Evacuation Model Research Articles

Simulating the Evacuation Process Involving Multitype Disabled Pedestrians

The study of crowd evacuation has received considerable attention as the frequent occurrence of crowd disasters in public places. Notably, the increasing proportion of disabled pedestrians makes vulnerable crowds an indispensable part of the evacuation process. However, most previous research neglects to introduce the motion characteristics of disabled pedestrians into the modeling of crowd evacuation. Therefore, we develop an extended model to simulate the evacuation process involving nondisabled, visual-disabled, acoustic-disabled, and physical-disabled pedestrians. Numerical simulations indicate that this model achieves a more realistic mixed crowd evacuation in the library scene and reproduces the escape movement of multitype disabled pedestrians. Moreover, several management strategies are provided to guide the evacuation of disabled pedestrians, and the appropriate strategy can be determined by comprehensively considering multiple factors such as efficiency, safety, and cost.

Revolutionizing indoor emergency evacuation prediction with machine learning: A Hand‐searching technique and content analysis on Random Forest algorithm

AbstractMachine learning and emergency evacuation prediction share a meaningful association in the context of the Industrial Revolution 4.0 era, given that machine learning can potentially enhance emergency evacuation prediction processes. Machine learning has the potential to revolutionise emergency evacuation prediction by providing a data‐driven approach that aids decision‐making and response time in emergencies. The complexity of emergency situations can lead to tragic accidents, as individual behaviour heavily influences evacuation time. While some researchers have explored emergency evacuation, existing computer evacuation models lack detailed analysis. To address this gap, this study focuses on reviewing the Random Forest algorithm, an advanced machine learning algorithm based on Decision Trees, in the application of forecasting emergency evacuation time. Through a qualitative research approach using the innovative Hand‐searching technique, we conducted a systematic review employing Content Analysis Theory. Multiple sources were examined, including Google Scholar, Science Direct, Scopus, and Universiti Kebangsaan Malaysia e‐Journal System. The study's findings shed light on the performance, prediction factors, advantages, and limitations of Random Forest in identifying impacts during an emergency evacuation. These insights hold significant implications for emergency responders, building designers, and policymakers.

Simulation of a multi-floor evacuation with the extended social force model in a multi-corridor environment

Abstract For the stagnation problem that easily occurs in the process of simulating multi-floor and multi-staircase evacuation using a traditional social force model, in this paper, it is proposed to improve the social force model by introducing a moth-flame optimization algorithm, thus to establish a new evacuation model. The model firstly integrates the field model into a social force model as the pedestrian self-driving direction. Meanwhile, an objective optimization function of minimum system evacuation time is established based on the evaluation indexes, such as staircase congestion degree and average velocity, and the moth-flame optimization algorithm is improved by introducing dynamic inertia weight and random reverse learning strategy, thus establishing an evacuation optimization method. Finally, a simulation and numerical analysis is carried out for the multi-floor evacuation process using the experimental simulation platform built, which deeply analyzes the key factors influencing the model, gives the change relationships among the parameters such as evacuation time, initial pedestrian velocity, the number of pedestrians and staircase width, and verifies the effectiveness of the model.

Evacuation effect analysis of guidance strategies on subway station based on modified cellular automata model

With the extension of the subway network and the increase in passenger flow, higher traffic pressure and risk level have occurred. Adopting appropriate guidance strategies is a critical factor in improving the evacuation effect and reducing casualties in subway stations. Automatic fare gate (AFG) groups are the key facilities in evacuation guidance, as their limited traffic capacity can easily cause congestion. This study proposes a modified cellular automata model that combines the multi-phase goals of passengers in the evacuation process to characterize the behavior of three guidance strategies: (A) Balancing passengers’ AFG group selection; (B) Balancing passengers’ AFG selection; (C) Setting dedicated AFGs for slower passengers. The pedestrian parameters obtained by field observation are used as inputs for the proposed evacuation model. The total evacuation time and the average waiting time for different types of passengers and key facilities are calculated. The impact of different guidance strategies on evacuation performance is explored in various scenarios. The results show that a combination of A and B strategies would achieve the shortest total evacuation time. While a combination of A, B, and C strategies would achieve the shortest average waiting time. The improvement of evacuation efficiency brought by guidance is proved significant even when only part of the people executes the arrangement.

Assessment of pre-warning, pre-travel and travel behavior interactions with smoke and toxic gases during fire incidents

Evaluation of potential fire scenarios and escape outcomes involves data and calculations for many different parameters and their interactions, with uncertainties regarding application to real systems. Assessment of fire development, occupant behaviors and effects of toxic smoke exposure during actual incidents can provide a reality check on engineering methods, data quality, assumptions and limitations. Because incidents encompass the performance of entire occupied built systems, they enable witness accounts, survival, medical and pathology outcomes and evidence of fire development and toxic smoke spread to be combined with fire, evacuation and toxicity modelling calculations. Since outcomes are known, they constrain the predictions derived, revealing previously unidentified parameters and enabling validation of current methodology. Examples from incidents highlight the following findings.• Pre-warning delays – neglected parameter, high consequence.• Effects of fire cues and warnings on occupant behavior and pre-travel times, and stair descent speeds in relation to visibility – generally validate current methods.• Probability of evacuation through irritant smoke– high consequence, needs more consideration.• Times to incapacitation from asphyxiant gases as a function of fuel composition and smoke density – useful assessment method.• Using smoke and carbon monoxide exposure toxicology to estimate fire development and exposure history - Useful method for fire scenario validation, confirms FED method validity.

The analysis of traffic data of wildfire evacuation: the case study of the 2020 Glass Fire

Evacuation is a crucial policy to mitigate wildfire impacts. Understanding traffic dynamics during a wildfire evacuation can help authorities to improve in improving emergency management plans, thus improving life safety. In this study, we developed a methodology to extract historical traffic data from vehicle detector stations and automate the analysis of traffic dynamics for actual wildfire evacuations. This has been implemented in an open-access tool called Traffic Dynamic Analyser (TDA) which generates speed-density and flow-density relationships from data using both commonly used macroscopic traffic models as well as machine learning techniques (e.g., support vector regression). The use of the methodology is demonstrated with a case study of the 2020 Glass Fire in California, USA. The results from TDA showed a slight reduction in speeds and flows on US Highway 101 during the evacuation scenario, compared with the routine scenario. Moreover, background traffic has been shown to play a key role in the 2020 Glass Fire compared with previous wildfire evacuation scenarios (e.g., the 2019 Kincade fire). The case study showed that the methodology implemented in the TDA can be used to understand traffic evacuation dynamics in wildfire scenarios and to validate evacuation models.

Development of a Model for Sediment Evacuation from Reservoirs

Development of a Model for Sediment Evacuation from Reservoirs

An extended model for crowd evacuation considering crowding and stampede effects under the internal crushing

An extended model for crowd evacuation considering crowding and stampede effects under the internal crushing

Dynamic Dose-Based Emergency Evacuation Model for Enhancing Nuclear Power Plant Emergency Response Strategies

The safe evacuation of residents near a nuclear power plant during a nuclear accident is vital for emergency response planning. To tackle this challenge, considering the dynamic dispersion of radioactive materials in the atmosphere and its impact on evacuation routes under different meteorological conditions is crucial. This paper develops a dynamic dose-based emergency evacuation model (DDEEM), which is an efficient and optimized nuclear accident evacuation model based on dynamic radiological dose calculation, utilizing an improved A* algorithm to determine optimal evacuation routes. The DDEEM takes into account the influence of radiological plume dispersion and path selection on evacuation effectiveness. This study employs the DDEEM to assess radiological consequences and evacuation strategies for students residing 5 km from a Chinese nuclear power plant. Under various meteorological conditions, including the three typical meteorological conditions, random ordered and random unordered meteorological sequences, optimal routes obtained through the DDEEM effectively reduce radiological dose exposure and mitigate radiation hazards. The results indicate that all evacuation paths generated by the DDEEM have a maximum dose of less than 1 mSv. Through simulations, the model’s effectiveness and reliability in dynamic radiological environments in terms of radiological consequences and evacuation analysis is verified. The research provides valuable insights and a practical tool for nuclear power plant emergency decision-making, enhancing emergency management capabilities during nuclear accidents. The DDEEM offers crucial technical support and a solid foundation for developing effective emergency response strategies.

Egress Modeling of Evacuation from Railcar - A Case Study

Egress Modeling of Evacuation from Railcar - A Case Study

Ridesharing evacuation models of disaster response

Evacuation models based on public transportation have been shown to increase network capacity for transportation systems while improving community and societal disaster preparedness. However, due to a lack of private vehicles and inconvenient mobility, it is often difficult for vulnerable groups to arrive at shelters on time. To address this issue, this research proposes an evacuation strategy that incorporates the ridesharing concept, allowing individuals with vehicles to provide rides for carless groups. Three mixed-integer programming models are developed based on assumptions such as different capacities and pick-up principles, with the goal of maximizing the number of evacuees transported to assembly points or shelters in a limited amount of time. To evaluate the effectiveness of the proposed ridesharing evacuation models, a real-world case study in Houston is conducted. Numerical analyses are performed with five factors: evacuation scales, data generation, evacuation models, evacuation clearance times, and the number of vehicles involved in the evacuation process. The findings demonstrate that ridesharing evacuation models can provide viable alternative evacuation options to carless and public transit-dependent populations. Furthermore, the study reveals that increasing the number of vehicles to assist vulnerable groups may not be necessary in cities with high population density due to excessive traffic volume, which can hamper disaster response implementation. Additionally, the number of evacuees arriving at shelters or assembly points is unbalanced due to space–time constraints. To address this issue, relief supplies should be distributed on demand in these areas to reduce waste. The findings of this study can inform the development of more effective and efficient evacuation strategies that can better serve communities and vulnerable populations during times of crisis.

Evacuation Simulation under Threat of Wildfire—An Overview of Research, Development, and Knowledge Gaps

Wildfires have become a common incident over the past decades, and they have been threatening people’s lives and assets. In the communities close to wildlands or wildland–urban interfaces (WUI), these threats become increasingly serious, and in case of wildfires, people are advised or often have to evacuate the area to save their lives. In order to have a safe and effective evacuation, data on people’s behavior and decisions during wildfires, evacuation modeling, and traffic simulations are required. This paper reviews past and recent research on evacuation, human behavior in wildfires, evacuation modeling, and traffic simulation. Similar research on evacuation in other situations is also reviewed, and the applicability of the models and simulations on wildfires is discussed. Different stages for an evacuation modeling design are assessed, and the gaps and challenges in obtaining an effective evacuation model are presented.

Cellular Automaton Model for Pedestrian Evacuation Considering Impacts of Fire Products

To accurately simulate realistic pedestrian evacuation from a fire, a cellular automaton model of the dynamic changes in pedestrian movement parameters is developed in conjunction with fire dynamics software. The fire dynamics software is used to simulate the spread of smoke within the scene to obtain visibility and CO concentration data within the scene. We imported the smoke data into the cellular automata and adjusted the pedestrian movement speed over time, resulting in simulation data that closely align with reality. The results show that for the single-room scenario, as pedestrian density increased from 0.1 to 0.5 persons per square meter (p/m2), the influence of the percentage of pedestrians familiar with their location on evacuation efficiency decreased from 44.93% to 24.52%. Conversely, in the multi-room scenario, it increased from 23.68% to 38.79%. The proportion of pedestrians less affected by smoke decreases and stabilizes as the CO yield increases. In the single-room scenario, when the CO yield is below 10%, the crowd with a low percentage of pedestrians familiar with the site is more affected by smoke than those with a high percentage. In the multi-room scenario, the victimization rate of the crowd follows an increasing-then-decreasing curve, ultimately stabilizing with changes in CO yield.

The impact of people with mobility limitations on exit choice

Demographic trends and advances in accessibility have led to more heterogeneous populations in public buildings. While providing accessible evacuation, it may be necessary to direct evacuees to different evacuation paths depending on functional capacity. The presence of people with mobility limitations (e.g., wheelchair users) may affect the evacuation of others, possibly influencing the decision making of neighbouring evacuees. This study investigates how exit choice of people is influenced by the functional capacity of other evacuees and the design of the exit. Virtual Reality was used to design a discrete choice experiment in which participants were exposed to multiple choice situations and a mixed logit model was fitted to the data. Results from 40 participants indicate that exit choice was positively influenced by other people choosing the exit, and a larger effect was found when the other person was a wheelchair user. The design of the exit also influenced the exit choice of the participants. Large inter-person differences existed. Strategies reported by the participants in the subsequent questionnaire included altruistic behaviour. These results can inform fire safety engineers when outlining evacuation strategies and evacuation model users in their assumptions concerning exit choice in building evacuation.

Augmenting the Social Vulnerability Index using an agent-based simulation of Hurricane Harvey

In this work, an agent-based model (ABM) of population evacuation during Hurricane Harvey is developed. The ABM integrates data from several sources, including data about physical conditions, population demographics, and geography. This simulation is then used to compute multiple measures of exposures to hazards for the population. Outputs from the ABM are then evaluated by adding computed measures to a statistical model associating flood exposure to emergency department visits. Finally, the flooding exposure duration measure is used to augment the CDC/ATSDR Social Vulnerability Index.

A machine vision-based method for crowd density estimation and evacuation simulation

In this study, we propose a machine vision-based method for crowd density estimation and evacuation simulation to help reduce the occurrence of stampedes in crowded public places. The method consists of pedestrian detection model, pedestrian positioning algorithm, and cellular automata evacuation model (CAEM). In the pedestrian detection model, an adaptive 2D Gaussian kernel was used to generate crowd density heatmaps for crowd density estimation. The model was found to have achieved a real-time detection accuracy of 96.5% for real-time images of evacuation scenes after being trained on a dataset of 66,808 pedestrian images using the YOLOv3. The pedestrian positioning algorithm was developed to determine the coordinates of pedestrians in real-world scenarios. Experiments show that the average positioning errors in the x and y directions are 0.177 m and 0.176 m, respectively. The pedestrian coordinates were then input into the Python-based cellular automata evacuation model (CAEM) for evaluation simulation, which is compared with the simulation results by Pathfinder, another piece of software that can deliver agent-based evacuation simulation. The difference in evacuation time calculated by the two models was found to be less than a second, which indicates a high degree of consistency.

Exploring single-line walking in immersive virtual reality

With increasing rates of elderly and obese people in the population, questions are being raised about the validity of inputs used by computer evacuation models to predict the movement of crowds in the built environment. The objective of this study is to examine the movement of individuals in a VR environment. Exploring individual movement in VR (where the individual is exposed to a virtual environment with virtual agents while actually moving alone in the physical environment) is a necessary step on the path to determining if VR is a useful tool to gather new crowd movement data. Specifically, this work presents the results of two experiments that were conducted to measure the correlation between inter-person distance (the distance from a participant to a virtual agent) and walking speed. Results show a positive correlation between walking speed and the inter-person distance for inter-person distances between 1.0 and 1.5 m. Above inter-person distances of 1.5 m, walking speed was not dependent on inter-person distance. An important finding from this work is no observed significant difference in the relationship between walking speed and inter-person distance across both experimental setups – ‘pushing’ or ‘following’ configurations. Finally, this work shows the potential of gathering individual movement data using VR.

Fire propagation-driven dynamic intelligent evacuation model in multifloor hybrid buildings

Modern multifloor hybrid (MFH) buildings usually have complex structures and intense closeness, significantly increasing difficulties for the safe evacuation ofpeople in the event of an accidental fire occurrence. Facing the incapacities of traditional evacuation strategies in pointing out the most reasonable evacuation paths in such MFH buildings, it is necessary to study the intelligent evacuation process considering the fire propagation, architectural features, optimal path, and dynamic guidance. To determine the optimal dynamic evacuation strategy after an accidental fire occurrence in MFH buildings, this study proposes a fire propagation-driven dynamic intelligent evacuation (FPDDIE) model. The FPDDIE model includes the optimal path planning method, evacuation time prediction model based on the stacking integration strategy and framework design of active instructions. Among them, the evacuation time prediction model can help people choose safe paths. Finally, a large gymnasium is taken as a real case study to verify the effectiveness of the proposed model and method. The results obtained from the numerical analysis and simulations indicate that the overall evacuation time of the crowd is reduced, the prediction error of individual evacuation time is acceptable and the path replanning can ensure evacuation safety in some fire scenarios.

Human behaviour and evacuation time for large underground comprehensive buildings during fire risk process

Human factors play an indispensable role in process safety during fire risk process. Fire evacuation of underground comprehensive buildings is a critical consideration for practical importance. Here, evacuation experiments were conducted in the Guangzhou East Railway Station Plaza, a large underground comprehensive building in Guangdong, China, with the plaza occupants as the participants. The evacuation factors, namely evacuation time and evacuee-selected evacuation routes, were evaluated using empirical equations (the Togawa model and the Melinek and Booth model) and evacuation modelling software (EVACNET 4 and composite occupant evacuation model). The experimental data were used to verify the empirical equations and evacuation models and to modify specific empirical parameters. The effect on escape route judgement and selection under emergency situations was analysed. The results demonstrated that direct access to outdoor exits could considerably reduce evacuation time. The experimental N and ts were 1 person/m·s and 28 s, respectively. The total evacuation time was 281 s, and the EVACNET 4 and composite models’ simulations of safe evacuation were 285–290 s, which was consistent with the experimental data—validating the high accuracy and applicability of the models.

Underground Evacuation and Smoke Flow Simulation in Guangzhou International Financial City during Fire

The underground space in the Starting Area in the Guangzhou International Financial City is being developed to save resources and improve land benefits. However, high-density development has increased the likelihood of fires. Therefore, PyroSim and Pathfinder were used in this study to investigate the fire smoke flow and personnel evacuation in the underground space in the Starting Area. Firstly, the 2D temperature cloud map and the temperature and visibility recorded by sensor A over time of Zone I in the Starting Area were analyzed. Then, the 3D smoke diffusion, the 3D temperature diffusion map, and the value of thermocouple and smoke obscuration recorded by sensors of Zone II were analyzed. Next, smoke flow of Zones III to V in the Starting Area under different fire source positions was simulated. Finally, the personnel evacuation model was established to simulate the personnel flow rate and density. The simulation results show that the available safe evacuation time for people is 530 s when all the firefighting facilities fail and fire breaks out in Zone I. For large public spaces, the overall spread speed of fire is fast, which requires the use of the fire control system in time to control the spread of fire. Fortunately, the space of evacuation time is relatively sufficient; it only takes 143 s to evacuate personnel safely in Zone II, which is sufficient compared to the time for the fire to completely spread. Suggestions were made for fire safety management, such as evacuating personnel to the safety exits of other adjacent areas during a fire and installing linkage fire alarm systems in large public space s.