Average Evacuation Time Research Articles

Maternal death before admission to the Sylvanus Olympia University Hospital Center (CHU SO) epidemiological and etiological aspects

Objective: determine the epidemiological and etiological profiles of a pregnant, parturient or women given birth before or within 10 minutes of admission Methods: This was a descriptive cross-sectional study from January 1, 2014, to December 31, 2021. All maternal deaths occurring before admission and within 10 minutes of admission to the clinic were included in the study. Of gynecology and obstetrics at CHU SO. The data were processed by Epi info version 7 software. Results: in total, 654 maternal deaths including 153 maternal deaths before admission were recorded, corresponding to 23.4% of all maternal deaths. The median age was 30.2 years. 37.2% of women were uneducated. 41.2% were resellers. 79.1% of women were cohabiting. 47.1% of women had performed less than 3 ANC. 43.8% of the women who died had completed their ANC in a medical center. 54.3% by a midwife, 37.3% by unqualified personnel. 62.7% of deaths occurred postpartum and 36.3% during pregnancy. 79.1% were referrals. 88.9% of the women who died arrived in a non-medical taxi car. Among the 57 patients who died during their pregnancy, 40.3% were carrying a pregnancy of 28 to 36 weeks, and 36.3% were full-term pregnancies. Among the 96 women who died postpartum; 93.3% had given birth vaginally. Among the 121 referrals, 34.7% came from a birthing center, 56.2% were referred by a state midwife and 30.6% by unqualified personnel; 46.3% referred without a reference form, 94.3% were referred without a venous access. In 10.7% the reason for evacuation was bleeding from the delivery with an average evacuation time of one-hour 5minutes. 60.3% of women who died had an evacuation delay of more than one hour. 94.8% of patients died of direct obstetric causes including immediate postpartum hemorrhage in 60.1% of cases. Conclusion: deaths before admission constitute an increasingly growing problem at CHU SO. Late referral is a determining factor in maternal deaths before admission.

Fire emergency management of large shopping malls: IoT-based evacuee tracking and dynamic path optimization

As urbanization accelerates and buildings become more complex, fire emergency evacuation has become increasingly challenging. Traditional evacuation plans often struggle with slow response times and suboptimal path planning in real-time dynamic and complex fire scenarios. To address these issues, this study proposes the IoT-based DWM-Evac model for fire emergency evacuation path planning. The model leverages IoT technology by using various sensors placed inside buildings to monitor fire incidents and spread in real-time, collecting critical data such as temperature, smoke concentration, and flame location. It integrates Dynamic Graph Neural Networks (DGNN), Whale Optimization Algorithm (WOA), and Markov Decision Process (MDP) to enhance path efficiency and safety. Experimental results indicate that the DWM-Evac model achieves an average evacuation time of 315 s in a virtual mall environment, 25 s shorter than traditional plans, with an average path length of 255 meters and a path safety score of 0.92, higher than the traditional plan’s 0.88. The application of IoT in fire emergency management not only improves response speed but also optimizes path planning, significantly enhancing personnel safety.

Quick-Response Model for Pre- and Post-Disaster Evacuation and Aid Distribution: The Case of the Tula River Flood Event

Background: In the context of humanitarian logistics, efficiently evacuating people from disaster-stricken areas is a complex challenge. This study focuses on the Tula River region in Hidalgo, Mexico, exploring the evacuation and support of individuals in temporary shelters. Despite the fact that the topic has been addressed in the literature, it is necessary to have quick response methods that can be used by decision-makers to adapt and utilize existing spaces as temporary shelters, in addition to knowing how to evacuate people. Methods: Addressing this void, a methodology to minimize evacuation and aid distribution costs is introduced. Leveraging existing algorithms, particularly Integer Linear Programming, the model determines shelter activation and utilizes the Vehicle Routing Problem to assess aid delivery strategies. Results: The research identifies optimal evacuation routes from 13 affected areas to 34 shelters and analyzes aid distribution costs under various demand scenarios: original, increased, and decreased by 10%, based on the number of transport units allocated and Google Maps distances. It also evaluates the costs associated with humanitarian aid distribution under varying collection strategies, involving state and municipal governments. Conclusion: This approach provides a decision-making foundation and can be adapted for similar analyses in other communities during extreme events.

Aeromedical Evacuation, the Expeditionary Medicine Learning Curve, andthe Peacetime Effect.

Organizational proficiency increases with experience, which is known as a learning curve. A theoretical peacetime effect occurs when knowledge and skills degrade during peacetime. In this study, the intertheater evacuation system was examined for evidence of a military learning curve and peacetime effect. Data on medical evacuations from U.S. Central Command occurring between January 1, 2003, and December 31, 2022, were acquired from the TRANSCOM Regulating and Command & Control Evacuation System. Priority mission evacuation time corresponding to peak periods of activity in Iraq and Afghanistan and minimal activity in Afghanistan was analyzed. Any reduction or increase in the delivery time of casualties would be considered a change in proficiency. There was a marginal monthly decline of 0.019 days (27.4 min) to perform a priority evacuation from Iraq (95% confidence interval [CI], 0.009 to 0.028 days, P < .001) and a decline of 0.010 days (14.4 min) from Afghanistan (95% CI, 0.003 to 0.016 days, P = .004) over 40 months from peak monthly average times. There was a monthly marginal increase in priority evacuation average time from Afghanistan of 0.008 days (11.5 min) (95% CI, 0.005 to 0.011, P < .001) between January 2013 and December 2020. The number of monthly evacuations estimated to maintain or improve monthly average evacuation time is approximately 50. An intertheater aeromedical evacuation system increased in proficiency during periods of conflict and declined during relative peacetime. There is evidence of a peacetime effect on intertheater aeromedical evacuation.

QUALITY OF EMERGENCY STAIR TRAVEL DEVICES

Emergency stair travel devices are designed to evacuate people from buildings and used duringinitial unguided spontaneous evacuations as well as by professional emergency services. Therequirements for evacuation chairs are varied: they are determined by the technical conditions ofthe building, by the specific way of operation of non-professional and professional operators, andon the other hand, they must take into account the specificity of evacuated people. The problemis to indicate the quality parameters of the evacuation chairs so that these chairs can be adaptedto the organization and equipment system. The article aims to indicate and justify such qualityparameters. The starting point was a quantitative analysis of scientific documents. It was foundthat there are only 33 scientific documents directly related to research on the evacuation of peoplewith physical disabilities. Based on the literature, the tested evacuated devices were classified intothree main types: hand-carried, tracked, and sled. Three groups of potential users of emergencystair travel devices have been identified: 1) non-professional rescuer, i.e., a person assisting inthe first spontaneous phase of evacuation; (2) professional rescuer, meaning a firefighter or otherprofessional who is physically fit and well versed in the operation of equipment and evacuationprocedures; this type of user usually appears in the second phase of the evacuation; (3) evacuee. Eachof these groups has specific needs and evaluates the quality of the device in different ways. Basedon the analysis of scientific literature, the following predictors of the quality of emergency stairtravel devices have been identified: average evacuation time(s), evacuation speed (m/s), numberof rescuers (pcs.), effort of rescuers (estimated oxygen consumption or pulse rate) and comfort ofthe person being rescued (estimated by pulse frequency or based on surveys). The expert analysispoints out that essential requirements for evacuation devices vary for different people and do notoverlap. For a person being evacuated, for example, the position of the body during evacuation isimportant, and from the point of view of rescuers, the ease of use, mobility and tolerance for errorsin use are important in case of evacuation. Both groups of rescuers have similar requirements, butthey have different priorities.

Incorporation of BIM-based probabilistic non-structural damage assessment into agent-based post-earthquake evacuation simulation

Earthquakes can cause severe damage to structural and non-structural elements of buildings; consequently, they pose high risks to human lives. To mitigate such risks, attention has been paid to enhancing the indoor environment for increased building safety. Yet little effort has been made to assess a building occupants' evacuation behaviors in response to damage to the indoor environment. This paper addresses this issue with a novel simulation framework that couples human behaviors with changes to the indoor building environment during post-earthquake evacuation. In particular, we present a building information modelling (BIM)-based prototype that simulates seismic damage to the non-structural indoor elements and visualizes its impacts on evacuation using a color-coded heat map. The simulated damage is then used as input to an agent-based model for post-earthquake evacuation. Using a probabilistic method to assess the non-structural elements' damage states, we are able to evaluate the impact of indoor damage on the evacuation process. We performed a trial of our prototype for a hypothetical earthquake in an educational building. The results revealed how the average evacuation time would increase as the earthquake intensity increases (from 38.6 s for the no-damage scenario to 122.9 for the highest-damage scenario). The proposed prototype has the potential to be joined with other tools, such as finite-element-based simulation, to incorporate structural analysis as well. Planners and designers can explicitly use our model's output to analyze the post-earthquake evacuation with the indoor non-structural damage to assess different building design geometries that increase the chances of a suitable evacuation process.

An agent-based model for cruise ship evacuation considering the presence of smart technologies on board

The cruise ship industry has faced in recent years a continuous increase in the number of passengers, reaching 28.5 million passengers in 2018, 60.11% more than in 2009. In this context, the research literature has acknowledged the key role played by the passengers' safety and has considered different aspects when dealing with the ship evacuation process related to both environmental properties and human behavioral aspects. Cruise ship companies have manifested a real interest in offering their passengers a safe travel experience and have continuously considered and adapted their evacuation plans to support a faster evacuation process. With the enhancement of technology, cruise ship companies can contribute more to increase safety during an emergency evacuation by providing smartphone applications that can assist each passenger involved in this process. As a result, the present paper aims to measure the impact made by the use of such an application on evacuation time by considering various weather conditions, ship conditions, and different times of the day. According to this purpose, an agent-based model is created for simulating the evacuation process of a ship deck which could accommodate up to 1200 people. The results indicate that using an application reduces the average evacuation time, with a 70% increase in usage leading to a 34.5% reduction in evacuation time. The main contribution of this work is to apply agent-based modelling to analyze the evacuation behavior on cruise ships when smart technologies are available, incorporating in the analysis elements and characteristics that have been previously considered in isolation. The model can be used as a support laboratory for policy advice: it can be applied for estimating the evacuation movement time under various conditions and for different levels of application adoption, as well as for assisting the decision-makers in designing the emergency plans and making the right decisions related to cruise ship evacuation.

Analysis of the Therapeutic Effect and Prognostic Factors of 126 Patients With Hypertensive Cerebral Hemorrhage Treated by Soft-Channel Minimally Invasive Puncture and Drainage.

BackgroundSurgery is the main method for the clinical treatment of hypertensive cerebral hemorrhage. Traditional craniotomy faces the disadvantages of the long operation time, easy to cause secondary injury to patients during the operation, and prone to infection after the operation, which is not conducive to the rehabilitation of patients. At present, it is urgent to find a surgical scheme, which can clear hematoma in time, protect brain tissue, and effectively reduce surgical trauma in the clinic.Materials and MethodsThe case database of our hospital was consulted, and the clinical data of patients with hypertensive intracerebral hemorrhage (HICH) treated with soft channel minimally invasive puncture and drainage from February 2018 to October 2021 were retrospectively analyzed. Patients were evaluated for efficacy, and the changes in serum C-reactive protein (CRP), tumor necrosis factor-α (TNF-α), interleukin-6 (IL-6), homocysteine (Hcy), endothelin (ET), and vasopressin (AVP) levels before surgery, 3 days after surgery, and 7 days after surgery were analyzed. Clinical data were collected and Logistic regression was used to analyze the prognostic factors.ResultsFinally, according to the inclusion and exclusion criteria, 126 patients were selected as the research object. Among them, there were 24 cases (19.05%) of recovery, 47 cases (37.30%) of markedly effective, 34 cases (26.98%) of effective, 11 cases (8.73%) of ineffective, and 10 cases (7.94%) of death. The total effective rate was 83.33%. The hematoma was basically removed in 116 cases (92.06%). The average evacuation time of hematoma was (7.82 ± 1.63) days. Post-operative intracranial infection occurred in 2 cases (1.59%) and post-operative rebleeding occurred in 5 cases (3.97%). The average hospital stay was (34.16 ± 16.59) days. Serum CRP, TNF-α, IL-6, Hcy, ET, and AVP levels of all patients on the third and seventh days after surgery were lower than those before surgery, and those on the seventh day after surgery were lower than those on the third day after surgery (p < 0.05). The differences in pre-operative Glasgow Coma Scale (GCS) score, bleeding volume, ventricular rupture, complicated cerebral hernia, and attack time to surgery between the good prognosis group and the bad prognosis group were statistically significant (p < 0.05). Pre-operative GCS score, bleeding volume, ventricular rupture, complicated cerebral hernia, and onset time to surgery were all independent factors that affect the prognosis of patients (p < 0.05).ConclusionSoft-channel minimally invasive puncture and drainage treatment of HICH has a significant effect, which is conducive to the complete removal of hematoma, reducing hospitalization time, while adjusting the balance and stability of various cytokines, and improving patient prognosis. Pre-operative GCS score, bleeding volume, rupture into the ventricle, complicated cerebral hernia, and time from onset to operation are all independent factors that affect the prognosis of patients.

Agent-based modeling for theme park evacuation

Each year theme parks can see up to 20 million patrons, but often little effort is put into planning for an emergency evacuation. In this study, we built a multiagent simulation model using AnyLogic® 8.5.1. The model was based on a preliminary design of a theme park provided by AOA Builds, Orlando. This research had two goals: the first was to compare evacuation time when the park is full (1) using only the main guest gate and (2) using all seven available exits. The second goal was to model first responder response time between various start and end locations within the park. Using only the main gate, evacuation took an average of 14 minutes and 51 seconds. Using all seven gates results in an average evacuation time of 11 minutes and 58 seconds. This was due to a gate being overwhelmed causing a delay in overall evacuation time. If that gate is not included in the calculation, the average evacuation time drops to 6 minutes and 44 seconds. For the purpose of measuring response times, four starting locations were chosen with the guidance of a subject matter expert. These locations included response teams positioned at the front gate, at a police station, at the service area behind a main attraction, and mobile patrol walking around the park. Based on our testing, walking around the park was the best option in terms of response time, using the main gate was 53.7 percent faster than other options and, using all seven gates, was 60.7 percent faster during an evacuation using all seven exits.

Effect of a Moving Obstacle on Pedestrian Flow Through an Exit

Current studies about moving obstacles mainly focus on uncommon evacuation scenarios, while there lacks researches on common egress scenarios, such as evacuation from an exit. This study aims to prove that pedestrian flow through exit can be improved by the presence of a moving obstacle and investigate the effect of a moving obstacle on regulating pedestrian flow. Unidirectional pedestrian flow simulations based on social force model are conducted to study the influence of a moving obstacle, that is a mobile robot, on the pedestrian flow through an exit. The robot reciprocates parallel to the wall of the exit with a constant speed 0.5m/s, and the gap between the robot and the exit is set to 1.0m. The pedestrians need to obey the rule of avoiding collision with the robot. By comparing the distributions of individual evacuation time with and without a moving obstacle, it is proven that that the average evacuation time can be reduced by a moving obstacle obviously. The moving obstacle can lead to the inhomogeneous distribution of the crowd near the exit by observing the density profiles. Furthermore, the crowd near the exit is classified into four groups according to movement direction (left or right) and position (the left or right part relative to the center of the exit) of the robot. It reveals that the moving obstacle impedes the evacuation of small proportion pedestrians, but promotes the evacuation of the large proportion pedestrians by the analysis on the fundamental diagrams of the four groups.

Large Event Halls Evacuation Using an Agent-Based Modeling Approach

The paper explores the usage of agent-based modeling in the context of large event halls evacuation during music festivals and cultural events. An agent-based model is created in NetLogo 6.2.2 for better representing the human behavior when involved in such situations. A series of characteristics have been set for the agents in order to preserve their heterogeneity in terms of speed, age, locomotion impairment, familiarity with the environment, evacuating with another person, choosing the closest exit or not, selecting the closest path to the exits. An “adapted cone exit” approach has been proposed in the paper in order to facilitate the guidance of the agents in the agent-based model to the closest exit and its advantages have been proved in comparison with the classical “cone exit” approach. Different evacuation scenarios have been simulated and analyzed for better observing the capabilities of evacuation modeling in the case of evacuation emergencies. Besides the overall evacuation time, an average evacuation time has been determined for the agents based on the individual evacuation time, which can be easily connected with a risk indicator associated to each situation. Due to the visual interface offered by the agent-based model, coupled with the evacuation indicators, the proposed model can allow the identification of the main factors that may contribute to a prolonged evacuation process (e.g. overcrowding at one of the exits, not choosing the appropriate door, evacuating with a friend/parent) and the potential measures to be considered for insuring a safe evacuation process.

Large-Scale Evacuation Shelter Selection Method Through Iterations of Pedestrian Simulations With Dynamic Congestion Reproduction

It is necessary to optimize evacuation guidance to shelters in short evacuation time. The state-of-the-art method based on an idea of combinatorial optimization problems related to evacuees&#x2019; locations and the capacities of nearby shelters has been developed, while it cannot mitigate the effect of congestion on roads/streets after evacuation starts. In this study, to cover this problem, we develop a new method that utilizes simulations for estimating the effect of congestion on roads/streets during evacuation and reassigning shelters to evacuees based on the simulation results. By iterating this step, our method derives the congestion-aware solutions for shelter selection that can realize more smooth evacuation. To evaluate our method, we conducted multi-agent simulations assuming a disaster situation in a sightseeing spot. Specifically, we examined a hypothetical case scenario involving the evacuation of 30,000 visitors from the Gion Festival. We compared the proposed method with conventional methods, such as the nearest shelter selection method and our previous method. We found that our proposed method reduced average and total evacuation time and congestion on roads compared to the conventional methods including the nearest shelter selection method and our previous method that only employs combinatorial optimization without estimating congestion. From this result, our idea of simulation-based congestion estimation has an impact of easing congestion during evacuation and preventing overcapacity of shelters at the same time. It shows the possibilities of help in developing congestion-aware evacuation strategies in emergency situations of crowded areas like huge cities or sightseeing spots.

Virtual Fire Evacuation Drills through a Web-Based Serious Game

Evacuation capacity is very important in building fire. In order to improve the safety evacuation capacity of occupants, a web-based serious game for virtual fire evacuation drills is proposed. As a prototype of the serious game, a stand-alone system for virtual drill had been developed. On this basis, the system framework of the serious game is first designed for web-based training, including the database, front and back ends. Secondly, an optimization solution including fire scenes and web codes is designed for smooth rendering performance. Lastly, a solution is designed to visualize the evacuation paths of numerous trainees, which can be used to reveal the evacuation rules, and an evaluation model of evacuation performance is created considering the features of evacuation paths and fire hazards, to provide comprehensive feedback for trainees. Thus, a convenient and accessible web-based serious game was developed. More than 100 people participated in the online virtual evacuation drill of a dormitory building fire. Through the drills, the average evacuation time of the trainees decreases from 79.77 s to 54.32 s, and the safety scores of the trainees improve from 74.71 to 81.21. Therefore, the evacuation abilities of trainees gradually improve, which demonstrates the effectiveness of the drill. Consequently, virtual fire drills using a web-based serious game can play an important role in improving the evacuation ability.

Optimizing the response for Arctic mass rescue events

We propose and study a model that optimizes the response to a mass rescue event in Arctic Alaska. The model contains dynamic logistics decisions for a large-scale maritime evacuation with the objectives of minimizing the impact of the event on the evacuees and the average evacuation time. Our proposed optimization model considers two interacting networks - the network that moves evacuees from the location of the event out of the Arctic (e.g., a large city in Alaska such as Anchorage) and the logistics network that moves relief materials to evacuees during the operations. We model the concept of deprivation costs by incorporating priority levels capturing the severeness of evacuees’ current medical situation and the period indicating the amount of time an evacuee has not received key relief resources. Our model is capable of understanding the best possible response given the current locations of response resources and is used to assess the effectiveness of an intuitive heuristic that mimics emergency response decision-making.

Multi-Dimensional evacuation risk evaluation in standard subway station

The performance-based approach using Required Safety Egress Time / Available Safety Egress Time concept is the most common method in fire safety evaluation. However, this one-dimension approach cannot fully evaluate the performance of a complex scenario with thousands of occupants in subway stations. Therefore, this paper develops a four-dimension parameter system that includes Required Safety Egress Time, Average Evacuation Time, Average Waiting Time, and Average Moving Distance to quantify the evacuation performance from four aspects. The convergence test approach is adopted to validate the accuracy of simulation results. A dimensionless parameter Risk Index (RI) is proposed to support multi-dimensional risk evaluation and comparison. One realistic standard subway station in Guangzhou metro system is modeled to testify the applicability of the proposed method. Totally twelve evacuation scenarios are designed and simulated, results indicate that: 1) Repeated simulations are necessary. Up to 629 simulation run times are required to meet the acceptance criterion of 0.01% for mean and 1% for standard deviation. 2) The Risk Index of Average Waiting Time is distinctly higher than other Risk Indexes. A long waiting time at bottlenecks increases the risk level in all scenarios. 3) The risk evaluation result of comprehensive RI is consistent with but not the same as the evaluation conclusion of RSET. The difference between RI and RSET shows that RI integrates more aspects of evacuation risk than RSET. The method proposed could support safety evaluation capacity improvement and standard revision in metro system.

Transport Management for Evacuation of Victims

The metropolitan area experiences disaster due to large population density, urbanization and heterogeneous demands at different time periods and places, like accidents, sudden vehicle breakdowns, congestions, lane blockages, earthquakes, flood and hurricanes. These disasters cause severe property damages, economic losses and losses of life. Therefore, in order to reduce the effects of disasters an effective evacuation management of victims from disaster zone is necessary. The evacuation during and after the disaster is difficult due to several unforeseen hurdles. We need a dynamic and adaptive transport management scheme to suite the requirements. In this paper, we propose Emergent Intelligence (EI) technique based transport management for evacuation of victims from disaster zone to safe zone. The EI technique makes static agent (resided at depot in a disaster zone) to logically divide disaster zones into multiple zones and sub-zones. Static agent creates and dispatches mobile agents to these zones and sub-zones, and EI technique makes group communication among mobile agents and static agent, during group communication they collect, analyze and share estimated victims density, resources availability, resources allocated, relevant routes availability, and boundary of disaster. The static agent at a depot estimates guaranteed routes, evacuation exit points, fraction of victims arrive at each exit points, average travel time to exit evacuees from disaster location to exit points. A case study is presented to test the proposed system transport allocation to different routes depending upon population of victims for evacuation management in a disaster zone. The proposed system reduces average delay propagation (i.e., 4 sec.) of disaster information compared to the traditional system (i.e., 30 sec.), minimizes the average evacuation time (i.e., at max. 10 minutes) required for evacuating victims from disaster zone to safe zone, and also uniformly distributes victim among evacuation exit points.

A hierarchical agent-based approach to simulate a dynamic decision-making process of evacuees using reinforcement learning

Simulation models are an undeniable tool to help researchers and designers forecast effects of definite policies regarding pedestrian social and collective movement behaviour. Considering both the environment's details and the complexity of human behaviour in choosing paths simultaneously is the main challenge in micro-simulation pedestrian dynamics models. This paper aims to present a novel comprehensive hierarchical agent-based simulation of pedestrian evacuation from a dynamic network of the environment using reinforcement learning, which is the closest to human behaviour among the other machine learning algorithms. In the approach, agents autonomously decide through a three-layer hierarchical model, including goal, node, and cell selection layers. A multinomial logit model is used to model the process of choosing the main movement direction at each time-step. The proposed model was successfully tested to simulate the pedestrian evacuation process from the Britomart Transport Centre platforms in Auckland during an abstract destructive event. Maximum evacuation flow, total evacuation time, average evacuation time, and average evacuation flow were investigated as dependent variables through different evacuation scenarios. The results from the approach can be used by designers and managers to optimise the quality of evacuation; also, the proposed model has the potential of becoming a potent tool for constructional management if coupled with other constructional tools.

Crawling evacuation from a room: experiment and modeling

In this paper, the impact of crawling on evacuation dynamics is investigated via experiments and modeling. Two types of experiments are implemented, in which pedestrians are asked to escape out of a virtual room with an exit either by crawling or walking. The experiments show that escaping by crawling will increase the average evacuation time. Moreover, it is found that the evacuation time gap of crawling is generally larger than that of walking. To model the evacuation dynamics, an improved heuristic-based model is proposed in which the pedestrians’ shape is represented by multiple connected circles rather than a single circle. Such representation can help the pedestrians keep a safer distance from others. Simulation results are in agreement with the experimental ones.

An investigation of fire evacuation performance in irregular underground commercial building affected by multiple parameters

Irregular underground commercial buildings (IUCB) are difficult to evacuate because of large space depth, complex form changes, and dense crowd. Fire evacuation performance of IUCB is of great practical importance with the development of underground buildings. To analyze the influence of different evacuation parameters on the properties of IUCB fire evacuation, this research gives person evacuation time under different strategies, action time of agent, and evacuation time based on IUCB fire evacuation numerical analysis model. A virtual reality system (VRS) including testing equipment and record platform is designed to perform model validation. Influence of three parameters are also analyzed on the performance of IUCB fire evacuation, including evacuation numbers, minimal group effect and pre-action time. Social force model and Generalized random graph model are applied to behaviors of evacuee. The results show that crowd congestion becomes a key factor determining evacuation time after the number of evacuees’ increases by 50.0%. The unbalanced use of exits reduces the overall evacuation rate. The average evacuation time, the overall evacuation time, and the total evacuation time of 95.0% population increase by 13.3%, 36.0%, and 15.9% respectively. When 50.0% of the evacuees are set as the minimal group effect, the overall evacuation time increased by the minimal group effect aggravates the congestion in some routes, but the minimal group effect could reduce the long tail time. Reducing the pre-action time can improve the overall evacuation rate in the early stage of evacuation. The basic linear relationship between pre-action time and evacuation time is presented. This research can provide the guidance for exit fire evacuation for IUCB to be built, which can also reduce the evacuation time in fire evacuation.

An Improved Simulation Model for Pedestrian Crowd Evacuation

This paper works on one of the most recent pedestrian crowd evacuation models—i.e., “a simulation model for pedestrian crowd evacuation based on various AI techniques”—which was developed in late 2019. This study adds a new feature to the developed model by proposing a new method and integrating it into the model. This method enables the developed model to find a more appropriate evacuation area design regarding safety due to selecting the best exit door location among many suggested locations. This method is completely dependent on the selected model’s output—i.e., the evacuation time for each individual within the evacuation process. The new method finds an average of the evacuees’ evacuation times of each exit door location; then, based on the average evacuation time, it decides which exit door location would be the best exit door to be used for evacuation by the evacuees. To validate the method, various designs for the evacuation area with various written scenarios were used. The results showed that the model with this new method could predict a proper exit door location among many suggested locations. Lastly, from the results of this research using the integration of this newly proposed method, a new capability for the selected model in terms of safety allowed the right decision in selecting the finest design for the evacuation area among other designs.