity that macroscale models could not provide. Mesoscale models often show the relative flow of cars over a network link rather than individual lanes. Mesoscale simulation models have been utilized in evacuation planning to depict better congestion situations and time-based impacts than macroscale models while covering a greater area. This feature enables the user to reproduce the congestion and dissipation loops of the previous evacuation to better understand each method’s necessity for the next operations.
DYNASMART is a traffic modeling software on mesoscale that has features to apply dynamic traffic to the road network. This feature gives the operator the ability to forecast where the vehicles are in the road network at specific times rather than only responding to present situations. DYNASMART is available in two varieties: DYNASMART-P 1.0, a standalone simulation software, and DYNASMAT-X, a real-time simulation program with hardware-in-the-loop features. DYNASMART has abilities such as traffic simulation models, which are mostly used for operational traffic studies, and road network applications simulation models, which are mostly used to forecast long-term traffic demands. This software has broader analyzing power compared to macro- or microscale models. This ability is achieved through an explicit explanation of traffic procedures throughout time and a complete demonstration of road networks [56][57]. Also, this software can present a more detailed decision behavior simulation for evacuees compared to macroscale simulators [58][59].
Cube Avenue (https://www.bentley.com/software/cube/, accessed on 1 July 2023) studies traffic flow over time using mesoscopic methods. The researchers defined the input data for this package as time intervals (hours and minutes) and the necessary details required on the inputs, such as vehicles, time, and road networks. The cheapest route for each vehicle unit based on its departure time is computed utilizing these input data. Also, the vehicle’s interaction when moving through the network is simulated. This software calculates the vehicle’s speed by accounting for the number of vehicles in a certain part of the road in time. Cube Avenue may mimic time-specific rules like variable road pricing or lane closures since it directly represents time. In the Houston-Galveston region, the Cube Avenue product was utilized to assist local planners and authorities in the emergency evacuation process. After Hurricane Rita, which led to the evacuation of 24 million people in 24 h, the authorities used this software to recreate the evacuation process to better understand the system’s shortcomings for the next incidents. This software can also create a scenario library by simulating different conditions so that the authorities can react better in case of an accident [60][61].
The Transportation Analysis and Simulation System (TRANSIMS) is a traffic simulation package that considers traffic as a combination of agents or individuals and is mostly used for transportation planning and emission analysis. It comprises simulations, models, and databases that all work together. It offers an integrated regional transportation system study platform combining modern computational and analytical methodologies. The capacity of TRANSIMS to simulate and track individual travel allows for the assessment of benefits to and impacts on various regions and travel markets. TRANSIMS may also assess severely crowded scenarios and operational adjustments on roads and public transportation networks. TRANSIMS’ core principles and structure set it apart from past travel demand forecasting solutions. A consistent and continuous representation of time, a thorough representation of people and homes, time-dependent routing, and a person-based micro simulator are among the advantages [62][63][64].

4.3. Microscale

The simulation models at the microscale are designed to anticipate the detailed traffic behavior of each vehicle or individual in road networks. The vehicles with their own characteristics are moved through the network by micromodels and interact with each other. The fundamental restriction of microscopic models is the lack of computer capacity and the need for significant data on route shape and traffic regulation. Many traffic control organizations use a combination of micro- and macro-level models to better understand each change’s effects on service and transport capacity. Today, the microscale models are used to assess novel methods of emergency evacuation management, such as contraflow, in addition to analyzing the bottlenecks and the effects of each vehicle’s movement on overall traffic. Microscopic transportation simulation has mostly been used to validate predetermined transportation evacuation plans and procedures.
Quadstone Paramics (http://www.paramics-online.com/index.php, accessed on 1 August 2023) [65] is a collection of microscale simulation modules that work together to represent various transportation issues. This software can be utilized on different scales, from a single crossroad to a city’s traffic simulation. The operator can anticipate the start- and end-point decision patterns and an area to develop useful traffic tools. This software is a fully scaled program for simulating a city’s whole traffic system, a crowded motorway, or a single crossroad. The operator does not need any other software to simulate the traffic or generate statistical outputs. Operators may simulate urban, highway, public transit, congested, intelligent transportation system, and high occupancy lanes modes of transportation. Many counties have now identified high-risk zones to assist in enhanced firefighting and evacuation preparations. One goal of this research is to determine how long it would take to remove a residential area during an evacuation. The Mission Canyon Neighborhood was modeled using the Paramics simulation model; this mission was the evacuation due to the wildfire in 1991 in Oakland Hills where 25 people were killed (Church and Sexton 2002) [66]. This package was used to test some traffic simulations at a micro level to design evacuation plans in case of a wildland fire [39].
vrEXODUS v. 5.1.5 [36] was created to satisfy the requirements of performance-based safety standards. It generates people–people, people–fire, and people–structure interactions using a collection of sub-models. EXODUS was created by the University of Greenwich’s Fire Safety Engineering Group. It is a software suite designed for the construction, marine, and aerospace industries. The EXODUS large-scale evacuation modeling system is an agent-based evacuation model that can simulate the evacuation of huge populations—in the hundreds of thousands and in big-scale environments spanning several square kilometers [67]. To let the EXODUS engine more readily depict large-scale urban areas, a desktop interface called urban EXODUS was created [68].
Based on the descriptions of the software, these simulation packages can be effectively utilized in wildfire evacuation cases to model and analyze different aspects of the evacuation process at various scales.

This entry is adapted from the peer-reviewed paper 10.3390/app13179587

References

  1. Robinne, F.-N.; Mimbrero, M.R.; Kim, Y.-S. Fire $, or the economics of wildfires: State of play and new contributions. For. Policy Econ. 2021, 133, 102610.
  2. Pacheco, R.M.; Claro, J. Characterising wildfire impacts on ecosystem services: A triangulation of scientific findings, governmental reports, and expert perceptions in Portugal. Environ. Sci. Policy 2023, 142, 194–205.
  3. Whelan, R.J. The Ecology of Fire, Cambridge Studies in Ecology, 1st ed.; Cambridge University Press: Cambridge, UK, 1995.
  4. McNamee, M.; Meacham, B.; van Hees, P.; Bisby, L.; Chow, W.; Coppalle, A.; Dobashi, R.; Dlugogorski, B.; Fahy, R.; Fleischmann, C.; et al. IAFSS agenda 2030 for a fire safe world. Fire Saf. J. 2019, 110, 102889.
  5. US Department of Agriculture. Report Registry 66; US Department of Agriculture: Washington, DC, USA, 2001.
  6. Cova, T.J. Public Safety in the Urban–Wildland Interface: Should Fire-Prone Communities Have a Maximum Occupancy? Nat. Hazards Rev. 2005, 6, 99–108.
  7. Haynes, K.; Handmer, J.; McAneney, J.; Tibbits, A.; Coates, L. Australian bushfire fatalities 1900–2008: Exploring trends in relation to the ‘Prepare, stay and defend or leave early’ policy. Environ. Sci. Policy 2010, 13, 185–194.
  8. Rodrigues, A.; Santiago, A.; Laím, L.; Viegas, D.X.; Zêzere, J.L. Rural Fires—Causes of Human Losses in the 2017 Fires in Portugal. Appl. Sci. 2022, 12, 12561.
  9. Stopher, P.; Rose, J.; Alsnih, R. Dynamic Travel Demand for Emergency Evacuation: The Case of Bushfires; Camperdown: Sydney, Australia, 2004.
  10. Cohn, P.J.; Carroll, M.S.; Kumagai, Y. Evacuation Behavior during Wildfires: Results of Three Case Studies. West. J. Appl. For. 2006, 21, 39–48.
  11. Folk, L.H.; Kuligowski, E.D.; Gwynne, S.M.V.; Gales, J.A. A Provisional Conceptual Model of Human Behavior in Response to Wildland-Urban Interface Fires. Fire Technol. 2019, 55, 1619–1647.
  12. McCaffrey, S.; Wilson, R.; Konar, A. Should I Stay or Should I Go Now? Or Should I Wait and See? Influences on Wildfire Evacuation Decisions. Risk Anal. 2018, 38, 1390–1404.
  13. McLennan, J.; Ryan, B.; Bearman, C.; Toh, K. Should We Leave Now? Behavioral Factors in Evacuation Under Wildfire Threat. Fire Technol. 2019, 55, 487–516.
  14. Vaiciulyte, S.; Hulse, L.; Veeraswamy, A.; Galea, E. Insight into behavioural itinerary impact on evacuation time delay in wildfires. In ESFSS18; European Symposium on Fire Safety Science: Nancy, France, 2018.
  15. Pel, A.J.; Bliemer, M.C.J.; Hoogendoorn, S.P. A review on travel behaviour modelling in dynamic traffic simulation models for evacuations. Transportation 2012, 39, 97–123.
  16. Alsnih, R.; Stopher, P.R. Review of Procedures Associated with Devising Emergency Evacuation Plans. Transp. Res. Rec. J. Transp. Res. Board 2004, 1865, 89–97.
  17. Hardy, M.; Dodge, L.; Smith, T.; Vásconez, K.C.; Wunderlich, K.E. Evacuation management operations modeling assessment: Transportation modeling inventory. In Proceedings of the 15th World Congress on Intelligent Transport Systems and ITS America’s 2008 Annual Meeting, New York, NY, USA, 16–20 November 2008.
  18. Ronchi, E.; Wahlqvist, J.; Rohaert, A.; Ardinge, A.; Gwynne, S.; Rein, G.; Mitchell, H.; Kalogeropoulos, N.; Kinateder, M.; Bénichou, N.; et al. WUI-NITY: A Platform for the Simulation of Wildland-Urban Interface Fire Evacuation; NFPA: Quincy, MA, USA, 2020.
  19. Countryman, C.M. The Fire Environment Concept; USDA Forest Service Pacific Southwest Forests Range Experimental: Berkeley, CA, USA, 1972.
  20. Li, D.; Cova, T.J.; Dennison, P.E. Setting Wildfire Evacuation Triggers by Coupling Fire and Traffic Simulation Models: A Spatiotemporal GIS Approach. Fire Technol. 2019, 55, 617–642.
  21. Duane, A.; Aquilué, N.; Gil-Tena, A.; Brotons, L. Integrating fire spread patterns in fire modelling at landscape scale. Environ. Model. Softw. 2016, 86, 219–231.
  22. Cova, T.J.; Dennison, P.E.; Kim, T.H.; Moritz, M.A. Setting Wildfire Evacuation Trigger Points Using Fire Spread Modeling and GIS. Trans. GIS 2005, 9, 603–617.
  23. Larsen, J.C.; Dennison, P.E.; Cova, T.J.; Jones, C. Evaluating dynamic wildfire evacuation trigger buffers using the 2003 Cedar Fire. Appl. Geogr. 2011, 31, 12–19.
  24. Fire Danger Indices Historical Data from the Copernicus Emergency Management Service. 2019. Available online: https://cds.climate.copernicus.eu/cdsapp#!/dataset/cems-fire-historical?tab=overview (accessed on 20 July 2023).
  25. Short, K.C.; Finney, M.A.; Vogler, K.C.; Scott, J.H.; Gilbertson-Day, J.W.; Grenfell, I.C. Spatial Datasets of Probabilistic Wildfire Risk Components for the United States (270 m); Forest Service Research Data Archive: Fort Collins, CO, USA, 2020.
  26. Jha, M.; Moore, K.; Pashaie, B. Emergency Evacuation Planning with Microscopic Traffic Simulation. Transp. Res. Rec. J. Transp. Res. Board 2004, 1886, 40–48.
  27. Drabek, T.E. Understanding disaster warning responses. Soc. Sci. J. 1999, 36, 515–523.
  28. Perry, R.W.; Lindell, M.K.; Tierney, K.J. Facing the Unexpected: Disaster Preparedness and Response in the United States; Joseph Henry Press: Washington, DC, USA, 2001.
  29. Lindell, M.K.; Perry, R.W. The Protective Action Decision Model: Theoretical Modifications and Additional Evidence. Risk Anal. 2012, 32, 616–632.
  30. Wolshon, P.B. Transportation’s Role in Emergency Evacuation and Reentry; National Academies: Washington, DC, USA, 2009.
  31. Lindell, M.K.; Murray-Tuite, P.; Baker, E.J. Large-Scale Evacuation: The Analysis, Modeling, and Management of Emergency Relocation from Hazardous Areas; Routledge: New York, NY, USA, 2019.
  32. Arlikatti, S.; Lindell, M.K.; Prater, C.S.; Zhang, Y. Risk Area Accuracy and Hurricane Evacuation Expectations of Coastal Residents. Environ. Behav. 2006, 38, 226–247.
  33. Wilmot, C.G.; Meduri, N. Methodology to Establish Hurricane Evacuation Zones. Transp. Res. Rec. J. Transp. Res. Board 2005, 1922, 129–137.
  34. Kuligowski, E. Evacuation decision-making and behavior in wildfires: Past research, current challenges and a future research agenda. Fire Saf. J. 2021, 120, 103129.
  35. León, J.; March, A. Taking responsibility for ‘shared responsibility’: Urban planning for disaster risk reduction across different phases. Examining bushfire evacuation in Victoria, Australia. Int. Plan. Stud. 2017, 22, 289–304.
  36. Veeraswamy, A.; Galea, E.R.; Filippidis, L.; Lawrence, P.J.; Haasanen, S.; Gazzard, R.J.; Smith, T.E. The simulation of urban-scale evacuation scenarios with application to the Swinley forest fire. Saf. Sci. 2018, 102, 178–193.
  37. Shahparvari, S.; Abbasi, B.; Chhetri, P.; Abareshi, A. Fleet routing and scheduling in bushfire emergency evacuation: A regional case study of the Black Saturday bushfires in Australia. Transp. Res. Part D Transp. Environ. 2019, 67, 703–722.
  38. Adam, C.; Gaudou, B. Modelling Human Behaviours in Disasters from Interviews: Application to Melbourne Bushfires. J. Artif. Soc. Soc. Simul. 2017, 20, 12.
  39. Cova, T.J.; Johnson, J.P. Microsimulation of Neighborhood Evacuations in the Urban–Wildland Interface. Environ. Plan. A Econ. Space 2002, 34, 2211–2229.
  40. Murray-Tuite, P.M.; Mahmassani, H.S. Model of Household Trip-Chain Sequencing in Emergency Evacuation. Transp. Res. Rec. J. Transp. Res. Board 2003, 1831, 21–29.
  41. Wu, H.-C.; Lindell, M.K.; Prater, C.S. Logistics of hurricane evacuation in Hurricanes Katrina and Rita. Transp. Res. Part F Traffic Psychol. Behav. 2012, 15, 445–461.
  42. Bulumulla, C.; Padgham, L.; Singh, D.; Chan, J. The importance of modelling realistic human behaviour when planning evacuation schedules. In Proceedings of the 16th Conference on Autonomous Agents and MultiAgent Systems, São Paulo, Brazil, 8–12 May 2017; pp. 446–454.
  43. Chiu, Y.-C.; Mirchandani, P.B. Online Behavior-Robust Feedback Information Routing Strategy for Mass Evacuation. IEEE Trans. Intell. Transp. Syst. 2008, 9, 264–274.
  44. Ronchi, E.; Gwynne, S.M.V.; Rein, G.; Intini, P.; Wadhwani, R. An open multi-physics framework for modelling wildland-urban interface fire evacuations. Saf. Sci. 2019, 118, 868–880.
  45. Murray-Tuite, P.; Wolshon, B. Evacuation transportation modeling: An overview of research, development, and practice. Transp. Res. Part C Emerg. Technol. 2013, 27, 25–45.
  46. Grajdura, S.; Borjigin, S.; Niemeier, D. Fast-moving dire wildfire evacuation simulation. Transp. Res. Part D Transp. Environ. 2022, 104, 103190.
  47. Grajdura, S.; Qian, X.; Niemeier, D. Awareness, departure, and preparation time in no-notice wildfire evacuations. Saf. Sci. 2021, 139, 105258.
  48. Wahlqvist, J.; Ronchi, E.; Gwynne, S.M.; Kinateder, M.; Rein, G.; Mitchell, H.; Bénichou, N.; Ma, C.; Kimball, A.; Kuligowski, E. The simulation of wildland-urban interface fire evacuation: The WUI-NITY platform. Saf. Sci. 2021, 136, 105145.
  49. Mitchell, H.; Gwynne, S.; Ronchi, E.; Kalogeropoulos, N.; Rein, G. Integrating wildfire spread and evacuation times to design safe triggers: Application to two rural communities using PERIL model. Saf. Sci. 2023, 157, 105914.
  50. Li, D. A data-driven approach to improving evacuation time estimates during wildfires for communities with part-time residents in the wildland-urban interface. Int. J. Disaster Risk Reduct. 2022, 82, 103363.
  51. Gwynne, S.M.V.; Ronchi, E.; Wahlqvist, J.; Cuesta, A.; Villa, J.G.; Kuligowski, E.D.; Kimball, A.; Rein, G.; Kinateder, M.; Benichou, N.; et al. Roxborough Park Community Wildfire Evacuation Drill: Data Collection and Model Benchmarking. Fire Technol. 2023, 59, 879–901.
  52. Rontiris, K.; Crous, W. Emergency evacuation modeling for the Koeberg nuclear power station. In Proceedings of the 2nd Asian EMME12 User’s Meeting, Tokyo, Japan, 8–10 November 2000.
  53. Moriarty, K.D.; Ni, D.; Collura, J. Modeling Traffic Flow under Emergency Evacuation Situations: Current Practice and Future Directions; University of Massachusetts Amherst: Amherst, MA, USA, 2007.
  54. Bhaduri, B.; Liu, C.; Franzese, O. Oak Ridge evacuation modeling system (OREMS): A PC-based computer tool for emergency evacuation planning. In Symposium on GIS for Transportation; AASHTO: Washington, DC, USA, 2006.
  55. Sun, Y.H.; Wang, F.M.; Zhang, F.L. Application of TransCAD Macro Simulation in Traffic Planning. J. Luoyang Inst. Sci. Technol. 2015, 3, 6.
  56. Chiu, Y.-C.; Zheng, H.; Villalobos, J.A.; Peacock, W.; Henk, R. Evaluating Regional Contra-Flow and Phased Evacuation Strategies for Texas Using a Large-Scale Dynamic Traffic Simulation and Assignment Approach. J. Homel. Secur. Emerg. Manag. 2008, 5.
  57. Kwon, E.; Pitt, S. Evaluation of Emergency Evacuation Strategies for Downtown Event Traffic Using a Dynamic Network Model. Transp. Res. Rec. J. Transp. Res. Board 2005, 1922, 149–155.
  58. Mahmassani, H.S. Dynamic traffic assignment and simulation for advanced network informatics (DYNASMART). In The 2nd International Seminar on Urban Traffic Networks; Engineering News-Record: New York, NY, USA, 1992.
  59. Mahmassani, H.S.; Abdelghany, K.F. Dynasmart-IP: Dynamic Traffic Assignment Meso-Simulator for Intermodal Networks. In Advanced Modeling for Transit Operations and Service Planning; Emerald Group Publishing Limited: Bradford, UK, 2002; pp. 200–229.
  60. Sin, H.G.; Joo, Y.J. Development of urban disaster evacuation model using Cube Avenue. Spat. Inf. Res. 2017, 25, 513–521.
  61. Sin, H.G.; Joo, Y.J. A study on prototype model for mesoscopic evacuation using Cube Avenue simulation model. Spat. Inf. Res. 2013, 21, 33–41.
  62. Lee, K.S.; Eom, J.K.; Moon, D. Applications of TRANSIMS in Transportation: A Literature Review. Procedia Comput. Sci. 2014, 32, 769–773.
  63. Nagel, K.; Beckman, R.J.; Barret, C.L. TRANSIMS for urban planning. In Proceedings of the 6th International Conference on Computers in Urban Planning and Urban Management, Venice, Italy, 8–11 September 1999.
  64. Smith, L.; Beckman, R.; Baggerly, K. TRANSIMS: Transportation Analysis and Simulation System; Los Alamos National Lab. (LANL): Los Alamos, NM, USA, 1995.
  65. Hidas, P. A functional evaluation of the AIMSUN, PARAMICS and VISSIM microsimulation models. Road Transp. Res. 2005, 14, 45–59.
  66. Church, R.L.; Sexton, R.M. Modeling Small Area Evacuation: Can Existing Transportation Infrastructure Impede Public Safety? University of California: Santa Barbara, CA, USA, 2002.
  67. Chooramun, N.; Lawrence, P.J.; Galea, E.R. An agent based evacuation model utilising hybrid space discretisation. Saf. Sci. 2012, 50, 1685–1694.
  68. Neis, P.; Zielstra, D.; Zipf, A. The Street Network Evolution of Crowdsourced Maps: OpenStreetMap in Germany 2007–2011. Futur. Internet 2011, 4, 1–21.
More
© Text is available under the terms and conditions of the Creative Commons Attribution (CC BY) license; additional terms may apply. By using this site, you agree to the Terms and Conditions and Privacy Policy.
This entry is offline, you can click here to edit this entry!
ScholarVision Creations
Feedback