Article

Traffic Modeling for Wildland–Urban Interface Fire Evacuation

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Article

Traffic Modeling for Wildland–Urban Interface Fire Evacuation

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Abstract

Several traffic modeling tools are currently available for evacuation planning and real-time decision support during emergencies. This paper reviews potential traffic-modeling approaches in the context of wildland-urban interface (WUI) fire-evacuation applications. Existing modeling approaches and features are evaluated pertaining to fire-related, spatial, and demographic factors; intended application (planning or decision support); and temporal issues. This systematic review shows the importance of the following modeling approaches: dynamic modeling structures, considering behavioral variability and route choice; activity-based models for short-notice evacuation planning; and macroscopic traffic simulation for real-time evacuation management. Subsequently, the modeling features of 22 traffic models and applications currently available in practice and the literature are reviewed and matched with the benchmark features identified for WUI fire applications. Based on this review analysis, recommendations are made for developing traffic models specifically applicable to WUI fire evacuation, including possible integrations with wildfire and pedestrian models.

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... A transport modelling framework generally consists of five submodels [74][75][76][77], where the first four sub-models describe the travel choice behaviour and the fifth sub-model describes the (resulting) traffic flows in the transport network. The travel choice behaviour sub-models attempt to predict the decisions that people make both prior to departure and during their trip, and what the collective of these individual decisions yields in terms of travel patterns. ...
... Modal split models [76] predict the mode of transport that evacuees will use. Transport modelling tends to focus on evacuating suburbs and regions where evacuation distances require some form of motorised transport. ...
... Traffic assignment models [74,76,82] predict the route that evacuees will follow. Although the vast majority of evacuation models do explicitly include a traffic assignment sub-model, there are a number of ways to sidestep this sub-model. ...
Article
This document presents the contributions of the Workshop "New approaches to evacuation modelling" that took place on the 11 th of June 2017 in Lund, Sweden within the Symposium of the International Association for Fire Safety Science (IAFSS). The scope of the workshop was to get insights into the building fire evacuation modelling world from experts in areas other than fire safety engineering. The workshop included contributions from five experts in different fields, namely 1) Psychology/Human Factors, 2) Sociology, 3) Applied Mathematics, 4) Transportation, 5) Dynamic simulation and biomechanics. This report presents a collection of the position papers which summarize the presentations given by the experts, the comments, questions and answers session after each presentation and the final workshop discussion.
... A transport modelling framework generally consists of five submodels [74][75][76][77], where the first four sub-models describe the travel choice behaviour and the fifth sub-model describes the (resulting) traffic flows in the transport network. The travel choice behaviour sub-models attempt to predict the decisions that people make both prior to departure and during their trip, and what the collective of these individual decisions yields in terms of travel patterns. ...
... Modal split models [76] predict the mode of transport that evacuees will use. Transport modelling tends to focus on evacuating suburbs and regions where evacuation distances require some form of motorised transport. ...
... Traffic assignment models [74,76,82] predict the route that evacuees will follow. Although the vast majority of evacuation models do explicitly include a traffic assignment sub-model, there are a number of ways to sidestep this sub-model. ...
Article
This paper presents the findings of the workshop “New approaches to evacuation modelling”, which took place on the 11th of June 2017 in Lund (Sweden) within the Symposium of the International Association for Fire Safety Science (IAFSS). The workshop gathered international experts in the field of fire evacuation modelling from 19 different countries and was designed to build a dialogue between the fire evacuation modelling world and experts in areas outside of fire safety engineering. The contribution to fire evacuation modelling of five topics within research disciplines outside fire safety engineering (FSE) have been discussed during the workshop, namely 1) Psychology/Human Factors, 2) Sociology, 3) Applied Mathematics, 4) Transportation, 5) Dynamic Simulation and Biomechanics. The benefits of exchanging information between these two groups are highlighted here in light of the topic areas discussed and the feedback received by the evacuation modelling community during the workshop. This included the feasibility of development/application of modelling methods based on fields other than FSE as well as a discussion on their implementation strengths and limitations. Each subject area is here briefly presented and its links to fire evacuation modelling are discussed. The feedback received during the workshop is discussed through a set of insights which might be useful for the future developments of evacuation models for fire safety engineering.
... Several simulation models exist for use in evacuation planning for WUI communities [14][15]. These include models that are specific to WUI fire evacuation [e.g., 16] and those that attempt to simulate fire and evacuation, including simplified macroscopic models [e.g., 17] and trigger models that identify the location on the landscape that, once crossed by fire, trigger an evacuation for a community [e.g., 18]. ...
... To simulate households' evacuation timeline, traffic models traditionally follow four steps: trip generation (which predicts the number of people who will evacuate and when they will depart), trip distribution (which predicts where [the destination] people travel to reach safety), modal split (which predicts the types of transportation chosen for evacuation), and traffic assignment (which predicts the routes chosen to reach the destination) [12,15]. Included within traffic assignment are driving parameters (e.g., speeds and flows) [14]. ...
... It should be noted that the data (type and format) required for each step differs based on modeling method. Three main modeling methods are used to represent household behavior and movement in evacuation models: macroscopic, microscopic, and mesoscopic [14]. Macroscopic models represent households/traffic behavior at the aggregate level to identify broader trends in evacuation behavior, requiring data on traffic speed and flows, capacities, and densities. ...
Article
Full-text available
Wildfires are becoming more common around the world, and households are frequently advised to evacuate when these fires threaten nearby communities. Effective evacuation requires an understanding of human behavior in wildfires, which is an area that needs further exploration. The purpose of this article is to present current research performed and data collected on evacuation decision-making and behavior during wildland-urban interface (WUI) fires, identify gaps in the research, and develop a future research plan for further data collection of important WUI fire evacuation topics. Research in this area can support developments of evacuation simulation models, and improvements in education programs, planning, decision-making, and design requirements for community-wide WUI fire evacuation.
... The literature shows that 22 traffic tools can be used to simulate the pedestrian and traffic evacuation dynamics of communities affected by wildfires. These models can be used for evacuation planning and real-time decision support during emergencies [9], [10]. Furthers, these modelling solutions present different features when focusing on fire-related, spatial, and demographic factors and can be selected depending on the spatial scale and simulation time of specific case studies [7], [9]. ...
... These models can be used for evacuation planning and real-time decision support during emergencies [9], [10]. Furthers, these modelling solutions present different features when focusing on fire-related, spatial, and demographic factors and can be selected depending on the spatial scale and simulation time of specific case studies [7], [9]. A comprehensive review of these modelling solutions and tools is available in [7], [9], [11]. ...
... Furthers, these modelling solutions present different features when focusing on fire-related, spatial, and demographic factors and can be selected depending on the spatial scale and simulation time of specific case studies [7], [9]. A comprehensive review of these modelling solutions and tools is available in [7], [9], [11]. Most of these models were originally developed to simulate traffic in non-emergency conditions and have been adapted to simulate wildfire evacuation conditions (see for instance [12]- [16]). ...
Article
Full-text available
Wildfire occurrences is creating serious challenges for fire and emergency response services and a diverse range of communities around the world due to the increment of the occurrence of these disasters. As such, understanding the physical and social dynamics characterizing wildfires events is paramount to reduce the risk of these natural disasters. As such, one of the main challenges is to understand how households perceive wildfires and respond to them as part of the evacuation process. In this work, the Wildfire Decision Model originally proposed in [1] is calibrated using a hybrid choice model formulation. The Wildfire Decision Model is a newly developed behavioural choice model for large-scale wildfire evacuations based on the estimation of the risk perceived by households and the impact that this has on the decision-making process. This model is calibrated using a hybrid choice modelling solution and survey data collected after the 2016 Chimney Tops 2 wildfire in Tennessee, USA. The proposed model shows good agreement with the preliminary findings available in the wildfire evacuation literature; namely, the perceived risk is affected by both external factors (i.e., warnings and fire cues) and internal factors (i.e., education, previous wildfire evacuation experience and time of residency in a property).
... Overall, the results of the simulated scenarios demonstrate the capability of the KFDSP system to produce real-time predictions of smoke transport in terms of CO, PM10, and PM2.5 concentrations given fire activity and weather. This indicates that the KFDSP system could be used as a screening model for a quick evacuation from the hazardous smoke of forest fires [44,45]. Particularly, it also implies that the capability of the real-time forecasting mode of the KFDSP system can effectively improve the accuracy of smoke plume dispersion predictions since a Gaussian plume model can only deal with a static dispersion, such as VSMOKE [46] and the Simple Approach Smoke Estimation Model (SASEM) [47]. ...
... Overall, the results of the simulated scenarios demonstrate the capability of the KFDSP system to produce real-time predictions of smoke transport in terms of CO, PM 10 , and PM 2.5 concentrations given fire activity and weather. This indicates that the KFDSP system could be used as a screening model for a quick evacuation from the hazardous smoke of forest fires [44,45]. Particularly, it also implies that the capability of the real-time forecasting mode of the KFDSP system can effectively improve the accuracy of smoke plume dispersion predictions since a Gaussian plume model can only deal with a static dispersion, such as VSMOKE [46] and the Simple Approach Smoke Estimation Model (SASEM) [47]. ...
Article
Full-text available
Smoke from forest fires is a growing concern in Korea as forest structures have changed and become more vulnerable to fires associated with climate change. In this study, we developed a Korean forest fire smoke dispersion prediction (KFSDP) system to support smoke management in Korea. The KFSDP system integrates modules from different models, including a Korean forest fire growth prediction model, grid-based geographic information system (GIS) fuel loading and consumption maps generated by national forest fuel inventory data, and the Korean Weather Research and Forecasting Model, into a Gaussian plume model to simulate local- and regional-scale smoke dispersion. The forecast system is operated using grid-based fires and simulates a cumulative smoke dispersion of carbon monoxide (CO) and <2.5 µm and <10 µm particulate matter (PM2.5 and PM10, respectively) ground-level concentration contours at 30-min intervals during the fire in concert with weather forecasts. The simulated smoke dispersions were evaluated and agreed well with observed smoke spreads obtained from real forest fires in Korea, and the performance of the KFSDP system was also analyzed using “what-if” scenarios. This is the first study to develop an integrated model for predicting smoke dispersion from forest fires in Korea.
... Researchers have also used traffic modeling to optimize the evacuation order progression and increase evacuation route efficiency (Wolshon & Marchive III, 2007;Intini et al., 2019). Wolshon & Marchive III (2007) confirmed that the more dense a residential area is, the longer it takes to evacuate residents to a safe location. ...
... Wolshon & Marchive III (2007) confirmed that the more dense a residential area is, the longer it takes to evacuate residents to a safe location. Intini et al. (2019) explored different approaches to modelling WUI fire evacuation events. They compiled modelling suggestions and highlighted the need for a dynamic modeling framework that integrates WUI evacuation scenarios with other existing models. ...
Preprint
Full-text available
The threat of wildfires is increasing at an alarming rate due to climate change and the expansion of the wildland-urban interface. It's more important now than ever for emergency officials to increase the efficiency and effectiveness of wildfire evacuations. One important area of research is to better understand people's evacuation decisions during wildfire emergencies. To this end, this study develops a new census-block-group-level evacuation rate model built from large-scale GPS data generated by mobile devices, which complements traditional research approaches such as surveys, focus groups, and traffic modeling. Specifically , we developed a linear regression model to examine how socio-demographic and built environment variables affect evacuation rates across census block groups. We used GPS data collected during the 2019 Kincade Fire in California. The results are generally consistent with findings of a prior survey study of the same fire event. Additionally, several built environment factors such as distance to the fire, land parcel size, and whether the person lives in a high fire risk area were found to be correlated with evacuation rates. This research shows that the use of GPS data is a valuable complement to existing research methods in wildfire evacuation research.
... These scenarios require the simulation of both human response as well as pedestrian and traffic movement [18]. A review [19] identified 22 traffic models which could potentially be used for such scenarios and it highlighted that most of these models have not been specifically designed for FSE applications. Therefore, their assumptions and integration with fire models need to be further developed [20]. ...
... For instance, most pedestrian movement models have been developed to investigate crowd dynamics. Similarly, many traffic models have been designed for optimization of traffic flows rather than assessing evacuation scenarios [19]. For this reason, an important step for the evacuation modelling community working in the FSE domain is to evaluate if the assumptions behind these models can be reasonably applicable to FSE. ...
Article
Full-text available
Evacuation models can adopt different approaches for the simulation of human behaviour in fire. This paper provides an overview of the most commonly used modelling methods to represent the evacuation process in a fire scenario. This is presented through a structure matching the engineering time-line model of evacuation. The evacuation model development process is discussed considering both data-driven empirical correlations as well as theory-based modelling approaches. Examples of alternative methods to the currently used evacuation modelling assumptions are also presented. These methods have been chosen to provide examples of cases in which revisions of well-established assumptions may be needed. This review mainly focuses on buildings and pedestrian evacuation scenarios. Nevertheless, many concepts presented are potentially applicable to traffic evacuation. Particular attention is given to the representation of the impact of smoke on the evacuation process, as this is an important issue for fire safety engineering. Finally, a discussion on existing methods and procedures for the verification and validation of evacuation models is presented and the need for their standardization is advocated.
... In recent years, urbanisation of forested regions has greatly increased the number of evacuations of large populations from wildfires associated with the Wildland Urban Interface (WUI) and with global warming, these incidents are expected to increase further [1]. Wildfire evacuations often involve people not just evacuating by vehicle, but also on foot. ...
... Three types of computer models that can aid crises managers to make difficult time sensitive evacuation decisions are pedestrian evacuation models [5], traffic models [1] and wildfire spread models [6] [7]. Pedestrian evacuation models can be used to represent the behaviour and movement of pedestrians for planning of evacuation routes and refuge locations. ...
Conference Paper
In recent years, urbanisation of forested regions has greatly increased the number of evacuations of large populations from wildfires associated with the Wildland Urban Interface (WUI) and with global warming, these incidents are expected to increase further [1]. Wildfire evacuations often involve people not just evacuating by vehicle, but also on foot. People may be required to walk to designated shelters when they are nearby, or to public transport hubs or to their cars [2]. During the 2018 Camp Fire in California [3], traffic jams forced people to abandon their cars and evacuate by foot. In the 2018 Mati fires near Athens Greece, in which 102 people lost their lives, 26 of the decedents were attempting to evacuate to the beach on foot when they were entrapped and overcome by the rapidly advancing fire [4]. Even if communities are well trained, not all contingencies can be anticipated and catered for. The variability of wildfire may require crisis managers to take unplanned actions [3]. The use of computer models can assist crisis managers to not only plan and train in managing large-scale evacuations but they can also be used to aid in real-time incident management [5].
... This poses a set of challenges from the evacuation perspective, among which the possibility for people to be located in an area where smoke is present. Smoke emission can have a direct impact on human behaviour during a wildfire scenario by potentially triggering the evacuation process, affect the choice of evacuation route, and affect the choice of speed while driving away from the danger [15,23]. ...
... No clear trend was found on the impact of the visibility conditions in the previously driven road segment. Traffic evacuation models currently neglect the impact of smoke and they do not explicitly consider the impact of reduced visibility conditions on speed [15]. They can subsequently produce non-conservative results when applied to wildfire evacuation scenarios. ...
Article
Full-text available
This work presents the results of a virtual reality (VR) experiment aiming at investigating how individual driving behaviour is affected by the presence of wildfire smoke. The experiment included a driving simulation task to study the chosen driving speed at different smoke densities and the lateral position of the driven car on the road cross section. During the VR experiment, participants were presented with a simulated wildfire evacuation scenario including the presence of smoke through a head mounted display and were given a task to evacuate via car using a steering wheel and pedals on a single carriageway road with two lanes. A total of 46 participants took part in the experiments and their driven trajectories along with their instantaneous speed were collected in 5 different visibility conditions. Driving speed decreased with increasing smoke density. No difference in choice of speed was found in relation to the smoke density in the previously driven road segment (in thicker or thinner smoke). No difference in lateral position (closer to or further from the centreline of the road) at different smoke densities was found. Suggested correlations between driving speed and wildfire smoke density are provided in this paper, referring to either a fractional reduction of the speed in smoke-free conditions or an absolute choice of speed at a given visibility condition. These correlations are useful to provide more accurate estimation of evacuation times with traffic evacuation modelling tools in case of wildfire and wildland-urban interface fire scenarios.
... One way to assess the impact of wildfire on a community and allow officials and planners to be informed of ways to mitigate negative consequences is via simulation models Intini et al., 2019). These tools are increasingly used to inform the development of evacuation plans for WUI communities. ...
Article
Full-text available
Wildfires are a significant safety risk to populations adjacent to wildland areas, known as the wildland-urban interface (WUI). This paper introduces a modelling platform called WUI-NITY. The platform is built on the Unity3D game engine and simulates and visualises human behaviour and wildfire spread during an evacuation of WUI communities. The purpose of this platform is to enhance the situational awareness of responders and residents during evacuation scenarios by providing information on the dynamic evolution of the emergency. WUI-NITY represents current and predicted conditions by coupling the three key modelling layers of wildfire evacuation, namely the fire, pedestrian, and traffic movement. This allows predictions of evacuation behaviour over time. The current version of WUI-NITY demonstrates the feasibility and advantages of coupling the modelling layers. Its wildfire modelling layer is based on FARSITE, the pedestrian layer implements a dedicated pedestrian response and movement model, and the traffic layer includes a traffic evacuation model based on the Lighthill-Whitham-Richards model. The platform also includes a sub-model called PERIL that designs the spatial location of trigger buffers. The main contribution of this work is in the development of a modular and model-agnostic (i.e., not linked to a specific model) platform with consistent levels of granularity (allowing a comparable modelling resolution in the representation of each layer) in all three modelling layers. WUI-NITY is a powerful tool to protect against wildfires; it can enable education and training of communities, forensic studies of past evacuations and dynamic vulnerability assessment of ongoing emergencies.
... Given the larger literature available on traffic modelling, detailed information concerning the review of traffic models can be found in a dedicated publication on this issue (Intini et al., 2019). ...
Article
Fire evacuations at wildland-urban interfaces (WUI) pose a serious challenge to the emergency services, and are a global issue affecting thousands of communities around the world. This paper presents a multi-physics framework for the simulation of evacuation in WUI wildfire incidents, including three main modelling layers: wildfire, pedestrians, and traffic. Currently, these layers have been mostly modelled in isolation and there is no comprehensive model which accounts for their integration. The key features needed for system integration are identified, namely: consistent level of refinement of each layer (i.e. spatial and temporal scales) and their application (e.g. evacuation planning or emergency response), and complete data exchange. Timelines of WUI fire events are analysed using an approach similar to building fire engineering (available vs. required safe egress times for WUI fires, i.e. WASET/WRSET). The proposed framework allows for a paradigm shift from current wildfire risk assessment and mapping tools towards dynamic fire vulnerability mapping. This is the assessment of spatial and temporal vulnerabilities based on the wildfire threat evolution along with variables related to the infrastructure, population and network characteristics. This framework allows for the integration of the three main modelling layers affecting WUI fire evacuation and aims at improving the safety of WUI communities by minimising the consequences of wildfire evacuations.
... Since it is impractical to do evacuation experiments to study evacuation traffic, we need to rely on computer simulation tools. In the past few decades, traffic simulation has grown in popularity in evacuation modeling and simulation [71][72][73]. Specifically, based on the four-step transportation planning procedure, Southworth [69] formulated regional evacuation planning into a fivestep process: trip generation, trip departure time, trip destination, route selection, and plan setup and analysis. Once the evacuation zones are delineated [74,75], one key step in evacuation trip generation is to determine evacuee population distribution [29,69]. ...
Article
Full-text available
The aim of this paper is to advance understanding of the value of national address point databases in improving wildfire public safety in the U.S. The paper begins with a review of the value of a national address point database in wildfire evacuations. An introduction to address point data in the U.S. is presented by examining two national address point datasets—the National Address Database and the OpenAddresses project. We examine the existing and potential uses of address point data in wildfire evacuation research and practice. Specifically, we cover four primary applications: wildland-urban interface mapping, wildfire evacuation warnings/zoning, wildfire evacuation traffic simulation, and house loss assessment/notification. Based on our review, we find that address point data has the potential to significantly improve these applications and a national address point database can help enhance wildfire public safety in the U.S. Finally, we conclude with a discussion of the challenges of using address point data in wildfire evacuations and future research directions. This review proposes an agenda for further research on the potential use of address point data in wildfire evacuation applications and sheds light on the development and applications of the two national address point database projects.
... The 2016 Fort McMurray fire alone had the costliest impact in the Canadian history in terms of insured losses [43]. Due to these urgent needs, research has started to address the consequences of these incidents [8,30], provide measures to aid evacuation planning [9], and coupling fire, traffic and pedestrian models to aid response to such incidents [25,42,43]. ...
Article
Full-text available
Wildland-Urban Interface (WUI) fires, a worldwide problem, are gaining more importance over time due to climate change and increased urbanization in WUI areas. Some jurisdictions have provided standards, codes and guidelines, which may greatly help planning, prevention and protection against wildfires. This work presents a wide systematic review of standards, codes and guidelines for the design and construction of the built environment against WUI fire hazard from North American, European, Oceanic countries, alongside with trans-national codes. The main information reviewed includes: the definition of WUI hazards, risk areas and related severity classes, the influence of land and environmental factors, the requirements for building materials, constructions, utilities, fire protection measures and road access. Some common threads among the documents reviewed have been highlighted. They include similar attempts at: (a) defining WUI risk areas and severity classes, (b) considering land factors including the defensible space (also known as ignition zones), (c) prescribing requirements for buildings and access. The main gaps highlighted in the existing standards/guidelines include lacks of detailed and widespread requirements for resources, fire protection measures, and lacks of taking into account environmental factors in detail. The main design and construction principles contained in the reviewed documents are largely based on previous research and/or good practices. Hence, the main contributions of this paper consist in: (a) systematically disseminate these guidance concepts, (b) setting a potential basis for the development of standards/guidelines in other jurisdictions lacking dedicated WUI fire design guidance, (c) highlighting gaps in existing standards/guidelines to be addressed by current and future research.
... Whilst accurately calculating tveh remains problematic (Cova et al, 2011;Intini et al, 2019;Ronchi et al, 2017), in an urban design and planning assessment context when a shelter in place strategy is adopted, calculation of tveh is not required as occupants are not leaving the site. To improve the design of wildfire prone communities (including visiting tourists) in regards to large scale evacuation and egress, Cova (2005) ...
Book
Full-text available
Each year firefighters from career and volunteer agencies across Australia respond to wildfires that impact the urban interface. When such an event occurs during a period of intense fire behavior, the conditions are often incompatible with life for persons either caught in the open or those seeking refuge in a vehicle. In order to improve firefighter safety and operational effectiveness during landscape scale wildfires, as well as providing sound engineering guidance to improve community resilience to wildfire impacts, this textbook forms part of the lead author’s PhD and examines critical components of wildfire response. These components are the wildfire fighting strategies and tactics applied during a landscape scale wildfire event; the procedures and protective systems utilised in the event of burnover; operational risk management; and wildfire resilient urban design. A Handbook of Wildfire Engineering (the Handbook) provides firefighters, engineers and town planners with detailed technical approaches and analysis to enhance the resilience of communities in areas prone to wildfire impacts, and enhance the safety and effectiveness of wildfire suppression at the urban interface during catastrophic wildfire conditions. Each chapter of the Handbook is designed to build upon the previous, providing a holistic approach to understanding vegetation and wildfire basics before exploring evidence based wildfire suppression. The critical linkage between wildfire suppression, firefighter safety and urban design is also explored. Whilst the primary focus of this Handbook is wildfire suppression, there are many aspects applicable to urban designers and policy makers. These are summarised at the conclusion of each chapter. During the preparation of this book, Australia was suffering from catastrophic wildfires on both the west and east coasts and, tragically, civilians and firefighters alike were injured or killed. The lead author was deployed as a Strike Team Leader from Western Australia and was tasked with wildfire suppression and property defense near Walcha, New South Wales. In addition to his own local experiences in Margaret River in 2011 and Yarloop 2016, during the 2019 NSW deployment he witnessed first-hand the devastating effects of wildfire on firefighters and the communities, survived near miss entrapments and nights spent on the fireground cut off by fire behaviour and falling trees. This book is dedicated to all those affected by wildfires, particularly for the firefighters of all backgrounds and jurisdictions who put themselves in harm’s way to protect life, property and the environment. May the guidance provided in this book help firefighters return safely to their loved ones and provide enhanced protection of communities in wildfire prone areas.
... Without adequate funding, staff, and research ability, governments need practice-ready strategies to successfully evacuate residents in wildfires. One positive direction in the field has been the development of wildfire evacuation models, including traffic simulation models (Intini et al., 2019) that have sometimes been coupled with fire spread models and trigger buffer models (e.g., Li et al., 2015). Despite these new integrated models, two key limitations remain in the wildfire evacuation simulation field. ...
Preprint
Full-text available
Government agencies must make rapid and informed decisions in wildfires to safely evacuate people. However, current evacuation simulation tools for resource-strapped agencies largely fail to compare possible transportation responses or incorporate empirical evidence from past wildfires. Consequently, we employ online survey data from evacuees of the 2017 Northern California Wildfires (n=37), the 2017 Southern California Wildfires (n=175), and the 2018 Carr Wildfire (n=254) to inform a policy-oriented traffic evacuation simulation model. We test our simulation for a hypothetical wildfire evacuation in the wildland urban interface (WUI) of Berkeley, California. We focus on variables including fire speed, departure time distribution, towing of items, transportation mode, GPS-enabled rerouting, phased evacuations (i.e., allowing higher-risk residents to leave earlier), and contraflow (i.e., switching all lanes away from danger). We found that reducing household vehicles (i.e., to 1 vehicle per household) and increasing GPS enabled rerouting (e.g., 50% participation) lowered exposed vehicles (i.e., total vehicles in the fire frontier) by over 50% and evacuation time estimates (ETEs) by about 30% from baseline. Phased evacuations with a suitable time interval reduced exposed vehicles most significantly (over 90%) but produced a slightly longer ETE. Both contraflow (on limited links due to resource constraints) and slowing fire speed were effective in lowering exposed vehicles (around 50%), but not ETEs. Extended contraflow can reduce both exposed vehicles and ETEs. We recommend agencies develop a communication and parking plan to reduce evacuating vehicles, create and communicate a phased evacuation plan, and build partnerships with GPS-routing services.
... In addition, simulations, both microscopic and mesoscopic, have been growing in the literature as a feasible mechanism to describe and predict traffic flows during wildfire evacuations (for framing, see Ronchi et al., 2017). A full review of traffic simulation models can be found in Intini et al. (2019), which also describes the need for improved modeling inputs through revealed preference behavior. Simulation research has also helped determine the effectiveness of different evacuation and transportation response strategies (Cova and Johnson, 2003;Chen and Zhan, 2008). ...
... These types of models are frequently leveraged to investigate the changes in evacuation performance metrics in parametric studies by focusing on detailed evacuation choices, such as departure time, route, and destination. Determine evacuation trigger buffers (ETBs), recommended evacuation departure times (REDTs), and a ranking of households based on lead time (42) Review of evacuation models Understand and review the scale, applicability, and interactions of fire, pedestrian, and traffic models (2) Review of traffic models for wildfire evacuations Understand and review the traffic models based on relation to fire spread, spatial and demographic factors, temporal issues, and intended application and identification of 22 traffic models and applications (43) Controlled behavior experiment and regression models Determine the collective evacuation decision of communities under different disaster likelihoods and shelter availabilities ...
Article
Full-text available
Government agencies must make rapid and informed decisions in wildfires to evacuate people safely. However, current evacuation simulation tools for resource-strapped agencies largely fail to compare possible transportation responses or incorporate empirical evidence from past wildfires. Consequently, this study employs online survey data from evacuees of the 2017 Northern California Wildfires ( n = 37), the 2017 Southern California Wildfires ( n = 175), and the 2018 Carr Wildfire ( n = 254) to inform a policy-oriented traffic evacuation simulation model. The simulation is tested for a hypothetical wildfire evacuation in the wildland-urban interface of Berkeley, California. The study focuses on variables including fire speed, departure time distribution, towing of items, transportation mode, GPS-enabled rerouting, phased evacuations (i.e., allowing higher-risk residents to leave earlier), and contraflow (i.e., switching all lanes away from danger). It was found that reducing evacuating household vehicles (i.e., to one vehicle per household) and increasing GPS-enabled rerouting (e.g., 50% participation) lowered exposed vehicles (i.e., total vehicles in the fire frontier) by over 50% and evacuation time estimates (ETEs) by about 30% from baseline. Phased evacuations with a suitable time interval reduced exposed vehicles most significantly (over 90%) but produced slightly longer ETEs. Both contraflow (on limited links because of resource constraints) and slowing fire speed were effective in lowering number of exposed vehicles (around 50%), but not ETEs. Extended contraflow can reduce both exposed vehicles and ETEs. It is recommended that agencies develop a communication and parking plan to reduce the number of evacuating vehicles, create and communicate a phased evacuation plan, and build partnerships with GPS-routing services.
... Those with reliance on electrified transportation (such as subways, light rail, electric buses) may experience significant service disruptions when getting to work or finding safety. The goal of this paper is to begin addressing these gaps, leading the transportation field to establish a more consistent and accurate understanding of travel behavior in these PSPS events, which can be eventually: 1) tied to transportation modeling approaches (e.g., 29,30), 2) integrated with related behavioral research for evacuations that are caused wildfires (e.g., [31][32][33][34], and 3) developed into its own comprehensive field (e.g., 35). ...
Article
Full-text available
Recent wildfire risks in California have prompted the implementation of public safety power shutoff (PSPS) events, procedures enacted by utility operators to deenergize parts of the electrical grid and reduce the likelihood of wildfire ignition. Despite their yearly occurrence, PSPS events are severely understudied, and little is known about how these events affect disaster preparation activity, travel behavior, and transportation systems. With growing wildfire risks in North America and beyond, PSPS events require immediate and thorough research to reduce their negative externalities and maximize their benefits. This exploratory study employs survey data from East Bay Hills residents in Alameda and Contra Costa counties in California who were affected by two PSPS events in October 2019 ( n = 210). Through descriptive statistics and basic discrete choice models for the decision to conduct typical or changed travel, this research contributes to the literature as the first assessment of PSPS event travel behavior. We found that travel did not change drastically during the event, although respondents conducted a high number of preparedness activities. A sizable portion of the sample conducted extended trips during the PSPS event days, whereas a small number evacuated to a destination overnight. Respondents received relatively clear information from multiple communication methods, indicating substantial information about the events. Modeling results found that power loss was a driver in travel behavior change, whereas demographics indicated heterogeneous responses within the sample. The paper concludes with a discussion of key takeaways and suggestions for research in this nascent field.
... Additional research in wildfire evacuations has extended to simulation and traffic modeling (Cova and Johnson, 2002;Wolshon and Marchive III, 2007;Chen and Zhan, 2008;Beloglazov et al., 2016;Ronchi et al., 2017;Intini et al., 2019;Gwynne et al., 2019;Ronchi et al., 2019), transportation response strategies (Cova and Johnson, 2003;Shahabi and Wilson, 2018) including trigger models for evacuations (Dennison et al., 2007;Larsen et al., 2011;Li et al., 2015;Li et al., 2019), and framing of decision-making (Nguyen et al., 2018;Folk et al., 2019;Lovreglio et al., 2019). ...
Technical Report
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Between 2017 and 2019, California experienced a series of devastating wildfires that together led over one million people to be ordered to evacuate. Due to the speed of many of these wildfires, residents across California found themselves in challenging evacuation situations, often at night and with little time to escape. These evacuations placed considerable stress on public resources and infrastructure for both transportation and sheltering. In the face of these clear challenges, transportation and emergency management agencies across California have widely varying levels of preparedness for major disasters, and nearly all agencies do not have the public resources to adequately and swiftly evacuate all populations in danger. To holistically address these challenges and bolster current disaster and evacuation planning, preparedness, and response in California, we summarize the evacuations of eleven major wildfires in California between 2017 and 2019 and offer a cross-comparison to highlight key similarities and differences. We present results of new empirical data we collected via an online survey of individuals impacted by: 1) the 2017 October Northern California Wildfires (n=79), 2) the 2017 December Southern California Wildfires (n=226), and 3) the 2018 Carr Wildfire (n=284). These data reveal the decision-making of individuals in these wildfires including choices related to evacuating or staying, departure timing, route, sheltering, destination, transportation mode, and reentry timing. We also present results related to communication and messaging, non-evacuee behavior, and opinion of government response. Using the summarized case studies and empirical evidence, we present a series of recommendations for agencies to prepare for, respond to, and recover from wildfires.
... In addition, simulations, both microscopic and mesoscopic, have been growing in the literature as a feasible mechanism to describe and predict traffic flows during wildfire evacuations (for framing, see Ronchi et al. 2017). A full review of traffic simulation models can be found in Intini et al. (2019), which also describes the need for improved modeling inputs through revealed preference behavior. Simulation research has also helped determine the effectiveness of different evacuation and transportation response strategies (Cova and Johnson 2003;Chen and Zhan 2008). ...
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For evacuations, people must make the critical decision to evacuate or stay followed by a multi-dimensional choice composed of concurrent decisions of their departure time, transportation mode, route, destination, and shelter type. These choices have important impacts on transportation response and evacuation outcomes. While extensive research has been conducted on hurricane evacuation behavior, little is known about wildfire evacuation behavior. To address this critical research gap, particularly related to joint choice-making in wildfires, we surveyed individuals impacted by the 2017 December Southern California Wildfires (n = 226) and the 2018 Carr Wildfire (n = 284). Using these data, we contribute to the literature in two key ways. First, we develop two latent class choice models (LCCMs) to evaluate the factors that influence the decision to evacuate or stay/defend. We find an evacuation keen class and an evacuation reluctant class that are influenced differently by mandatory evacuation orders. This nuance is further supported by different membership of people to the classes based on demographics and risk perceptions. Second, we develop two portfolio choice models (PCMs), which jointly model choice dimensions to assess multi-dimensional evacuation choice. We find several similarities between wildfires including a joint preference for within-county and nighttime evacuations and a joint dislike for within-county and highway evacuations. Altogether, this paper provides evidence of heterogeneity in response to mandatory evacuation orders for wildfires, distinct membership of populations to different classes of people for evacuating or staying/defending, and clear correlation among key wildfire evacuation choices that necessitates joint modeling to holistically understanding wildfire evacuation behavior.
... Whereas, for example, in Case A, evacuating occupants may be exposed to untenable conditions within corridors and stairwells, for Case B evacuating occupants are exposed to smoke, moving fire fronts, and embers/burning brands on a larger, external scale as they evacuate in their motor vehicle on the local roading network. Whilst the complexities of such evacuations are well documented [39][40][41][42][43][44][45][46], we previously [20] reported only a single study [47] providing guidance for the design of public road networks and access/egress points to facilitate mass evacuation as required for Case B. ...
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The hazard posed by wildland–urban-interface (WUI) fires is recognized by the international fire research community and features as one of nine research need priority threads in the Society of Fire Protection Engineers (SFPE) Research Roadmap. We posit that the first step in the journey to enhancing fire safety engineering at the WUI is to develop a common understanding between developers, engineers, planners, and regulators of the development scope, wildfire problem, technical design solutions, and verification methods to be used. In order to define a fire safety engineering consultation process appropriate for the wildfire context, this paper aims to translate well-established and evidence-based performance-based design (PBD) consultation frameworks and approaches from traditional fire safety engineering to the wildfire context. First, we review international English-language fire safety engineering frameworks that have been developed for the urban context. Next, we distil the results into a streamlined framework, which we call the “CAED Framework”. Finally, we apply and discuss the contextualization of the CAED Framework to the WUI context through a comparative case study of urban and WUI development. In doing so we seek to provide a structure for the development of standardized PBD within the WUI context across jurisdictions internationally, as well as to embed best practices into the emerging field of performance-based wildfire engineering.
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Traffic models can be used to study evacuation scenarios during wildland-urban interface fires and identify the ability of a community to reach a safe place. In those scenarios, wildfire smoke can reduce visibility conditions on the road. This can have serious implications on the evacuation effectiveness since drivers would reduce their speed in relation to the optical density on the road. To date, there is no traffic model which explicitly represents the impact of reduced visibility conditions on traffic evacuation flow. This paper makes use of an experimental dataset collected in a virtual reality environment to calibrate two widely used macroscopic traffic models (the Lighthill-Whitham-Richards and the Van Aerde models) in order to account for the impact of reduced visibility conditions on driving speed. An application of the calibrated traffic model considering the impact of smoke has been performed using the WUI-NITY platform, an open multi-physics platform which includes wildfire spread, pedestrian response and traffic modelling. A dedicated verification test has been developed and performed considering different values of optical densities of smoke and traffic densities to ensure the model has been implemented correctly in WUI-NITY. A case study that demonstrates the applicability of the model to real life scenarios was also implemented, based on data from an evacuation drill. This paper shows that the presence of smoke on the road can significantly decrease movement speed and increase evacuation times thus highlighting the need for inclusion of this factor in traffic evacuation models applied for wildland-urban interface fire scenarios.
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No-notice wildfires pose a serious threat to the safety of residents, where the notification time and departure time may all be within a matter of minutes. Here, we examine the factors associated with the initial wildfire awareness time, the evacuation preparation time, and finally the departure time. We use unique interview and survey data gathered in Red Cross shelters just weeks after evacuation for the 2018 Camp Fire in Northern California. We specify models for awareness time, departure time, and preparation time and find that quicker awareness is associated with seeing the fire first-hand, familiarity with local evacuation protocol, smartphone ownership, among others. We find that higher incomes are associated with quicker awareness, but had no effect on departure or preparation times. Longtime residents had longer preparation and departure times. Taken together, our results suggest new pathways for better understanding how to plan and prepare for no-notice disaster events.
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This paper applies the min-cut max-flow theorem combined with a grid cell disruption method over a large transportation network, to identify the importance of network locations in providing travel capacity when a community is evacuating (say, from wildfire). We develop metrices that look at the importance and contribution of individual links to network bottleneck capacity in traveling from the evacuating community to the shelter community. The purpose of this is to determine the network location that is the most restrictive of all, and more importantly, where it is in reference to the evacuating community location. We apply this method to the highway network of Alberta, Canada, with evacuating communities identified as those that have been under wildfire threat historically and/or are expected to in the future. We find that in all cases, network locations that contribute the greatest share of bottleneck capacity, are located adjacent to these evacuating communities. Next, we look at combining the measure for multiple fire-prone communities, finding that the highways in remote northern Alberta are important despite some of them having lower capacity than the multi-lane highways in the south. Application of this simple method can support provincial and local municipal governments in deciding which communities require more detailed emergency evacuation studies to better identify and communicate transportation network deficiencies to provincial and federal bodies that would be making infrastructure investments towards community health and resilience.
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Wildfires have caused increasingly negative impacts with increasing occurrences close to densely populated regions. Evacuations are among the most critical measures in the immediate wildfire relief measures. While social media have been used in natural disasters, there has been limited understanding of the efficacy of using social media to aid evacuations. This paper presents a data-driven study of social media-aided evacuations for the 2020 wildfires in the western United States, based on 53,990 relevant tweets. First, we analyzed the aggregated social media data and validated its reliability against information from official channels. Both the temporal and spatial investigations show good agreements with official information. Further, we classified the tweets into pre- and on-evacuation based on extracted word patterns. The classifications align well with evacuation levels from official channels. Next, we demystified the information dissemination patterns via network analysis. We have found that government channels, news agencies, and public figures prevail among top users. The top users for on-evacuations tend to be more local-focused than pre-evacuations. This study demonstrates the efficacy of using social media to aid evacuations. In addition, it provides guidelines for future studies on extracting high-priority information from social media for disaster relief.
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Wildfire is a growing global concern for rural and urban areas (Boustras et al., 2017). Statistics show that the intensity and negative consequences of wildfire have increased in recent decades creating serious challenges for fire and emergency services, as well as communities in the wildland-urban interface (Liu et al., 2010; McCaffrey et al., 2018; Ronchi et al., 2019). As an example, 85 people lost their lives in California's Camp fire, which made 2018 the deadliest US wildfire year in a century. To reduce the life safety risk of wildfire and to enhance the safety of communities threatened by wildfire, it is fundamental to understand the physical and social dynamics characterizing wildfires (Lovreglio et al., 2019). Such an understanding will help to improve the design of a community's built environment (e.g. buildings and transportation infrastructure) and enhance emergency planning via the incorporation of actual household evacuation behaviour to eventually enable safe and effective evacuations during wildfire emergencies. To address this challenge, several wildfire evacuation models have been proposed in the literature and a comprehensive review of the modelling approaches is provided in Ronchi et al. (2019). Existing literature on wildfire evacuation modelling can be divided into two categories: conceptual models and engineering models. Conceptual models (Lindell and Perry, 2004; Cova et al., 2009; Ronchi et al., 2019; Whittaker et al., 2017; McLennan et al., 2019; Lovreglio et al., 2019) provide conceptual frameworks explaining the behavioural components and steps humans go through when assessing, deciding about, and responding to wildfire emergencies. Engineering models (Intini et al., 2019; Ronchi and Gwynne, 2019; Lovreglio et al., 2019) include choice models and traffic models. Choice models are designed to investigate the factors affecting human behaviour and model the decision-making process. In a wildfire evacuation, they can be used to estimate how and/or when humans will respond to a wildfire and the time required to evacuate an area threatened by a wildfire, for example. Traffic models, on the other hand, are tools that allow for the simulation of microscopic or macroscopic traffic conditions during the wildfire emergencies. Thus, in the example of macroscopic simulations, traffic models require output from choice models to define trip generations, trip distributions, mode choices, and route assignments as an input of the traffic simulator.
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Wildfires are becoming more frequent and increasing in intensity, which results in significant threats to human life and property. Road networks play an important role in emergency activities. It is reasonable that robust road connectivity will give evacuees and emergency services the ability to respond more effectively, which may lead to a reduction in casualties. This study explores a novel graph-based connectivity index for road networks that considers different analysis scales to measure the impact on global wildfire fatality events in past decades. We find a significant and systematic relationship between fatalities and a calibrated connectivity index across different wildfire events. This parsimonious and simple graph theoretic measure can provide planners a useful metric to reduce vulnerability and increase resilience among areas that are prone to wildfires.
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In this study, we explore how short- and no-notice evacuation instructions may be designed by a risk-averse emergency management body, explicitly considering varying levels of evacuee compliance and responses to these instructions in the design. The population compliance rate, or its distribution, are unlikely to be known; even a small error in estimating either could lead to drastically worse outcomes. Our solution, which is based on the idea of hedging, determines optimal evacuation instructions in a range of possible scenarios. As part of the proposed solution algorithm, we utilize conditional-value-at-risk (CVaR) and stochastic optimization paradigms to demonstrate how and when compliance uncertainty is a critical input for improved evacuation instructions. Our findings show that in situations where compliance rates are low or highly uncertain, relying solely on highly risk-averse strategies is unlikely to deliver efficient outcomes in comparison to a naïve or no-information approach (where instructions are designed assuming 100% compliance). Although accounting for compliance uncertainty in designing evacuation instructions can be highly beneficial, the added benefits are realized beyond certain population compliance rate levels and uncertainty ranges. This work provides insights into how emergency managers can best overcome non-compliance to instruction, the benefits which are realized together with efforts to promote compliance (community education initiatives and improved communication mechanisms).
Technical Report
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The present work describes a novel framework for modelling wildfire urban evacuations. The framework is based on multi-physics simulations that can quantify the evacuation performance. The work argues that an integrated approached requires considering and integrating all three important components of WUI evacuation, namely: fire spread, pedestrian movement, and traffic movement. The report includes a systematic review of each model component, and the key features needed for the integration into a comprehensive toolkit.
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Household behavior and dynamic traffic flows are the two most important aspects of hurricane evacuations. However, current evacuation models largely overlook the complexity of household behavior leading to oversimplified traffic assignments and, as a result, inaccurate evacuation clearance times in the network. In this paper, we present a high fidelity multi-agent simulation model called A-RESCUE (Agent-based Regional Evacuation Simulator Coupled with User Enriched behavior) that integrates the rich activity behavior of the evacuating households with the network level assignment to predict and evaluate evacuation clearance times. The simulator can generate evacuation demand on the fly, truly capturing the dynamic nature of a hurricane evacuation. The simulator consists of two major components: household decision-making module and traffic flow module. In the simulation, each household is an agent making various evacuation related decisions based on advanced behavioral models. From household decisions, a number of vehicles are generated and entered in the evacuation transportation network at different time intervals. An adaptive routing strategy that can achieve efficient network-wide traffic measurements is proposed. Computational results are presented based on simulations over the Miami-Dade network with detailed representation of the road network geometry. The simulation results demonstrate the evolution of traffic congestion as a function of the household decision-making, the variance of the congestion across different areas relative to the storm path and the most congested O-D pairs in the network. The simulation tool can be used as a planning tool to make decisions related to how traffic information should be communicated and in the design of traffic management policies such as contra-flow strategies during evacuations.
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Many possible emergency conditions, including evacuations, negatively affect the urban transportation system by substantially increasing the travel demand and/or reducing the supplied capacity. A transportation model can be used to quantify and understand the impact of the underlying disasters and corresponding management strategies. To this end, we develop an efficient methodology suitable for simulating multimodal transportation systems affected by emergencies, based on the novel integration of an activity-based choice model with both pre-trip and en-route choices, and a macroscopic or mesoscopic dynamic network loading model. The model structure first estimates the daily equilibrium and then uses that result as a starting point to simulate the emergency situation without further iterations. Unlike previous efforts, our methodology satisfies all requirements identified from literature regarding transportation modeling for emergencies, and is sufficiently general to investigate a wide range of emergency situations and management strategies. An evacuation case study for Delft shows the feasibility of applying the methodology. Furthermore, it yields practical insights for urban evacuation planning that stem from complex system dynamics, such as important interactions among travel directions and among modes. This supports the need for a comprehensive modeling methodology such as the one we present in this paper.
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While agent-based modelling of traffic demand is gaining attention, a macroscopic dynamic network loading model may be beneficial, particularly in large-scale applications. We investigate the implications of coupling such models, with inclusion of en-route choices, for the modelling of links and the determination of turning fractions, yielding useful recommendations to help select an appropriate solution scheme of the macroscopic traffic flow theory and overcome other practical challenges specifically associated with the coupling of agent-based traffic demand and macroscopic traffic propagation.
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This paper presents a multimodal evacuation simulation for a near-field tsunami through an agent-based modeling framework in Netlogo. The goals of this paper are to investigate (1) how the varying decisn time impacts the mortality rate, (2) how the choice of different modes of transportation (i.e., walking and automobile), and (3) how existence of vertical evacuation gates impacts the estimation of casualties. Using the city of Seaside, Oregon as a case study site, different individual decision-making time scales are included in the model to assess the mortality rate due to immediate evacuation right after initial earthquake or after a specified milling time. The results show that (1) the decision-making time (τ) and the variations in decision time (σ) are strongly correlated with the mortality rate; (2) the provision of vertical evacuation structures is effective to reduce the mortality rate; (3) the mortality rate is sensitive to the variations in walking speed of the evacuee population; and (4) the higher percentage of automobile use in tsunami evacuation, the higher the mortality rate. Following the results, this paper concludes with a description of the challenges ahead in agent-based tsunami evacuation modeling and simulation, and the modeling of complex interactions between agents (i.e., pedestrian and car interactions) that would arise for a multi-hazard scenario for the Cascadia Subduction Zone.
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The behaviour of building occupants before the purposive movement towards an exit, known as the pre-evacuation behaviour, can have a strong impact on the total time required to leave a building in case of fire emergency as well as on the number of casualties and deaths. The pre-evacuation time can be simulated within computational models using different approaches. This work introduces a new model for the simulation of pre-evacuation behaviour based on the Random Utility Theory. The proposed model represents the pre-evacuation behaviour of simulated occupants considering three behavioural states: normal, investigating and evacuating. The model simulates the probability of choosing to start investigating and evacuating in relation to physical and social environmental factors as well as personal occupant characteristics. These two decisions make occupants pass from their starting normal states to investigating and evacuating states. The paper presents a case study of the proposed pre-evacuation time model using an experimental evacuation data set in a cinema theatre. The application of the model allows identifying the main factors affecting the decision to move from a state to another. In the present case study, the main factors influencing the decisions were the time elapsed since the start of the alarm, the occupant's position, and social influence. The issues associated with the implementation of the model are discussed.
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Climate strongly influences global wildfire activity, and recent wildfire surges may signal fire weather-induced pyrogeographic shifts. Here we use three daily global climate data sets and three fire danger indices to develop a simple annual metric of fire weather season length, and map spatio-temporal trends from 1979 to 2013. We show that fire weather seasons have lengthened across 29.6 million km2 (25.3%) of the Earth’s vegetated surface, resulting in an 18.7% increase in global mean fire weather season length. We also show a doubling (108.1% increase) of global burnable area affected by long fire weather seasons (>1.0 σ above the historical mean) and an increased global frequency of long fire weather seasons across 62.4 million km2 (53.4%) during the second half of the study period. If these fire weather changes are coupled with ignition sources and available fuel, they could markedly impact global ecosystems, societies, economies and climate.
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This paper explains a modeling approach that offers better understanding of the routing strategies taken by evacuees to reach a safe destination during hurricane evacuation. Route choice during evacuation is a complex process because evacuees may prefer to take the usual or familiar route on the way to the destination, or they might follow the routes recommended by the emergency officials. Depending on the condition of the traffic stream, sometimes they might switch to a different route to obtain better travel time from the one initially attempted, i.e.,the routing behavior is random. By using data from Hurricane Ivan, a mixed (random parameters) logit model is estimated which captures the decision making process on what type of route to select while accounting for the existence of unobserved heterogeneity across households. Estimation findings indicate that the choices of evacuation routing strategy involve a complex interaction of variables related to household location, evacuation characteristics, and socioeconomic characteristics. The findings of this study are useful to determine the manner in which different factions of people select a type of route for a given sociodemographic profile during an evacuation.
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Numerous traffic simulation tools are available to assist in the efficient allocation of resources while operating and managing traffic networks. Their model fidelity ranges from detailed (microscopic) to aggregate (mesoscopic) and intermediate (anisotropic mesoscopic), each approach differing in the level of detail of their component demand and supply models. While each tool may have been demonstrated on specific datasets, these data are generally too varied to allow meaningful comparisons of use to practitioners. Objective evaluations of such tools are often lacking, leading to ambiguity about their modeling assumptions, accuracy, feature sufficiency, running time and scalability. In this paper, we apply three popular traffic simulation tools on a common network and demand data. TransModeler (microscopic), DYNASMART-P (mesoscopic) and DynusT (anisotropic mesoscopic) are tested on a real, urban region in Eureka, CA. Their performance over a range of factors are studied, documented and analyzed from both theoretical and empirical perspectives.
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Understanding the local context that shapes collective response to wildfire risk continues to be a challenge for scientists and policymakers. This study utilizes and expands on a conceptual approach for understanding adaptive capacity to wildfire in a comparison of 18 past case studies. The intent is to determine whether comparison of local social context and community characteristics across cases can identify community “archetypes” that approach wildfire planning and mitigation in consistently different ways. Identification of community archetypes serves as a potential strategy for collaborating with diverse populations at risk from wildfire and designing tailored messages related to wildfire risk mitigation. Our analysis uncovered four consistent community archetypes that differ in terms of the local social context and community characteristics that continue to influence response to wildfire risk. Differences among community archetypes include local communication networks, reasons for place attachment or community identity, distrust of government, and actions undertaken to address issues of forest health and esthetics. Results indicate that the methodological approach advanced in this study can be used to draw more consistent lessons across case studies and provide the means to test different communication strategies among archetypes.
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This study examines the factors influencing shadow evacuation. A mock television evacuation order was used to experimentally manipulate the presenter level of authority and message script. The dependent measures were the judged likelihood of evacuation and the judg-ments of message and presenter characteristics. The participants were 186 members of the general public from Lower Hutt, New Zealand. Thirty-three percent of the participants (14% knowingly) outside the evacuation zone reported they were likely to evacuate; therefore, they were classified as shadow evacuees. Nearly three-quarters of the shadow evacuation was the result of participants incorrectly including themselves in the evacuation zone. The remaining quarter reported higher levels of concern about their safety, property, and their ability to travel as a result of flooding and traffic blocking roads, relative to others outside the zone who chose to shelter in place. The presenter's level of authority and the message script did not significantly affect the reported likelihood of evacuation; however, the perceptions of trust, clarity, and message authority increased with higher levels of presenter authority. The participants indicated they would place the greatest trust in evacuation information from the highest role within Civil Defense and Emergency Management followed by local police. Effective evacuation messages should accurately and simply convey which areas are included in the evacuation zone, and provide appropriate information to those who are not at risk to minimize unnecessary travel. Official evacuation messages should be delivered by a person in the highest role appropriate to increase trust in these messages. DOI: 10.1061/(ASCE)NH.1527-6996.0000070. © 2012 American Society of Civil Engineers.
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The principal objective of the present work was to examine the effects of mind state (mind-wandering vs. on-task) on driving performance in a high-fidelity driving simulator. Mind-wandering is thought to interfere with goal-directed thought. It is likely, then, that when driving, mind-wandering might lead to impairments in critical aspects of driving performance. In two experiments, we assess the extent to which mind-wandering interferes with responsiveness to sudden events, mean velocity, and headway distance. Using a car-following procedure in a high-fidelity driving simulator, participants were probed at random times to indicate whether they were on-task at that moment or mind-wandering. The dependent measures were analyzed based on the participant's response to the probe. Compared to when on-task, when mind-wandering participants showed longer response times to sudden events, drove at a higher velocity, and maintained a shorter headway distance. Collectively, these findings indicate that mind-wandering affects a broad range of driving responses and may therefore lead to higher crash risk. The results suggest that situations that are likely associated with mind-wandering (e.g., route familiarity) can impair driving performance.
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A review of literature related to fire evacuation in high-rise buildings was carried out with the following objectives, (1) to identify the key behavioural factors affecting the performance of people during a fire in a high-rise building, the singularities associated to this type of buildings and areas of future research; (2) to review the procedures and strategies currently adopted in high-rise buildings; (3) to review and analyse the capabilities of evacuation models by reviewing their current characteristics and applications in the context of high-rise building evacuations. The review included both findings on human behaviour in high-rise buildings and modelling techniques and tools. Different categories of building use were taken into account, namely office buildings, residential buildings and health care facilities. The individual or combined use of different egress components was analysed. Egress components include the use of stairs, elevators as well as alternative means of escape (e.g., sky-bridges, helicopters, etc.). The effectiveness of the egress components is strongly affected by the building use and the population involved. The review shows that evacuation models can be effectively employed to study relocation strategies and safety issues associated with high-rise buildings. The suitability of egress models for high-rise building evacuations is associated with their flexibility in representing different egress components and complex behavioural processes. The review highlights that there is not a definitive model to be used but that the predictive capabilities of evacuation modelling techniques would be enhanced if more than one model is employed to study different egress aspects. Future research and model developments should focus on the study of the impact of staff actions, group dynamics and people with disabilities. Given the increasing height of buildings and the gradual reduction in the physical abilities of the population, the effects of fatigue on evacuation need further studies.
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Evacuation models generally include the use of distributions or probabilistic variables to simulate the variability of possible human behaviours. A single model setup of the same evacuation scenario may therefore produce a distribution of different occupant-evacuation time curves in the case of the use of a random sampling method. This creates an additional component of uncertainty caused by the impact of the number of simulated runs of the same scenario on evacuation model predictions, here named behavioural uncertainty. To date there is no universally accepted quantitative method to evaluate behavioural uncertainty and the selection of the number of runs is left to a qualitative judgement of the model user. A simple quantitative method using convergence criteria based on functional analysis is presented to address this issue. The method permits (1) the analysis of the variability of model predictions in relation to the number of runs of the same evacuation scenario, i.e. the study of behavioural uncertainty and (2) the identification of the optimal number of runs of the same scenario in relation to pre-defined acceptance criteria.
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Gives a unique up-to-date account of mainstream approaches to transport modelling with emphasis on the implementation of a continuous approach to transport planning. The authors discuss modern transport modelling techniques and their use in making reliable forecasts using various data sources. Importance is placed on practical applications, but theoretical aspects are also discussed and mathematical derivations outlined. -from Publisher
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Evacuations necessitated by extreme events are usually envisioned as taking place with all people evacuating simultaneously; this leads to premature congestion on the surface streets and excessive delays. With the evacuating load onto the network staggered, the onset of congestion may be delayed, and people can evacuate more quickly. In this study, the problem of scheduling evacuation trips between a selected set of origin nodes and (safety) destinations was considered, with the objective of minimizing network clearance time. A modified system-optimal dynamic traffic assignment formulation is proposed; in it the total system evacuation time, as opposed to the total system trip time, is minimized. An iterative heuristic procedure is used to solve this problem: the method of successive averages is used to find the flow assignments for the next iteration; a traffic simulator, DYNASMART-P, is used to propagate the vehicles on their prescribed paths and determine the state of the system. Therefore, the simulator serves as a tool to satisfy the dynamic traffic assignment constraints implicitly while evaluating the objective function. The output of this model will be the departure time, route, and destination choices for each evacuee. The output is then aggregated to produce a time-dependent staging policy for each selected origin.
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Previous research has suggested that drivers’ route familiarity/unfamiliarity (using different definitions of familiarity), and the interactions between familiar and unfamiliar drivers, may affect both the driving performances and the likelihood of road crashes. The purpose of this study is to provide a contribution in the search for relationships between familiarity and crashes by: 1) introducing a measure of familiarity based on the distance from residence; 2) analyzing a traffic and accident dataset referred to rural two-lane sections of the Norwegian highways E6 and E39; 3) using a multi-level approach, based on different perspectives, from a macro analysis to more detailed levels. In the macro analyses, the accident rates computed for different seasons and for different summer traffic variation rates (used as indicators of the share of familiar drivers in the flow) were performed. At the second level, a logistic regression model was used to explain the familiarity/unfamiliarity of drivers (based on their distance from residence), through variables retrieved from the database. In the last step, an in-depth analysis considering also accident types and dynamics was conducted. In the macro analysis, no differences were found between accident rates in the different conditions. Whereas, as emerged from the detailed analyses, the factors: high traffic volume, low summer traffic variation, autumn/winter, minor intersections/driveways, speed limits <80 km/h, travel purposes (commuting/not working) are associated to higher odds of having familiar drivers involved in crashes; while the factors: high traffic volume, high summer traffic variation, summer, head on/rear end-angle crashes, heavy vehicles involved, travel purposes (not commuting), young drivers involved are associated to higher odds of finding unfamiliar drivers involved. To a minor extent, some indications arise from the in-depth analyses about crash types and dynamics, especially for familiar drivers. With regard to the definitions used in this article, the familiarity was confirmed as an influential factor on the accident risk, possibly due to distraction and dangerous behaviors, while the influence of being unfamiliar on the accident proneness has some unclarified aspects. However, crashes to unfamiliar drivers may cluster at sites showing high summer traffic variation and in summer months.
Book
The increasing power of computer technologies, the evolution of software engineering and the advent of Intelligent Transport Systems (ITS) worldwide has helped make traffic simulation one of the most used approaches for traffic analysis in support of the design and evaluation of traffic systems. The ability of traffic simulation software to emulate the time variability of traffic phenomena makes it a uniquely useful tool for capturing the complexity of traffic systems. While a wide variety of simulation software is available, no one book has presented a unified treatment of the subject. Fundamentals of Traffic Simulation is the first book to provide practitioners and researchers with a comprehensive treatment of the state of the art of traffic simulation. Leading researchers worldwide provide up-to-date information on: - Simulation as a well established and grounded OR technique and its specificities when applied to traffic systems. - The main approaches to traffic simulation and the principles of traffic simulation model building. - The fundamentals of traffic flow theory and its application to traffic simulation in Microscopic traffic modeling, Mesoscopic traffic modeling, and Macroscopic traffic modeling. - The principles of Dynamic Traffic Assignment and its application to traffic simulation. - The calibration and validation of traffic simulation models. This important work will appeal to professionals, including transport consultants, managers in design firms and government, and even simulation software developers. It will also provide researchers with the first comprehensive overview of the subject, and can serve as a text or recommended reading in courses on traffic simulation and transportation analysis.
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Evacuation planning and management involves estimating the travel demand in the event that such action is required. This is usually done as a function of people's decision to evacuate, which we show is strongly linked to their risk awareness. We use an empirical data set, which shows tsunami evacuation behavior, to demonstrate that risk recognition is not synonymous with objective risk, but is instead determined by a combination of factors including risk education, information, and sociodemographics, and that it changes dynamically over time. Based on these findings, we formulate an ordered logit model to describe risk recognition combined with a latent class model to describe evacuation choices. Our proposed evacuation choice model along with a risk recognition class can evaluate quantitatively the influence of disaster mitigation measures, risk education, and risk information. The results obtained from the risk recognition model show that risk information has a greater impact in the sense that people recognize their high risk. The results of the evacuation choice model show that people who are unaware of their risk take a longer time to evacuate.
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Coastal areas of the United States are vulnerable to substantial loss of lives and property damage from repeatedly occurring hurricanes and evacuation is the usual recourse to prevent loss of life when high storm surge threatens. The fundamental question in evacuation modeling is to explore the complex evacuation decision-making process leading to an individual's decision to evacuate or not during a hurricane threat. Recent studies suggest that the social network characteristics of individuals could potentially determine overall evacuation patterns. This study explores the joint evacuation decisions of individuals in personal networks by using ego-centric social network data obtained from Hurricane Sandy and by considering the nested structure of the ego-centric network data, i.e. close contacts (alters) as nested within an individual (ego). In this regard, the study develops a multinomial multilevel model of joint evacuation decisions at the dyadic (ego-alter tie) level utilizing a Hierarchical Generalized Linear Modeling (HGLM) approach. Model estimation results suggest factors that define a social tie (contact frequency, discussion topic and geographic proximity) significantly influence the evacuation decisions between individuals and their social partners. In addition, individuals' (both ego and alter) own socio-demographics such as age, marital status, previous evacuation experience, evacuation order, household's type, size, location and proximity to a water body also affect the decision to evacuate. These findings are useful to help emergency managers implement efficient evacuation strategies and to facilitate planning by policymakers by determining fractions of people evacuating or not for a major hurricane within the context of their social networks.
Article
Hurricanes often threaten with catastrophic impacts on the lives of residents in the coastal areas of the United States. Timely evacuation limits this impact, but people may choose to evacuate or not during an extreme weather conditions due to differing personal constrains and environments that have little do with the direct risk. For example, during Hurricane Sandy a significant portion of New York and New Jersey residents facing potentially life-threatening storm-surge risk elected not to evacuate. While previous evacuation studies have investigated the complexities of hurricane behavior and revealed important factors impacting evacuation choice including the influence of social networks and information media, no quantitative analyses of social network effects on evacuation have been done. In some cases, evacuation decisions are solely based on personal obligations and needs, yet they can often be influenced by the people an individual frequently contacts. Previous sociological studies suggest that social networks serve the purpose of transmitting warning message by disseminating information about an impending threat and individuals having more social connections can be expected to receive more warning information. However, the empirical literature is inconclusive about how warnings received from social connections weigh into evacuation decision making. This study uses data obtained by interviewing people from high storm-surge risk areas to understand how they responded to Sandy. Individuals' ego-centric social network information was obtained by using the Personal Network Research Design (PNRD) approach. A mixed (random parameters) logit model of individual-level evacuation decision making is developed to explain the combined effects of individual, household, and social network characteristics along with the reliability of different information sources within a unified modeling framework. This model will enable emergency managers and planners to better predict evacuation demand: the number of individuals evacuating to a safe destination during a major hurricane threat. Researchers exploring different dimensions of evacuation logistics (for example, departure time, destination, modal split, route choice) and simulations may also find this study informative.
Conference Paper
Emergency evacuation plans for metropolitan areas frequently rely on transportation simulation models to predict evacuation durations. Previous work frequently assumes drivers will demonstrate similar aggressiveness during an evacuation as during peak hour commuting. On the contrary, several studies suggest driving behavior is different during evacuations. The objective of this study was to find if changing driver aggressiveness will significantly impact the expected duration of a no-notice evacuation. In this study, the authors used microscopic traffic simulation software to model travel demand for a no-notice evacuation scenario in the St. Louis metropolitan area and represented driver aggressiveness by adjusting headway, following distance, and other parameters. This model included the traffic volumes predicted by the regional planning agency and the route diversions and freeway service patrol operations expected by the local state Department of Transportation. Although the findings suggested that changes in driver aggressiveness will not significantly change the evacuation duration; for key evacuation routes, a significant reduction in travel time and delay was found when driver aggressiveness decreased, compared to the observed normal peak hour driver behavior. These findings suggest that traffic engineers and emergency managers should carefully consider the wording of evacuation information provided to drivers in an attempt to reduce anxiety and prevent aggressive driving during a no-notice evacuation. Further, research should attempt to capture driver behavior during future evacuations so modelers can model travel behavior more accurately.
Article
While the wildland-urban interface (WUI) is not a new concept, fires in WUI communities have rapidly expanded in frequency and severity over the past few decades. The number of structures lost per year has increased significantly, due in part to increased development in rural areas, fuel management policies, and climate change, all of which are projected to increase in the future. This two-part review presents an overview of research on the pathways for fire spread in the WUI. Recent involvement of the fire science community in WUI fire research has led to some great advances in knowledge; however, much work is left to be done. While the general pathways for fire spread in the WUI (radiative, flame, and ember exposure) are known, the exposure conditions generated by surrounding wildland fuels, nearby structures or other system-wide factors, and the subsequent response of WUI structures and communities are not well known or well understood. This first part of the review covers the current state of the WUI and existing knowledge on exposure conditions. Recommendations for future research and development are also presented for each part of the review.
Article
This research documents more than thirty surface transportation modeling tools that have been applied or could be applied to evacuation modeling. Each tool represents a tradeoff between desired scope and analytical complexity, ranging from state-to-state coordination tools such as the Evacuation Traveler Information System (ETIS) to detailed traffic micro-simulation models such as the TSIS/CORSIM traffic simulation model. Based upon a comprehensive literature review, the report first provides a summary of evacuation event types and then a description of the three general classes of modeling approaches (macro, meso, micro) currently used to model evacuation events. Some tools have been developed to model specific types of evacuations, others have more general applications. The modeling inventory concludes with an analysis of the tools as they relate to a modeling spectrum according to scope and analytical complexity, including a discussion of how the decisions supported by analysis drive tradeoffs in terms of scale and computational speed.
Article
Differences in driving behavior due to the presence of users familiar (or unfamiliar) with the road are considered in the road and traffic engineering. However, although considered, the matter is largely unexplored: there is a lack of theoretical foundations and data on determining the impact of route familiarity on accident rates, speed choice and risk perception. On the other hand, some literature studies confirm that route familiarity is influential on driving behavior, encouraging research in this sense.This paper reports the results of an on-road test carried out on a two lane rural road in the District of Bari in the Puglia Region (Italy) over six days of testing by following this time schedule: first four tests in four consecutive days, the fifth test in the ninth day after the first test and the sixth test in the twenty-sixth day after the first test. The main aim of the experiment was to find relationships between route familiarity and speed choice. In particular, speed data were analyzed by considering the influence of road geometry and human factors.The main finding is that speed choice seems to be affected by route familiarity: speed increases with the repetition of travels on the same route. The particular schedule used for the tests allows to consider the influence of memory on the speed behavior of the test drivers. Moreover, some relationships between changes in speed over days, road geometry and drivers' attitudes were shown.
Article
Wildfires cause devastation on communities, most significantly loss of life. The safety of at-risk populations depends on accurate risk assessment and emergency planning. Evacuation modelling and simulation systems are essential tools for such planning and decision making. During a wildfire evacuation, the behaviour of people is a key factor; what people do, and when they do it, depends heavily on the spatio-temporal distribution of events in a scenario. In this paper, we introduce an approach that enables the behaviour of people and the timing of events to be explicitly modelled through what we term dynamic factors. Our approach composes several simulation and modelling systems, including a wildfire simulator, behaviour modeller, and microscopic traffic simulator, to compute detailed projections of how scenarios unfold. The level of detail provided by our modelling approach enables the definition of a new risk metric, the exposure count, which directly quantifies the threat to a population. Experiments for a wildfire-prone region in Victoria, Australia, resulted in statistically significant differences in clearance times and exposure counts when comparing our modelling approach to an approach that does not account for dynamic factors. The approach has been implemented in a high performance and scalable system - the architecture of which is discussed - that allows multiple concurrent scenarios to be simulated in timeframes suitable for both planning and response use cases.
Article
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Article
This statistical meta-analysis (SMA) examined 38 studies involving actual responses to hurricane warnings and 11 studies involving expected responses to hypothetical hurricane scenarios conducted since 1991. The results indicate official warnings, mobile home residence, risk area residence, observations of environmental (storm conditions) and social (other people's behavior) cues, and expectations of severe personal impacts, all have consistently significant effects on household evacuation. Other variables—especially demographic variables—have weaker effects on evacuation, perhaps via indirect effects. Finally, the SMA also indicates that the effect sizes from actual hurricane evacuation studies are similar to those from studies of hypothetical hurricane scenarios for 10 of 17 variables that were examined. These results can be used to guide the design of hurricane evacuation transportation analyses and emergency managers' warning programs. They also suggest that laboratory and Internet experiments could be used to examine people's cognitive processing of different types of hurricane warning messages.
Article
Under no-notice conditions in which family members are collecting dependents, the geographic location and the characteristics (e.g., the number of entrances and exits) of the pickup points become factors crucial to efficient evacuation. This paper presents a linear integer mathematical program for facilities to relocate dependents who need to be picked up in an optimal manner. The program is iterated with a traffic simulation model to obtain an optimal set of locations to which dependents are relocated, on the basis of anticipated travel times. The entire methodology is applied to a sample network based on the Chicago Heights, Illinois, network with three safety time thresholds. The results indicated that the safe evacuation time threshold is important to the relocation strategy. When the safe evacuation time threshold is adequate, the relocation of dependents increases the number of successful evacuees and increases the average travel speed of the network; it also significantly benefits those who rely on public transit to evacuate because new sites are closer to bus stops and walking times to those stops are reduced. Application of the proposed methodology can assist local decision makers with taking effective measures during no-notice evacuations, and the relocation sites could be part of local evacuation management plans.
Article
Route guidance instructions are crucial in the implementation of an evacuation plan. Considering travelers' compliance with these instructions is controllable by adopting traffic management at intersections, a simulation-based framework for optimizing traffic management is presented with the objective function of maximizing evacuation efficiency with uncertain budget constraint. A comprehensive case study illustrates the sensitivity of traffic simulation model with traffic demand, duration of hazard, and traffic management. The specific analyses on network performances provide some practical insights. In reality, mandatory traffic management is unnecessary as the optimal instructions are unavailable. Well-staged departure and appropriate enforcement of traffic management at intersections are recommended, which contribute to extensive distribution of traffic flow and then high-efficiency evacuation.
Conference Paper
This paper discusses this ‘new’ model type and uses a new development in the Cube transport planning software package, Cube Avenue, to exemplify how mesoscopic dynamic traffic assignment models can help model very congested areas to the required level of detail. Keywords: transportation planning, mesoscopic models, traffic congestion. 1 Introduction Transport modelling is mostly done on a strategic level often categorised as ‘macroscopic’ modelling. For traffic engineering and area traffic control/intelligent traffic management purposes, the ‘microscopic’ models have become very popular and useful. The macroscopic models can cover a very large area but their shortfall for detailed traffic planning purposes is their inability to model the required level of detail in congested areas. The microscopic simulation models traffic dynamically and captures this level of detail perfectly, but its shortfall is the inability to model route choice properly and it is also very limited in terms of model sizes. So, there is scope for a ‘mesoscopic’ modelling level that can handle the right level of detail for large study areas. Cube Avenue, an extension to the macroscopic planning module Cube Voyager, offers transportation professionals an innovative tool for analyzing traffic. With Cube Avenue, analysts can study problems for which traditional models don’t provide enough data and for which microscopic models provide too much data.
Article
This paper presents an agent-based travel demand model system for hurricane evacuation simulation, which is capable of generating comprehensive household activity-travel plans. The system implements econometric and statistical models that represent travel and decision-making behavior throughout the evacuation process. The system considers six typical evacuation decisions: evacuate/stay, accommodation type choice, evacuation destination choice, mode choice, vehicle usage choice, and departure time choice. It explicitly captures the shadow evacuation population. In addition, the model system captures pre-evacuation preparation activities using an activity-based approach. A demonstration study that predicts activity-travel patterns using model parameters estimated for the Miami-Dade area for a hypothetical category-4 hurricane is discussed. The simulation results clearly indicate the model system produces a distribution of choice patterns that is consistent with sample observations and existing literature. The model system also identifies the proportion of the shadow evacuation population and their geographical extent. About 23% of the population outside the designated evacuation zone would evacuate. The shadow evacuation demand is mainly located within 5 km of the coastline. The output demand of the model system works with agent-based traffic simulation tools and conventional trip-based simulation tools.
Article
Time geography had led geographers to analyse and model activity-travel patterns since the 1970s. The notion that activity-travel patterns are highly constrained has been frequently used in analytical studies and models of space-time behaviour. The popularity of this field of research lost most of its momentum in geography in the 1990s, but is now the dominant approach among civil engineers in transportation research. This paper critically reviews these developments. It briefly summarizes recent developments in space-time research, focusing on empirical and modelling studies. Potential strengths and weakness of the various modelling approaches are discussed.
Article
This paper describes our research into the processes that govern driver attention and behavior in familiar, well-practiced situations. The experiment examined the effects of extended practice on inattention blindness and detection of changes to the driving environment in a high-fidelity driving simulator. Participants were paid to drive a simulated road regularly over 3 months of testing. A range of measures, including detection task performance and driving performance, were collected over the course of 20 sessions. Performance from a yoked Control Group who experienced the same road scenarios in a single session was also measured. The data showed changes in what drivers reported noticing indicative of inattention blindness, and declining ratings of mental demand suggesting that many participants were “driving without awareness”. Extended practice also resulted in increased sensitivity for detecting changes to road features associated with vehicle guidance and improved performance on an embedded vehicle detection task (detection of a specific vehicle type). The data provide new light on a “tandem model” of driver behavior that includes both explicit and implicit processes involved in driving performance. The findings also suggest reasons drivers are most likely to crash at locations very near their homes.
Article
This paper presents a review of highway-based evacuation modeling and simulation and its evolution over the past decade. The review includes the major components of roadway transportation planning and operations, including the current state of modeling in the forecasting of evacuation travel demand, distribution and assignment of evacuation demand to regional road networks to reach destinations, assignment of evacuees to various modes of transportation, and evaluation and testing of alternative management strategies to increase capacity of evacuation networks or manage demand. Although this discussion does not cover recent work in other modes used in evacuation such as air, rail, and pedestrian, this paper does highlight recent interdisciplinary modeling work in evacuation to help bridge the gap between the behavioral sciences and engineering and the application of emerging techniques for the verification, validation, and calibration of models. The manuscript also calls attention to special considerations and logistical difficulties, which have received limited attention to date. In addition to these concerns, the following future directions are discussed: further interdisciplinary efforts, including incorporating the medical community; using new technologies for communication of warnings and traffic condition information, data collection, and increased modeling resolution and confidence; using real-time information; and further model refinements and validation.
Article
The goal of this paper is to develop a random-parameter hazard-based model to understand hurricane evacuation timing by individual households. The choice of departure time during disasters is a complex dynamic process and depends on the risk that the hazard represents, the characteristics of the household and the built environment features. However, the risk responses are heterogeneous across the households; this unobserved heterogeneity is captured through random parameters in the model. The model is estimated with data from Hurricane Ivan including households from Alabama, Louisiana, Florida and Mississippi. It is found that the variables related to household location, destination characteristics, socio-economic characteristics, evacuation notice and household decision making are key determinants of the departure time. As such the developed model provides some fundamental inferences about hurricane evacuation timing behavior.