Evacuation Travel Time Research Articles

Predicting temporal evacuation travel time in staircases between adjacent floors of super high-rise buildings by artificial neural networks

Predicting temporal evacuation travel time in staircases between adjacent floors is crucial for dynamic evacuation guidance to improve vertical evacuation efficiency during emergencies in super high-rise buildings. However, the prediction methods used in previous studies mainly focus on the total evacuation time in buildings and cannot predict the temporal evacuation travel time in every single part of the total evacuation route. This study proposes a novel method to predict temporal evacuation travel time in staircases between adjacent floors of super high-rise buildings by using artificial neural networks. Firstly, evacuation simulation data of a 71-storey office building are analyzed for feature engineering and dataset building. Secondly, three types of artificial neural networks, including convolutional neural networks, RNN-based model (the traditional recurrent neural networks, long-short term memory networks, and bidirectional long-short term memory networks), and Attention-based model (Attention model and Transformer model) are extended to predict the temporal evacuation travel time in staircases between adjacent floors based on the simulation data. Finally, the prediction results by different artificial neural networks are compared. The results show that the artificial neural networks can provide accurate prediction values for the temporal evacuation travel time in staircases between adjacent floors of super high-rise buildings.

Pre-disaster evacuation network design with uncertain demand and behavioural choices: A bi-criteria generalized cost perspective

Pre-disaster evacuation network design is critical to improving evacuation efficiency and reducing the loss of life and property caused by disasters. However, evacuation network design is complicated by the uncertainty of pre-disaster evacuation demand and the heterogeneity of evacuees’ route choices when faced with a disaster scenario. Therefore, how to design efficient evacuation networks considering these uncertainties has become an important research topic. In this paper, we study the pre-disaster flood evacuation network design problem, define a bi-criteria generalized cost metric based on Evacuation Travel Time (ETT) and Evacuation Network Congestion Degree (ENCD), and develop a model that considers uncertain demand and evacuation behavioural choices to minimize the generalized cost in the evacuation network. For the uncertain behavioural choice, we propose an Expanded Stochastic User Equilibrium (ESUE) model based on the Random Regret Minimization (RRM) principle and Boundedly Rational User Equilibrium (BRUE) conditions to describe it. For the uncertain demand, we design a Distributionally Robust Optimization (DRO) method based on Mean Absolute Deviation (MAD) to deal with this uncertain model. This paper designs an Improved Method of Successive Averages Combined with Genetic Algorithm (IMSA-GA) to solve the model and uses the Nguyen-Dupuis test network to verify the applicability and feasibility of the model and algorithm. In addition, we also conduct an evacuation network design in the Hawkesbury-Nepean region of Australia. The larger the threshold value based on the regret utility ratio, the smaller the generalized cost value, which indicates the behavioural choices of the evacuees have an impact on the evacuation network design, and the generalized cost decreases by 6.12% when the threshold value is increased from 0.1 to 0.9. In addition, the generalized cost value obtained by IMSA-GA will be 0.0025 lower than Improved Method of Successive Averages Combined with Simulated Annealing (IMSA-SA) and 0.0005 lower than Improved Method of Successive Averages Combined with Taboo Search (IMSA-TS). The theoretical contribution of this paper is to find an approach to the uncertain evacuation network design problem, especially to improve the practicality of the model by fully considering evacuation behaviour. The practical contribution is that our findings can provide insights for government authorities to design efficient and reliable evacuation networks that balance evacuation travel time and evacuation network congestion. In the future, if evacuation behavioural choices can be analysed based on actual surveys, the estimation of behavioural parameters based on survey data will be more instructive for the evacuation process.

Evacuation Scenario Simulator with Location-Based Social Network Data Support

Disaster management systems apply different evacuation models to provide transportation responses to disaster situations. The main goal of the evacuation models is to reduce evacuation times to save lives.The significant increase in the use of location-based social networks (LBSN) as a major media factor can provide data for inferring the Origin-Destination matrix that represents the demands for trips during routine times. To achieve the goal of reducing overall evacuation travel times, an innovative methodology was developed that supports LBSN data for both demand prediction and decentralized personal destination recommendation.A modular multi-dimensional tool for evacuation scenario simulation (MMDT-ESS) was developed for supporting this methodology. This tool can handle multiple scenarios and compute performance evaluation values of VhT (Vehicle-Hours Traveled). MMDT-ESS can be applied using both conventional evacuation models (i.e., evacuation to predefined shelters) or decentralized personalized evacuation models and can provide different recommendations: either self-evacuation to nearest family or best friend that is situated outside the affected area or to a predefined shelter area.A test case using data from a U.S. metropolitan agency showed significant reduction in VhT when choosing evacuation scenarios with LBSN data for decentralized evacuation. Sensitivity analysis on main model parameters illustrate the robustness of the results.

Cascadia Subduction Zone Residents’ Tsunami Evacuation Expectations

The U.S. Pacific Northwest coast must be prepared to evacuate immediately after a Cascadia Subduction Zone earthquake. This requires coastal residents to understand the tsunami threat, have accurate expectations about warning sources, engage in preimpact evacuation preparedness actions, and plan (and practice) their evacuation logistics, including an appropriate transportation mode, evacuation route, and destination. A survey of 221 residents in three communities identified areas in which many coastal residents have reached adequate levels of preparedness. Moreover, residents who are not adequately prepared are willing to improve their performance in most of the areas in which they fall short. However, many respondents expect to engage in time-consuming evacuation preparations before evacuating. Additionally, their estimates of evacuation travel time might be inaccurate because only 28–52% had practiced their evacuation routes. These results indicate that more coastal residents should prepare grab-and-go kits to speed their departure, as well as practice evacuation preparation and evacuation travel to test the accuracy of these evacuation time estimates. Overall, these results, together with recommendations for overcoming them, can guide CSZ emergency managers in methods of improving hazard awareness and education programs. In addition, these data can guide transportation engineers’ evacuation analyses and evacuation plans.

Fast-moving dire wildfire evacuation simulation

Extreme wildfire events are fires that move too fast or erratically for adequate evacuation or notification. Modeling evacuations from these wildfires is markedly different than modeling disasters with slower or more predictable movements. There is a gap in our knowledge of human evacuation behavior under these conditions. In this paper, we develop an agent-based simulation model (ABM) of the 2018 Camp Fire evacuation in Northern California, an extreme wildfire evacuation. We use a post-disaster survey and decision tree methods to model agent movements, empirically grounding evacuee behavior. We explore individual scenarios inspired by the Camp Fire, simulating limited smartphone access, delays in awareness time, and reduced vehicle access. We then combine these to produce “worst case” critical scenarios. Our results show that longer evacuation travel times are associated with reduced smartphone use, increased delays in awareness, and reduced vehicle access. More agents are trapped with awareness delays and limited vehicles.

Dynamic Sign Guidance Optimization for Crowd Evacuation considering Flow Equilibrium

The dynamic exit sign has been verified as an effective means to guide the pedestrian during evacuation. The most critical mechanism with dynamic exit sign guidance is to balance the pedestrian flow on each exit route by optimizing the direction of signs. This paper formulates a bi-level programming model for the direction optimization problem of dynamic signs in buildings. In the bi-level program, the upper-level model is a system optimal model, aiming to minimize the total travel time by optimizing the dynamic sign direction. The lower-level model is a pedestrian assignment model satisfying the dynamic user optimal principle that describes the evacuee exit/route choice behaviour to achieve a balanced pedestrian distribution on the route. A method based on the fundamental diagram, the cell transmission model, and the point-queuing theory is developed to estimate evacuation travel time considering congestion and queuing. A heuristic algorithm is extended to solve the bi-level program. Finally, the proposed methodology is validated with numerical examples. Results reveal that the proposed model can produce the optimal dynamic sign direction, significantly improving the evacuation efficiency.

Evacuation Preparation Scenarios of Households during Early and Emergency Evacuation: A Case Study of Cyclone Bulbul in Southwestern Coastal Bangladesh

Bangladesh is one of the most disaster-prone countries worldwide. Every year, the country sees a small- or large-scale disaster, with past disaster history showing that cyclones have been a major phenomenon. Cyclone Bulbul made landfall on the southwestern coast of Bangladesh on November 10, 2019, with an average storm surge height of 1.0 to 1.5 meters—resulting in the inundation of low-lying areas in 14 coastal districts. This study conducted a questionnaire survey of 413 households in Gabura, a union under Shyamnagar Upazila of Satkhira District, from mid-February to mid-March 2020 to investigate respondents’ evacuation experience during Cyclone Bulbul. The questionnaire survey comprised both qualitative and quantitative approaches that analyzed disaster preparedness level, successful evacuation scenarios, and early evacuation decisions and their implementation in Gabura Union during Cyclone Bulbul. The survey results revealed that approximately 55% of the respondents evacuated during Cyclone Bulbul. Among them, 47% made early evacuation decisions, but an analysis of their evacuation starting time revealed that only 35.5% of them evacuated early before Cyclone Bulbul made landfall. Moreover, this research highlighted the importance of having adequate preparation time before starting evacuation, with most of the household respondents stating that they needed 2 hours in preparation time before starting evacuation. However, the preparation time varied between 1 and 6 h depending on the evacuation stage, the type of preparation activities, and the presence of vulnerable members in the house. Both categories of respondents, that is, those who made early evacuation decisions and those who followed through, cited evacuation companions, the presence of sick members in the house, distance from home to shelter, evacuation travel time, and socio-economic characteristics as common influential factors. Respondents in the emergency evacuation category identified previous disaster experience, the presence of sick members in the house, and socio-economic aspects as the factors that they considered while making evacuation decisions during Cyclone Bulbul. This analysis of factors influencing early evacuation decisions, early evacuation, and emergency evacuation helped shed light on people’s perceptions about disasters, which can guide authorities in taking policy actions to improve early evacuation possibility and issuing early evacuation orders to ensure safe and successful evacuation during cyclones or other disasters in the future.

Spatial modeling of tsunami for evacuation planning in Ciletuh Bay, Sukabumi, West Java

Indonesia is a country located at the meeting of three tectonic plates. This condition makes Indonesia a disaster-prone region, especially caused by the movement of tectonic plates, one of which is the tsunami. Several tsunami occurs were recorded in Indonesia. Tsunami occur on a large scale and causes losses, both environmental damage and fatalities. Therefore, evacuation planning is needed to minimize the number of fatalities when a tsunami occurs. This study aims to find out the potential threat of tsunami hazard in Ciletuh Bay and analyze evacuation planning in the region. The method used in this research is the spatial modeling method which is divided into tsunami inundation modeling, population distribution, and estimated evacuation travel time. The result of this study is that potential tsunami hazard in Ciwaru Village id found in two hamlets with an area of 104.63 Ha. A total of 590 exposed residents can evacuate within 0 – 15 minutes. The selection of evacuation routes with the fastest travel time is needed so that residents can reach the evacuation collection point before the tsunami arrives. The availability of road access in the hamlet makes it easier for residents in tsunami-exposed areas to reach evacuation gathering points.

Tsunami evacuation analysis in the urban built environment: a multi-scale perspective through two modeling approaches in Viña del Mar, Chile

ABSTRACT In the case of near-field tsunami emergencies, evacuation is considered the most important and effective method to save human lives. While significant efforts have been carried out to examine tsunami evacuation within the urban built environment, challenges remain on delivering a more detailed examination of the latter’s suitability for this task, and on addressing different scales of analysis. Along these lines, this paper draws upon a multi-scale framework for examining resilience of the urban form, to carry out a multi-scale tsunami evacuation analysis of the Great Valparaíso Metropolitan Area (GVMA) and the city of Viña del Mar, Chile, supported by both LCD and agent-based modeling approaches. The results show a significant macro-scale tsunami exposure of some areas in the territory, particularly in the Población Vergara and downtown neighborhoods. In line with this, the meso-scale evacuation analysis shows moderate escape rates (as low as 61.4%) in these areas. Moreover, the comparison between the models’ outcomes show that micro-scale vulnerabilities can potentially increase evacuation travel times by roughly 11%. These results, however, are noticeably dependent on the modeling choice.

Defining evacuation travel times and safety areas in a debris flow hazard scenario

Debris flows are one of the most hazardous types of landslides in mountain regions. In the upper part of the Zêzere valley (Serra da Estrela, Portugal) several debris flows events occurred in the last 200 years, some of them causing loss of lives and material damages. In this work, a methodology for pedestrian evacuation modelling, in a debris flow hazard scenario, was implemented. A dynamic run-out model, developed in previous studies, was used to evaluate the debris flows velocities, thickness of the deposits and extent of the mobilized material. The buildings potentially affected by the impact of debris flows were identified and the potentially exposed population was estimated by applying a dasymetric distribution. The results lead to the conclusion that, in the study area, the elderly are those who are most exposed to debris flows. Furthermore, the time lapse between the debris flows initiation and the arrival at the buildings at risk was estimated, allowing to account for the overall number of buildings where the evacuation time takes longer than the debris flows arrival. Additionally, the safe areas within the study area were identified, and several safe public buildings with the capacity to gather a large number of persons were selected. Considering that the study area is located in a mountain region, characterized by steep slopes, the evacuation modelling was performed based on an anisotropic approach, in order to consider the influence of slope direction on travel costs. At the end, three pedestrian evacuation travel time scenarios, based on different walking speeds to accommodate residents with different ages in safer places, were compared and the results mapped. The implemented methodology is not local dependent, which allows its reproduction elsewhere.

Agent-based discrete-event simulation model for no-notice natural disaster evacuation planning

Over the past century, natural disasters have been often large-scale and overwhelming catastrophes resulting in irreparable and uncontrollable damage. In order to decrease the average evacuation travel time and reduce casualties, as well as improve the medical care for vulnerable populations in natural disasters, it is worthwhile to look at strategic disaster evacuation planning. In this paper, an agent-based discrete-event simulation (ABDES) modeling framework based on an embedded GIS module is developed for making no-notice natural disaster evacuation planning and improving the evacuation strategy periodically by using the proposed simulation model. Furthermore, in order to examine the applicability and extensibility of the proposed integrated GIS-based ABDES modeling framework, we conduct the experiments using several practical scenarios based on the city of San Francisco in California. The proposed evacuation simulation modeling framework can offer decision-makers significant insight on the traffic assignment and the configuration of resources such as evacuation vehicles and relief facilities that are required to complete successfully an evacuation against the no-notice large-scale disasters.

Community clusters of tsunami vulnerability in the US Pacific Northwest

Many coastal communities throughout the world are threatened by local (or near-field) tsunamis that could inundate low-lying areas in a matter of minutes after generation. Although the hazard and sustainability literature often frames vulnerability conceptually as a multidimensional issue involving exposure, sensitivity, and resilience to a hazard, assessments often focus on one element or do not recognize the hazard context. We introduce an analytical framework for describing variations in population vulnerability to tsunami hazards that integrates (i) geospatial approaches to identify the number and characteristics of people in hazard zones, (ii) anisotropic path distance models to estimate evacuation travel times to safety, and (iii) cluster analysis to classify communities with similar vulnerability. We demonstrate this approach by classifying 49 incorporated cities, 7 tribal reservations, and 17 counties from northern California to northern Washington that are directly threatened by tsunami waves associated with a Cascadia subduction zone earthquake. Results suggest three primary community groups: (i) relatively low numbers of exposed populations with varied demographic sensitivities, (ii) high numbers of exposed populations but sufficient time to evacuate before wave arrival, and (iii) moderate numbers of exposed populations but insufficient time to evacuate. Results can be used to enhance general hazard-awareness efforts with targeted interventions, such as education and outreach tailored to local demographics, evacuation training, and/or vertical evacuation refuges.

Selecting Four-Leg Intersections for Crossing Elimination in Evacuations

This study investigates the implementation of crossing elimination con-figurations at intersections, by neighborhood characteristics; a “neighborhood” is defined by its surrounding intersections and by supply and demand situations to identify evacuation travel time impacts and situations and crossing elimination configurations that should be considered and further investigated for implementation in practice or should be avoided in practice. A hypothetical network of 41 signalized intersections modeled in microscopic simulation software was used to test 104 scenarios. On the basis of the analyses, crossing elimination should be considered and should be further investigated for evacuations with high demand and when the evacuation flow is asymmetric but oriented with the major roads. Crossing elimination should generally be avoided or carefully implemented when the evacuation flow is asymmetric but oriented with the minor roads and when destinations aligned with major roads (rather than minor ones) are unreachable (in at least one of the two directions). This latter situation is the one exception to the high demand recommendation. Pairing of intersection location and crossing elimination configuration is more complicated in terms of the effects on total travel times across scenarios.

Variations in population vulnerability to tectonic and landslide-related tsunami hazards in Alaska

Effective tsunami risk reduction requires an understanding of how at-risk populations are specifically vulnerable to tsunami threats. Vulnerability assessments primarily have been based on single hazard zones, even though a coastal community may be threatened by multiple tsunami sources that vary locally in terms of inundation extents and wave arrival times. We use the Alaskan coastal communities of Cordova, Kodiak, Seward, Valdez, and Whittier (USA), as a case study to explore population vulnerability to multiple tsunami threats. We use anisotropic pedestrian evacuation models to assess variations in population exposure as a function of travel time out of hazard zones associated with tectonic and landslide-related tsunamis (based on scenarios similar to the 1964 M w 9.2 Good Friday earthquake and tsunami disaster). Results demonstrate that there are thousands of residents, employees, and business customers in tsunami hazard zones associated with tectonically generated waves, but that at-risk individuals will likely have sufficient time to evacuate to high ground before waves are estimated to arrive 30–60 min after generation. Tsunami hazard zones associated with submarine landslides initiated by a subduction zone earthquake are smaller and contain fewer people, but many at-risk individuals may not have enough time to evacuate as waves are estimated to arrive in 1–2 min and evacuations may need to occur during earthquake ground shaking. For all hazard zones, employees and customers at businesses far outnumber residents at their homes and evacuation travel times are highest on docks and along waterfronts. Results suggest that population vulnerability studies related to tsunami hazards should recognize non-residential populations and differences in wave arrival times if emergency managers are to develop realistic preparedness and outreach efforts.

Changes in population evacuation potential for tsunami hazards in Seward, Alaska, since the 1964 Good Friday earthquake

Pedestrian evacuation modeling for tsunami hazards typically focuses on current land-cover conditions and population distributions. To examine how post-disaster redevelopment may influence the evacuation potential of at-risk populations to future threats, we modeled pedestrian travel times to safety in Seward, Alaska, based on conditions before the 1964 Good Friday earthquake and tsunami disaster and on modern conditions. Anisotropic, path distance modeling is conducted to estimate travel times to safety during the 1964 event and in modern Seward, and results are merged with various population data, including the location and number of residents, employees, public venues, and dependent care facilities. Results suggest that modeled travel time estimates conform well to the fatality patterns of the 1964 event and that evacuation travel times have increased in modern Seward due to the relocation and expansion of port and harbor facilities after the disaster. The majority of individuals threatened by tsunamis today in Seward are employee, customer, and tourist populations, rather than residents in their homes. Modern evacuation travel times to safety for the majority of the region are less than wave arrival times for future tectonic tsunamis but greater than arrival times for landslide-related tsunamis. Evacuation travel times will likely be higher in the winter time, when the presence of snow may constrain evacuations to roads.

Using consequence – based hazard zone assessment for effective evacuation planning of vulnerable settlements along hazmat transport corridors through industrial city of Surat in western India

Following a toxic hazmat release, decisions need to be made vis-a-vis a safe evacuation distance for population along the transportation route, or whether to shelter-in-place if there is insufficient time to conduct an evacuation. The first responder may have to take these decisions through a quick estimation of the Initial Isolation Zone (IIZ) and protective action distances based on assumed rate of releases. Real-time evacuation planning models could be used for effective evacuation purposes. However, factors that usually were taken into account include weather conditions, population density, time of day, and uncertainty about the chemicals involved and/or the accumulating concentrations in public areas. Emergency planning including evacuation in India is generally in line with UNEP's APELL (Awareness and Preparedness for Emergencies at Local Level) methodology. However, quantitative risk based assessment is catching up particularly in large industrial zones. The paper highlights a case study of the evaluation of identified seven critical evacuation planning zones along three highway study routes in and around the industrial city of Surat in western India. The elapsed time after a release is estimated through ALOHA and evacuation travel time estimates are made for each zone. For identified scenarios, evacuation of IIZ, generally comprising of road side population, is effectively possible in six out of seven identified emergency planning zones except in zone-7, where certain people would still be exposed, as full evacuation may not be achieved. However, shelter-in-place would be advisable for population residing in permanent settlements within the protective action zone.

Wildland firefighter entrapment avoidance: modelling evacuation triggers

Wildland firefighters are often called on to make tactical decisions under stressful conditions in order to suppress a fire. These decisions can be hindered by human factors such as insufficient knowledge of surroundings and conditions, lack of experience, overextension of resources or loss of situational awareness. One potential tool for assisting fire managers in situations where human factors can hinder decision-making is the Wildland–Urban Interface Evacuation (WUIVAC) model, which models fire minimum travel times to create geographic trigger buffers for evacuation recommendations. Utilising multiple combinations of escape routes and fire environment inputs based on the 2007 Zaca fire in California, USA, we created trigger buffers for firefighter evacuations on foot, by engine and by heavy mechanised equipment (i.e. bulldozer). Our primary objective was to examine trigger buffer sensitivity to evacuation mode and expected weather and fuel conditions. Evacuation travel time was the most important factor for determining the size and extent of modelled trigger buffers. For the examined scenarios, we show that WUIVAC can provide analytically driven, physically based triggers that can assist in entrapment avoidance and ultimately contribute to firefighter safety.

Sensitivity of Evacuation Performance Estimates to Evacuee Route Choice Behavior

The objective of this paper is to illustrate the sensitivity of evacuation travel time estimates in relation to assumptions about evacuee route choice behavior. The user equilibrium (UE) assignment procedure in which evacuees are assumed to choose the shortest (or fastest) path from their origins to destinations is considered as the base case. Few studies have documented the actual route choice behavior of evacuees during a hurricane. These studies were used to develop realistic route choice behavior assumptions: the types of route choices and proportion of evacuees for those types. The impact of route choice behavior on evacuation performance, especially travel times, was then explored with a large-scale regional simulation model of the Hampton Roads, Virginia, region. The model was developed in an earlier study with real-world network data and actual evacuee behavioral data. The analysis indicates that using UE to generate average travel time estimates for evacuation planning significantly underestimates the actual evacuation travel times. The extent of underestimation of evacuation travel times depends on the total evacuation demand (a function of storm intensity) and the percentage of evacuees willing to use en route information to seek alternate routes when facing congestion. For the three en route percentages reported in the literature (30%, 50%, and 70%), the UE travel times were 58%, 42%, and 33% lower than actual travel times in a Category 1 evacuation; 94%, 71%, and 58% lower than in a Category 2 evacuation; and 90%, 69%, and 54% lower than in a Category 3 evacuation.

Microsimulation of Neighborhood Evacuations in the Urban–Wildland Interface

Residential development in fire-prone wildlands is occurring at an unprecedented rate. Community-based evacuation planning in many areas is an emerging need. In this paper we present a method for using microscopic traffic simulation to develop and test neighborhood evacuation plans in the urban–wildland interface. The method allows an analyst to map the subneighborhood variation in household evacuation travel times under various scenarios. A custom scenario generator manages household trip generation, departure timing, and destination choice. Traffic simulation, route choice, and dynamic visualization are handled by a commercial system. We present a case study for a controversial fire-prone canyon community east of Salt Lake City, Utah. GIS was used to map the spatial effects of a proposed second access road on household evacuation times. Our results indicate that the second road will reduce some household travel times much more than others, but all evacuation travel times will become more consistent.