Improve Evacuation Efficiency Research Articles

Optimal time interval of information update using by exit-choice system to improve pedestrian evacuation efficiency

In the case of emergency evacuation of large places, the rapid evacuation of pedestrians has always been a matter of great concern. However, research on pre-movement behavior and decision-making fatigue behavior of pedestrians in emergency evacuation situations is limited. In order to study how to improve evacuation efficiency under the introduction of exit-choice system, this paper uses the cellular automata model to simulate different exit arrangements and exit numbers at different time intervals of information update by the system. It is found that there is an optimal time interval of information update when three exits of a room have different widths, and the evacuation time significantly reduces compared to real-time update. The potential mechanism of the optimal time interval of information update is that the evacuees can be reasonably allocated to each exit, so that the passage capacity of each exit can be maximized, and the evacuation time is significantly reduced. Sensitivity analysis shows that the change of exit location has an important influence on the evacuation time. The different pre-movement times of pedestrians have a certain degree of influence on the evacuation time of real-time update. The different distance parameters and flow parameters had a significant impact on the evacuation time in the real-time update case.

Multi-Objective Evcuation Planning Model Considering Post-Earthquake Fire Spread: A Tokyo Case Study

As an integral part of the 2030 Agenda for Sustainable Development, Disaster Risk Reduction (DRR) is essential for human safety and city sustainability. In recent years, natural disasters, which have had a tremendous negative impact on economic and social development, have frequently occurred in cities. As one of these devastating disasters, earthquakes can severely damage the achievements of urban development and impact the sustainable development of cities. To prepare for potential large earthquakes in the future, efficient evacuation plans need to be developed to enhance evacuation efficiency and minimize casualties. Most previous research focuses on minimization of distance or cost while ignoring risk factors. We propose a multi-objective optimization model with the goal of reducing the risk during the evacuation process, which is called the risk reduction model (RRM). Problem-specific indicators for screening optimal solutions are introduced. The research selects the Ogu area in Tokyo as a case study, where there is a relatively high density of wooden structures, increasing the risks of building collapse and fire spread after an earthquake, and is based on a two-phase evacuation flow that considers secondary evacuation for fire response. The results indicate that, in this case, RRM can, in most situations, reduce the risk level during the evacuation process and improve evacuation efficiency and success rate without significantly increasing the total evacuation distance. It proves to be superior to the traditional distance minimization model (DMM), which prioritizes minimizing the total distance as the objective function.

Density control in pedestrian evacuation with incorrect feedback information: Data correction

Improving evacuation efficiency is a central concern in evacuation simulation research. Incorrect feedback information can affect the effectiveness of evacuation control in partially observable evacuation. In this paper, we introduce a framework for evacuation guidance control, emphasizing data prediction and correction to mitigate the impact of abnormal observed data. The framework is built upon force-driven Cellular Automaton (CA) models and employs a data correction module to rectify abnormal information. Guided by this framework, we implement the data correction module’s functionality using Back Propagation (BP) neural networks. We utilize historical simulation data to train the BP network, obtaining a model for correcting abnormal data. Additionally, we integrate the data correction model with density control algorithms to facilitate pedestrian flow management in abnormal evacuation scenarios. Subsequently, we conduct two comparative simulation experiments to verify the algorithm’s effectiveness. One experiment utilizes an abnormal data replacement method, while the other employs a data correction method. The results show that the method of abnormal data replacement is simple, but it cannot improve the control efficiency in abnormal evacuation scenarios. The data correction method proposed in this paper can effectively improve evacuation efficiency and alleviate the congestion at exits caused by abnormal data, which reduces evacuation efficiency. The results are expected to provide insights into improving evacuation systems’ efficiency and personnel safety.

A numerical simulation-based solution for enhancing the safety evacuation of the high-rise complex in geriatric hospital

Due to the vulnerability of the elderly population and the specificity of the treatment environment, geriatric hospitals face more serious fire safety issues than ordinary medical buildings. Existing conclusions are mostly one-sided as researchers have mainly ignored the diversity of geriatric patients and the types of functional rooms in the buildings. To make the findings in this field completer and more adequate, a geriatric hospital high-rise was employed as a case study and simulated in Pathfinder software. This model evaluated the efficacy of hierarchical organization of evacuating crowds, modification of vertical functional zoning, and specific congestion mitigation measures. The synthesized outcomes from these evaluations informed the development of an integrated evacuation strategy. Output parameters were used in place of control variables to verify repeatability. Crowd prioritization improve evacuation efficiency by optimizing the evacuation sequence and reducing the crossing of people; vertical functional zoning modification avoid congestion by locating high-risk departments near the bottom, for example; and blockage point evacuation enhance evacuation speed by adjusting the location of exits and improving the design of staircases. This comprehensive strategy successfully reduces evacuation time by 28.8 % and can be used as a guideline for a generic evacuation optimization system for similar buildings.

Flexible optimal bus‐schedule bridging for metro operation‐interruption

AbstractUnder the sudden interruption of the metro, it is significant to dispatch emergency bridging buses to evacuate stranded passengers to improve linkage management and service reliability. Aiming at the problem of emergency bridging bus scheduling, considering the remaining capacity of conventional buses, passenger tolerance, and site convenience, a flexible combined emergency bridging bus scheduling model is constructed based on the constraints of vehicle capacity, dispatching capacity, and maximum evacuation times of emergency bridging bus, aiming at minimizing the maximum evacuation time and passenger delay. The improved Harris hawk algorithm is used to solve the model, and the evacuation plan of the demand‐responsive and station‐station bridging lines is obtained. The maximum evacuation time is 41 min, and the average delay of stranded passengers is 19 min. The results show that the maximum evacuation time is 10% and 27% less than the fixed combined and single scheduling. The average delay is 16% and 42% less than the fixed combination and traditional single scheduling. The sensitivity analysis of the influencing factors of emergency bridging bus scheduling is conducted. The results show that the flexible combined emergency bridging bus scheduling model constructed in this paper can improve evacuation efficiency and reduce passenger travel delays.

Modeling and simulation of fire evacuation considering guiding factors: a case study of Shenyang subway interchange station

In case of fire, effective evacuation guidance can reduce evacuees’ hesitation time and improve evacuation efficiency. In this paper, an improved model of fire evacuation (guided fire cellular automata, GF-CA) is provided. First, the parameters of the improved model are quantified and analyzed to determine the parameter calculation method and variation law. Second, a typical subway interchange station in Shenyang is used as an example for evacuation simulation. Then, a lightweight building information modeling (BIM) is constructed and applied to the example combining the evacuation simulation software of Pathfinder and virtual reality technology for personnel evacuation simulation. Three types of simulation scenarios are examined, including simulation without guiding factors, simulation with guiding factors based on the improved model, and simulation based on the virtual reality experiment with guiding factors. The results show that the evacuation efficiency is increased by 75.63% after effective guidelines through personnel diversion and reasonable exit allocation, noting that the path results of the model are highly similar to those of the virtual reality experiment, which has good validity and rationality. Meanwhile, we analyze the guiding scheme and propose that we should pay attention to the position of the sign and the configuration of the conductor, and flexibly use the composite scheme, which provides effective technical support for improving evacuation safety and evacuation efficiency.

On group evacuation behavior of subway station halls using an improved evolutionary game model

This study is dedicated to examining the impact of stress on group evacuation behaviors in urban subway station fires, with the ultimate goal of improving evacuation efficiency and enhancing public safety. The authors have introduced an advanced evolutionary game model that combines “selfishness coefficient” and “stress threshold.” This introduces the concept of stress threshold and contributes to the exploration of stress propagation within groups. The simulation results indicate that the degree of selfishness in a crowd significantly affects evacuation efficiency. The emergence and propagation of stress emotions fundamentally represent a large-scale shift in individual evacuation behavior strategies within a group. At the onset of evacuation, the majority of the crowd follows an orderly and organized evacuation process, which occurs before the emergence of group stress. When the overall system benefit falls below the stress threshold, evacuees choose to crowd due to negative emotions and their pre-existing behavior strategies, leading the entire system into a “stress state”. Hence, effective strategies must simultaneously consider both physical methods and stress levels, lowering the threshold for orderly evacuation. For example, measures such as circular broadcasting and sending mobile messages can be employed to promptly inform evacuees about the on-site situation, and staff cab be deployed for guidance and comfort to improve people's sense of security. Analyzing evacuation patterns reveals factors influencing efficiency and individual behaviors, contributing insights to evacuation protocols and applications.

Modelling and accelerating the passenger evacuation process in a bi-modal system with bus and e-hailing modes after mass gathering events

After mass gathering events, passenger trip demand surges immediately within a small area. While organized bus bridging services would be usually offered in such circumstances to speed up passenger evacuation, many people prefer the more comfortable e-hailing service to leave the area. However, the use of e-hailing services for speedy passenger evacuation is a double-edged sword. On the one hand, e-hailing vehicles supplement to buses by offering more transportation supplies; on the other hand, the large inflows of e-hailing vehicles and the time-consuming passenger-driver (offline) searching process would lead to a huge vehicle accumulation within the area, thus lower vehicle speed and reduce bus evacuation efficiency. This paper proposes a mathematical model to depict the dynamic passenger evacuation rates and traffic conditions in a bi-modal system with both bus and e-hailing services for passenger evacuation. Numerical examples show that e-hailing service could significantly deteriorate the evacuation process when the arrival rates of e-hailing vehicles are high. Thus we further propose two feedback based perimeter control strategies that control the total inflow rates of e-hailing vehicles and background traffic, based on the vehicle accumulation and the number of parked vehicles within the area respectively. As shown by simulation experiments, both perimeter control strategies can effectively improve evacuation efficiency in the presence of e-hailing vehicles, especially when bus frequency is low, passenger demand is high, and/or the number of parking spaces is limited.

An efficient evacuation path optimization for passengers in subway stations under floods

The optimization of passenger evacuation paths in subway stations under flood scenarios plays an important role in improving evacuation efficiency and ensuring escape safety. Considering the impact of evacuation network failures such as gates on path planning, a two-stage passenger evacuation path optimization method under flood scenarios of subway stations is established with the objectives of minimizing total evacuation time, risk, and congestion. The black widow algorithm is proposed to optimize the BP neural network model to predict the travel time of passengers at nodes, which could improve the prediction accuracy of calculating the total evacuation time. The path optimization model is solved by the NSGA-II algorithm, and the optimal Pareto solution is determined based on the minimum total cost. Taking a subway station in Qingdao, China as an example, a passenger evacuation simulation system under flood scenarios is built using PathFinder software. The effect of the path optimization strategy is comprehensively evaluated through comparative experiments. It is found that the overall evacuation optimization degree could be increased by 18.75%, by comparing the evacuation time, congestion, and risk objectives under the situations with and without path optimization strategies.

Investigation and simulation of non-adaptive psychology of crowd evacuation under toxic gas leakage accident in chemical park

Chemical parks promote economic development, but accidents have caused casualties. Non-adaptive crowd psychology during evacuations can lead to panic, which can result in irrational behavior. Exploring ways to control panic and improve evacuation efficiency is crucial. In this paper, we conduct a questionnaire survey to grasp the psychological rules of evacuating people in the context of toxic gas leakage accidents in chemical parks, and define the weight of panic influence factor accordingly. Combined with a specific chemical park scenario, ALOHA software was used to simulate and analyze the consequences of toxic gas (liquid ammonia as an example) leakage, and the evacuation available time was obtained as 720s. The simulation experiments of personnel evacuation in the accident context were conducted by using Anylogic software based on the social force model, and the required evacuation time was 663.9s, 706.3s, 725.4s and 744.5s respectively under different panic influence factors. Drawing on the idea of building fire performance assessment, the safety criteria for evacuation under toxic gas leakage considering evacuation panic is proposed, namely RSET < ASET, to determine the safety status of personnel evacuation under different panic impact factors, which is hoped to provide some effective guidance for emergency evacuation in chemical parks.

Modified social force model considering emotional contagion for crowd evacuation simulation

Pedestrian behavior in emergency situations is an important part of emergency management, and crowd evacuation simulation provides a low-cost and low-risk method for investigating pedestrian evacuation dynamics by describing pedestrian evacuation behavior and predicting evacuation outcomes under hypothetical situations. This study proposed a modified social force model that combines dynamic hazard and emotional contagion frameworks to investigate the effect of panic on pedestrian evacuation dynamics under heterogeneous personality trait distributions. First, the emotional contagion framework was improved, and an updated emotional model was developed considering personality characteristics. Second, the individual self-driving force in the social force model was transformed from a physical drive mode to a psychological drive mode by introducing panic factors. Lastly, new rules for the spread of panic within small groups were formulated based on the important influence of small group behavior on evacuation. The simulation results confirmed the feasibility of analyzing crowd evacuation from the perspective of social psychology, and revealed that the spread of panic was closely related to the distribution of personality characteristics, individual walking speed, exit usage, evacuation efficiency, and small groups. In addition, the model could reproduce self-organizing behavior in crowd evacuation, such as fast is slow and herd behavior. Further, the results revealed that moderate panic can help improve evacuation efficiency, whereas excessive panic can result in uneven utilization of exits. In addition, the presence of small groups in panic crowds increased the crowd evacuation time.

Evacuation Simulation Implemented by ABM-BIM of Unity in Students’ Dormitory Based on Delay Time

China’s university dormitories have high population densities, which can result in a large number of casualties because of crowding and stampedes during emergency evacuations. It is therefore important to plan properly for evacuations by mitigating the effect of choke points that create backlogs ahead of time. Accurate computer representations of the structure of a building and behavior of the evacuees are two important factors to obtain accurate evacuation time. In this paper, Agent-Based Modeling (ABM) and Building Information Modeling (BIM) are, respectively, implemented using the Unity platform to simulate the evacuation process. As a case study, the layout of a student dormitory building at Shanghai Normal University Xuhui District, Shanghai, China, is utilized along with the A* algorithm in Unity to explore the impact of evacuation speed and delays in creating choke points. Compared with previous research, the innovation of this study lies in: (1) using Unity software to make simulation of the physical environment both realistic and easy to implement, demonstrating Unity can be a well-developed platform to implement ABM-BIM research that focuses on crowd evacuation. (2) Using these simulations to evaluate different degrees of congestion caused by varying evacuation speeds, thus providing information about possible issues relating to evacuation efforts. Using the results, several recommended measures can be generated to help improve evacuation efficiency.

A Metaverse-Based Teaching Building Evacuation Training System With Deep Reinforcement Learning

With the development of IoT, virtual reality, cloud computing, and digital twin technologies, the advent of metaverse has attracted increasing world attention. Metaverse integrates and applies multiple emerging technologies to cloud education, smart health, digital government, and emergency evacuation. Evacuation systems are of great importance to ensure life safety. Due to panic, people in a building may not be able to make the right judgment to choose an optimal path to leave the building in case of an emergency event such as a fire. As a branch of machine learning, deep reinforcement learning (DRL) can model an evacuation scene, collect real-time information, such as crowd distribution and disaster location, find the optimal escape path with a path-planning algorithm, induce the movement state of the crowd through dynamic guidance signs, and improve the evacuation efficiency. In this article, we apply DRL technology to solve the efficient emergency evacuation problem with the help of metaverse and show a training system built upon metaverse that would enable evacuees to choose the most efficient route and leave the building in the least amount of time. The information collected by various sensors, such as video cameras and smoke detectors, can give a whole picture of the status of the building in a real-time manner. The collected data are processed by cloud servers in which a DRL model is trained to dynamically guide evacuees. Experiments in different simulation scenes demonstrate that the proposed method is superior to the traditional static guidance method in saving evacuation time. It can effectively avoid major crowding along the evacuation route and improve evacuation efficiency.

Resilience-based optimization model for emergency bus bridging and dispatching in response to metro operational disruptions.

Bus-bridging evacuation services can significantly enhance metro resilience during operational disruptions. A resilience-based optimization model was proposed to generate a bus bridging and dispatching plan. The objective of the model is to maximize the resilience index of evacuated passengers while meeting pre-established restrictions on operational indicators and resources. The proposed approach consists of three steps: representing an integrated network based on a hyper-network, generating candidate bus-bridging routes using the K-shortest paths algorithm, and solving the optimization model using a genetic algorithm to determine the optimal vehicle allocation among the candidate routes. The Nanjing metro network was used to demonstrate the proposed model. The results show that the average waiting time is the main reason for travel delays, especially in short-distance travel. Furthermore, the cycling strategy is beneficial for reducing the average travel delay and improving evacuation efficiency with limited vehicles. In particular, when resources are very limited, the vehicle cycling strategy may have significant advantages over fixed vehicles for servicing fixed lines. The proposed model could be widely used in emergency response to quickly and efficiently evacuate passengers.

Emergency shelter allocation planning technology for large-scale evacuation based on quantum genetic algorithm.

Shelter allocation is one of the most important measures in urban disaster prevention and mitigation planning. Meanwhile, it is essentially a comprehensive planning problem combining resource allocation and traffic routing. A reasonable allocation scheme can avoid congestion, improve evacuation efficiency, and reduce the casualty rate. Owing to the large region and large evacuation population demand, quickly solving the complex allocation problem is somewhat challenging, and thus, the optimal results are difficult to obtain with the increase of evacuation scale by traditional allocation methods. This article aims to establish a shelter allocation model for large-scale evacuation, which employs an improved quantum genetic algorithm (IQGA) based on spreading operation and considering the total evacuation distance, the capacity constraint of evacuation sites, and the dispersion of allocation results, and compare allocation schemes of the spreading model with those of models that consider different constraints. Results show that the allocation model with the spreading operation has better allocation results than that without the spreading operation. For the allocation model with spreading operation, the spreading model with different spreading speeds is more reasonable than that with the same spreading speed, and the allocation results are closer to the ideal results with the increase of constraints. In addition, according to the allocation results, the evacuation route map and the evacuation heat map are drawn to intuitively understand the distribution scheme of each shelter.

A simulation-based optimization method for emergency evacuation induced by gas pipeline leakage risk

Long-distance gas pipelines with large diameter and high pressure, on which a leakage induces gas diffusion or fire explosion, may result in major influence to nearby buildings and residents. Safety and rapid evacuation of potentially affected people is a top priority. To analyze the affected areas by disasters and improve evacuation efficiency of the affected areas, this study presents a simulation-based optimization method for emergency evacuation induced by gas leakage risk. First, the influence radii of different leakage accidents were calculated based on damage criteria and the evacuation radii around the accidents were determined considering the panic psychology. The number of evacuees and their spatial distribution were calculated. Secondly, an evacuation simulation model for affected communities based on the multi-agent system was established to analyze the evacuation process of residents. Finally, the optimal design method and strategies for community evacuation were proposed. Responsibility areas of organized evacuation service for community exits were determined. The results showed that the evacuation times of the two communities A and C were reduced by 10% and 24%, which indicates organized evacuation is more efficient than unorganized evacuation. The selection of community exits is more balanced. Its rationality of the proposed method was verified by the comparison of evacuation simulation.

Study on emergency evacuation in underground urban complexes.

With progressive urbanization and the development and utilization of urban underground space, underground urban complexes (UUCs) have been increasingly used. UUCs have brought much convenience to people's lives. However, due to their enclosed nature and complexity, it has been an urgent issue to avoid (or reduce) casualties and allow rapid and safe evacuation of people during an emergency. In this study, the evacuation simulation software Pathfinder was used. Based on the steering model, the variation of the total evacuation time and pedestrian flow at main exits with different simulated evacuation measures, congestion at key nodes and people's path selection were compared and analyzed. Then, the critical locations in the spatial layout of UUCs that were prone to evacuation bottleneck effects were focused on and determined. The evacuation effectiveness of UUCs in an emergency was studied to investigate the problems of emergency evacuation in UUCs. It is found that in UUCs, the bottleneck effects were likely to occur at stairway entrances and exits as well as supermarket checkout counters and caused severe congestion. These locations should be focused on during emergency evacuation. For key locations prone to evacuation bottlenecks, increasing the width of exits or setting up auxiliary evacuation channels could be an effective measure to improve evacuation efficiency. In addition, formulating rational evacuation rules can be a favorable measure for emergency evacuation. However, during the evacuation, the herd mentality in people has an uncertain (positive or negative) impact on evacuation effectiveness. Setting up diversion walls may improve evacuation efficiency and reduce congestion to a certain extent, while evacuation confusion and chaos are prone to occur after diversion. These findings in this study have significant implications for improving the emergency management of UUCs.

Simulation investigation on crowd evacuation strategies for helping vulnerable pedestrians at different stages of egress

In emergencies, evacuations move pedestrians to safety. Extensive research has investigated strategies that can improve evacuation efficiency of pedestrian crowds in various contexts. However, most of this research considers pedestrians as homogeneous agents and the question of how vulnerable pedestrians can be supported in evacuations has not been convincingly resolved. Here, we focus not only on pedestrians with the same characteristics but also on vulnerable pedestrians defined by moving at a lower speed or being located further away from exits. We investigate strategies for helping these vulnerable pedestrians in three evacuation stages: the pre-evacuation stage where vulnerable pedestrians can respond to incidents quickly, the response stage where vulnerable pedestrians can be preferentially assigned to exits in the case of limited exit resources, and the evacuation phase where we place an obstacle in front of the exit. Our simulation results indicate that the effectiveness of these strategies depends on crowd characteristics and contexts (e.g., crowd size and pedestrian initial distribution), and different strategies may have other consequences, such as making pedestrians experience a higher accumulated force or walk longer distances. We found that only in some cases machine learning methods including kernel naïve Bayes and Linear SVM can predict which strategy will work best based on the initial distribution of pedestrians, suggesting that at least sometimes it may be possible to select strategies based on observed conditions. Our work highlights the importance of context and heterogeneous populations when developing strategies for evacuations and may thus be helpful for crowd safety and evacuation management.

Research on the Influence of Narrow and Long Obstacles with Regular Configuration on Crowd Evacuation Efficiency Based on Tri-14 Model with an Example of Supermarket

Regular shelves configuration forms unique characteristics of internal obstacles in a supermarket. It is crucial to study the crowd evacuation affected by obstacles during accidents or disasters in supermarkets as assembly occupancies. Based on the Tri-14 model, this paper studied the influence of safety exit designs and shelves’ configuration on the crowd evacuation efficiency with different densities in a supermarket through parameters and images. The results mainly indicate that: (1) The evacuation distance of farthest grid (Dfg) is the key factor to determine the total evacuation time of a low-density crowd. (2) For a high-density crowd, the closer the proportion ratio of the number of evacuees choosing each exit is to that of designed strand numbers of crowd flow at each exit, the higher the evacuation efficiency and average utilization efficiency of exits get; the scattered arrangement of exits will not necessarily lead to improving evacuation efficiency. Shelves’ configuration could lead to the extension of Dfg, but the change may reduce evacuation time instead, especially when forming effective advanced-gathering zones. (3) Under appropriate conditions, the impact of shelves’ configuration on evacuation efficiency can be negligible. This study has certain guiding significance for obstacle configuration and architectural design in large public gathering places.

A multi-trip electric bus routing model considering equity during short-notice evacuations

Widespread use of electric vehicles (EVs) and electric buses (EBs) adds new challenges to short-notice evacuations. Studies modeling EV behavior suggest that the power grid in most cases cannot handle EV demand during the evacuation preparation stage, but ignore the potential efficiencies of coordinated EBs. To overcome this shortcoming, a generalized framework based on three multi-trip EB routing models is developed to a) estimate the feasibility of using EBs during short-notice evacuations by examining evacuation efficiency, b) analyze EB charging behavior considering evacuation efficiency and path assignment equity, and c) explore the economic advantages of using EBs by comparing EBs with conventional liquefied natural gas (LNG) buses in terms of efficiency and operational cost. A genetic algorithm with an improved recombination strategy (GA-IR) is developed to solve these models. Computational results show that GA-IR outperforms GA in terms of convergence speed and solution quality, as GA-IR takes into account the empty trip removal process and the charging station re-insertion process, which is an innovation of GA-IR. Sensitivity analysis shows that using k-means clustering to position charging stations, and increasing the number of charging stations and EBs can improve evacuation efficiency.