Evacuation Planning Research Articles

OCR concerned with building evacuation plan

Case name: Letter re: North Carolina Central University, No. 11‐23‐2058 (OCR 10/23/23).

Using human mobility data to detect evacuation patterns in hurricane Ian

ABSTRACT Hurricane Ian in 2022 was a destructive category 4 Atlantic hurricane striking the state of Florida, which caused hundreds of deaths and injuries, catastrophic property damage, and an economic loss of more than $112 billion. Before the landfall of Ian in Florida, the state government issued evacuation orders in high-risk zones to reduce casualties and injuries. However, there is limited data available to monitor the actual evacuation patterns and compliance with the evacuation orders at a large geographic scale. This study utilizes human mobility data (i.e. SafeGraph Weekly Pattern) to analyse the spatial patterns of evacuation during Hurricane Ian in 2022. The objectives of the study include three key aspects: 1) proposing an analytical workflow that utilizes human mobility data to detect mobility patterns in disasters and other emergency events; 2) identifying significant evacuation patterns, and 3) revealing the spatial variations in the compliance with evacuation orders in the affected areas. Using data science and spatial analysis techniques, this study detected notable changes in population movements, both within Florida and nationwide, which are potentially linked to the hurricane-induced population evacuation. The distance decay pattern of population flows from Florida demonstrates a propensity for individuals to relocate to nearby areas during the hurricane. Furthermore, the increase in population outflows from the impacted areas suggests the effectiveness of mandatory evacuation orders. A more pronounced increase in outflows from designated mandatory evacuation areas points to the public awareness of the evacuation zone designation. This study provides large-scale, fine-resolution analysis of evacuation behaviours in natural disasters which cannot be easily detected in traditional data sources. The analytical workflows provide actionable tools for government agencies and policymakers to evaluate the effectiveness of evacuation orders and improve evacuation plans in future disasters.

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.

OCR concerned with building evacuation plan

Case name: Letter re: North Carolina Central University, No. 11‐23‐2058 (OCR 10/23/23).

OCR concerned with building evacuation plan

Case name: Letter re: North Carolina Central University, No. 11‐23‐2058 (OCR 10/23/23).

The Continuous Time-Resource Trade-off Scheduling Problem with Time Windows

We introduce a variant of the cumulative scheduling problem (CuSP) characterized by continuous modes, time windows, and a criterion that involves safety margin maximization. The study of this variant is motivated by the Geospatial based Environment for Optimisation Systems Addressing Fire Emergencies Horizon 2020 Project, which is devoted to the design of evacuation plans in the face of natural disasters and more specifically, wildfire. People and goods have to be transferred from endangered places to safe places, and evacuation planning consists of scheduling evacuee moves along precomputed paths under arc capacities and deadlines. The resulting model is relevant in other contexts, such as project or industrial process scheduling. We consider here several formulations of the continuous time-resource trade-off scheduling problem (CTRTP-TW) with a safety maximization objective. We establish a complete complexity characterization distinguishing polynomial and NP-hard special cases depending on key parameters. We show that the problem with fixed sequencing (i.e., with predetermined overlap or precedence relations between activities) is convex. We then show that the preemptive variant is polynomial, and we propose lower and upper bounds based on this relaxation. A flow-based mixed-integer linear programming formulation is presented, from which a branch-and-cut exact method and an insertion heuristic are derived. An exact dedicated branch-and-bound algorithm is also designed. Extensive computational experiments are carried out to compare the different approaches on evacuation planning instances and on general CTRTP-TW instances. The experiments also show the interest of the continuous model compared with a previously proposed discrete approximation. History: Accepted by Andrea Lodi, Area Editor for Design & Analysis of Algorithms—Discrete. Funding: This work was funded by the Horizon 2020 Marie Skłodowska-Curie Research and Innovation Staff Exchange European Project 691161 GEO-SAFE (Geospatial based Environment for Optimisation Systems Addressing Fire Emergencie). This work has also been supported by ANITI, the Artificial and Natural Intelligence Toulouse Institute. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2022.0142 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2022.0142 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .

Foreign Experience Review on Engineering Protection of Seashores and Hillslopes

Introduction. During design, construction and operation of buildings and structures in the coastal areas there arises a serious problem of engineering protection of coasts and hillslopes from the dangerous geological processes, which include the coastal erosion, underflooding of territories, hillslope erosion, gravitational (slope) processes, such as intensification of landslides, etc. These processes often result in significant economic damage usually related to the non-recoverable loss of valuable coastal territories, deformation of buildings and structures, damage and destruction of the objects of transport and engineering infrastructure. In this regard, the study of the advanced foreign experience in engineering protection of the coastal areas and the evaluation of the possibility of it to be implemented in Russia is a relevant scientific and engineering task.Materials and methods. To prepare the review, the following data was used: field observation data with photographic evidences of the objects of engineering protection of seashores and hillslopes obtained by the authors during a business trip to the People's Republic of China (PRC) in October–November 2023; study and analysis of literature sources in the subject area, including the methods and technologies implemented in the Netherlands, Japan, the USA, Great Britain, Italy; summary and systematisation of the coastal protection methods for further development of their classification and evaluation of the possibility of implementing thereof in the Russian Federation.Research results. The main principles of design, the advanced methods and technologies of coastal protection used in China have been defined. They include: construction of the multi-level protective structures, such as the multiple-row breakwaters of special shape in combination with the stepped retaining walls, the vertical walls consisting of the driven piles, anchoring and spray-concrete covering of the slopes in combination with the metal meshes; terracing in combination with the retaining reinforced concrete (horizontal and vertical) crossbeams; the sloping wave-absorbing reinforced concrete surfaces; the meshy structures and fences combating the Aeolian processes, etc.The main methods used in the Netherlands, Japan, the USA and Italy for comprehensive protection of the large territories have been defined. Such as: the network of dams, weirs, wave breakers, coast paving, regulation of the flows by water locks and barriers, creation of the artificial protective islands; artificial replenishment with sand; creation of the green spaces along the coast; forming the dunes — natural or artificial hills of sand or pebbles located along the coast; creation of the concrete structures, stone jetties, floating constructions and even artificial reefs.The importance of management was emphasised, including the early warning systems for storms and tsunamis, weather and marine condition monitoring systems, as well as dissemination of the warning messages and evacuation plans among the population in the areas of potential threat.Discussion and conclusion. Based on the results of the review, the main conclusions were formulated. The recommendations were given on the possible ways of improving the coastal protection of the Caucasian Black Sea Coast and other coastal areas of the Russian Federation based on the advanced foreign experience.

Evaluation of Tsunami Evacuation Plans for an Underground Mall Using an Agent-Based Model

Tsunamis resulting from earthquakes can cause extensive damage, including infrastructure destruction, injuries, and fatalities. To address these risks, numerous tsunami evacuation simulations have been conducted to assess evacuation strategies and identify issues, particularly in Japan, where the Nankai Trough earthquake is anticipated, affecting underground malls in urban areas. Our previous research primarily focused on evacuating from underground to the surface, neglecting surface evacuation. Therefore, this study extended the previous study to include horizontal evacuation following the exit from the underground mall to ground level. It also considered the impact of pedestrian road crossings as interactions with vehicular traffic within the simulation to enhance realism and verify the evacuation risk. The study also explores scenarios where evacuation guide stairs are unavailable, comparing the effectiveness of alternative staircases in the evacuation plan. Results indicate that while using alternative staircases temporarily increases the number of evacuees reaching the surface, road crossings on evacuation routes become significant bottlenecks, leading to surface congestion and delayed evacuations. This raises doubts about the effectiveness of alternative staircases and highlights the need to consider alternative evacuation routes as a secondary plan. Additionally, the study reveals that reduced pedestrian flow at road crossings can significantly impact on evacuation times. Ultimately, this study demonstrated that achieving a short evacuation time from underground malls to the ground level might not always yield the best evacuation plan. It underscored the importance of considering aboveground factors in evacuation planning to ensure safety in the event of a tsunami caused by an earthquake.

Social resilient networks for improving animal evacuation in emergencies: rural/isolated community perspectives

Effective animal evacuation strategies are imperative for comprehensive emergency management in rural and isolated communities. Community resilience significantly influences the success of these strategies. This research examined the critical role of social resilience networks to improve evacuation of livestock and community resilience in rural and isolated communities. The study took a qualitative approach to studied community members and animal emergency management stakeholders during 3 focus group discussions and workshops. These workshops were conducted in Moorland, Whittingham and the MidCoast Council area of the Hunter Region in New South Wales. The research confirmed that communities with existing strong and cohesive networks and relationships often create a safe and supportive environment when emergencies arise and evacuation is required. The themes from community-led initiatives encompass social connections, communication management, preparedness, sensitisation and situational awareness. Establishing networks for mutual aid, involving the community in planning, promoting animal welfare and improving preparedness through education are recommended. These recommendations facilitate smooth animal evacuation and safeguard animals and the community. Creating an inclusive, participatory evacuation plan that takes into consideration community networks enhances emergency preparedness and evacuation.

Emergency Preparedness Among Community-Dwelling Older Adults: Risk and Protective Factors

ABSTRACT Recent increases in natural (e.g. extreme weather) and human-caused (e.g. industrial accidents) have raised concerns for public health and safety. Older adults face greater vulnerability and are often less prepared for these emergencies. Applying the risk and protective factor model, this study explored factors associated with emergency preparedness among older people living in the community. We analyzed cross-sectional secondary data obtained from the National Poll on Healthy Aging to compare factors associated with emergency preparedness among community-dwelling older people aged 60–69 (n = 383) and 70–80 (n = 370). Results showed that among all community-dwelling older adults, having previously discussed evacuation plans with family or friends was associated with a higher level of emergency preparedness. Among those aged 60–69, experience with an emergency in the past year was associated with a higher level of emergency preparedness, whereas living alone and having a Hispanic background were associated with a lower level of emergency preparedness. For those aged 70–80, higher perceived mental health was associated with a higher level of emergency preparedness. Implications focus on shifting the perspective of the disproportional risks for older adults around emergencies to one that values and supports older adults’ strengths and insights.

Landslide susceptibility evaluation in the Beas River Basin of North-Western Himalaya: A geospatial analysis employing the Analytical Hierarchy Process (AHP) method

In the North-Western Himalayas, particularly within the Beas Basin of Himachal Pradesh, landslide incidents are frequent, primarily due to the unique interplay of adverse geological conditions, heavy rainfall, and human factors. These incidents result in substantial loss of life and property each year. To mitigate such issues, systematic landslide research is essential, encompassing aspects like inventory mapping and risk assessment. This study leverages the Analytical Hierarchy Process (AHP) for an in-depth Landslide Susceptibility Index (LSI) mapping in the Beas River basin, employing remote sensing data to analyze key factors contributing to the region's instability. The process involved a detailed selection and mapping of landslide conditioning variables, guided by validated landslide inventory data and high-resolution remote sensing images, ensuring an accurate representation of the basin's geographical variations. The creation of the landslide susceptibility map utilized the weighted overlay approach, categorizing the area into five levels of susceptibility: very low, low, moderate, high, and very high. This classification incorporated ten critical factors influencing landslide occurrence, including elevation, slope aspect, slope angle, distance from drainage, lithology, distance from lineament, geomorphology, rainfall, and land use/land cover (LULC). The LSI was calculated through the Weighted Linear Combination (WLC) technique, leveraging the weights and ratings derived via the AHP method. This analysis revealed that approximately 634.1 square kilometers, or 12.8% of the region, face very high landslide susceptibility, followed by 22.6% at high, 25.8% with moderate, 24.4% at low, and 14.5% at very low susceptibility. The LSI map's accuracy in predicting landslides was affirmed through Receiver Operating Characteristic (ROC) and Area Under Curve (AUC) evaluations, showcasing an 86.3% precision rate. This classification facilitates focused interventions in high-risk areas, guiding planners in landslide-conscious development and infrastructure planning. It directs engineers toward engineering solutions like slope stabilization and drainage improvements to mitigate landslide effects. Moreover, this approach supports the creation of evacuation and emergency response plans, bolstering community resilience to landslide threats in the river basin.

A comprehensive survey of research towards AI-enabled unmanned aerial systems in pre-, active-, and post-wildfire management

Wildfires have emerged as one of the most destructive natural disasters worldwide, causing catastrophic losses. These losses have underscored the urgent need to improve public knowledge and advance existing techniques in wildfire management. Recently, the use of Artificial Intelligence (AI) in wildfires, propelled by the integration of Unmanned Aerial Vehicles (UAVs) and deep learning models, has created an unprecedented momentum to implement and develop more effective wildfire management. Although existing survey papers have explored learning-based approaches in wildfire, drone use in disaster management, and wildfire risk assessment, a comprehensive review emphasizing the application of AI-enabled UAV systems and investigating the role of learning-based methods throughout the overall workflow of multi-stage wildfire management, including pre-fire (e.g., vision-based vegetation fuel measurement), active-fire (e.g., fire growth modeling), and post-fire tasks (e.g., evacuation planning) is notably lacking. This survey synthesizes and integrates state-of-the-science reviews and research at the nexus of wildfire observations and modeling, AI, and UAVs — topics at the forefront of advances in wildfire management, elucidating the role of AI in performing monitoring and actuation tasks from pre-fire, through the active-fire stage, to post-fire management. To this aim, we provide an extensive analysis of the existing remote sensing systems with a particular focus on the UAV advancements, device specifications, and sensor technologies relevant to wildfire management. We also examine the pre-fire and post-fire management approaches, including fuel monitoring, prevention strategies, as well as evacuation planning, damage assessment, and operation strategies. Additionally, we review and summarize a wide range of computer vision techniques in active-fire management, with an emphasis on Machine Learning (ML), Reinforcement Learning (RL), and Deep Learning (DL) algorithms for wildfire classification, segmentation, detection, and monitoring tasks. Ultimately, we underscore the substantial advancement in wildfire modeling through the integration of cutting-edge AI techniques and UAV-based data, providing novel insights and enhanced predictive capabilities to understand dynamic wildfire behavior.

Nurses Taking on Readiness Measures (N-TORM): A nurse-facilitated household emergency preparedness intervention feasibility study.

This research evaluated the effect of a nurse-facilitated intervention on elderly or medically frail community members' level of household emergency preparedness as measured in knowledge, actions taken, and supplies gathered. These community members had access and functional needs that must be accommodated during disasters to mitigate their increased risk of injury, illness, and death because of the disaster. With adequate preparedness, it is plausible these community members may survive the aftermath of a disaster without needing assistance from disaster responders. This was a non-randomized, single group, before-after feasibility study (N = 31) conducted in a one-on-one session with a nurse interventionist in an urban community setting in the United States of America. We used the Household Emergency Preparedness Instrument to measure intervention effectiveness and a Participant Experience Survey to evaluate participant satisfaction with the intervention. The intervention included an educational booklet that provided instruction to participants on how to create a disaster-related evacuation and communication plan and identify community resources. Upon completion of the booklet, participants received a complimentary disaster supply kit. Mean general preparedness scores increased from 5.5 (SD = 4.1) pre-intervention to 20.2 (SD = 3.1) post-intervention (p < .001). Preparedness in all sub-scales also increased significantly (all ps < .001). Study findings provide support for the feasibility of the intervention to increase all measured aspects of emergency preparedness (knowledge, behaviors, and supplies) among elderly and medically frail adults with access and functional needs during disasters.

A scenario-based robust approach for joint planning of multi-blood product logistics and multi-casualty type evacuation

This paper presents a two-stage hybrid robust programming model that integrates multiple blood products scheduling and multiple casualty types evacuation under facility disruptions risk and casualty number uncertainty. The model seeks to maximize the rescue efficiency and minimize the total operations cost. Under a set of disruption scenarios, we adopt a scenario-based robust method to address disruption risks at temporary medical centres and two robust uncertainty sets to deal with uncertain casualty numbers. We propose a proximal bundle algorithm to solve large-scale instances of the proposed hybrid robust model approximately. Extensive numerical experiments show that: (i) the trade-off between model robustness and solution robustness can help the decision-makers determine an appropriate risk-aversion weight; (ii) compared with the corresponding stochastic and scenario-based robust models, the hybrid robust model can obtain more robust solutions with a slight increase in cost; (iii) the proximal bundle algorithm can produce near-optimal solutions within reasonable computational times; (iv) some model parameters have significant impact on the total cost, which can help decision-makers set the appropriate parameters to achieve the desired outcome. Finally, we also use the real data of the 2008 Wenchuan County Earthquake in Sichuan Province, China, to illustrate the application of the model.

Modeling and solving Passenger ship evacuation arrangement problem

In emergency scenarios on large passenger ships, ensuring the safety and efficient evacuation of a significant number of passengers is of utmost importance. Well-designed and preplanned evacuation arrangements are essential for a smooth and timely evacuation process. However, current research and evacuation models often overlook crucial planning considerations, including the allocation framework, counterflow prevention, and plan feasibility. To bridge these gaps, this paper presents a novel multi-criteria optimization model that addresses these overlooked aspects. A unique heuristic solution is proposed, utilizing a two-stage simulated annealing algorithm, to effectively solve this complex evacuation problem. When applied to a full-scale 14-deck cruise ship with 4977 passengers, the presented approach results in an optimized evacuation plan that markedly outperforms the original plan from the design materials, achieving reduced evacuation time and counterflow elimination. This research offers a practical tool for emergency management on large passenger ships, empowering ship designers and safety engineers to make informed, data-driven decisions.

Quick large-scale spatiotemporal flood inundation computation using integrated Encoder-Decoder LSTM with time distributed spatial output models

Accurate spatiotemporal flood simulations are essential for making informed decisions regarding flood release in affected regions, such as flood detention areas. Traditional spatiotemporal flood simulation approach uses partial differential equation (PDE) models (or physics-based models), which need high computational time. Although many machine learning (ML) models for inundation are increasingly being used to emulate the PDE models to address this issue, utilizing conventional ML models to achieve large-scale spatiotemporal flood prediction (i.e., simulation output in tens of thousands of grids and time steps over the whole flood event) remains a significant challenge. Therefore, we developed a new inundation model (IM) using encoder-decoder long short-term memory (ED-LSTM) with Time Distributed Spatial output model (ED-LSTM-TDS) that can acquire accurate spatially distributed flood information more rapidly. In the new IM framework, each ED-LSTM-TDS is built to simultaneously generate output for multiple (K) cell grids and multiple ED-LSTM-TDS models are built for prediction at all grids of entire PDE model. This study is the first of its kind to employ the ED-LSTM-TDS method to address spatiotemporal flood inundation simulation problems for flood detention areas. A 1994 km2 flood detention area in northeastern China was used as a case study. ED-LSTM-TDS exhibited better performance in predicting flood characteristics (e.g., water depth, velocity) than alternative methods, including ordinary LSTM, artificial neural network (ANN), and multiple linear regression (MLR). In addition, we investigated the trade-off relationship between the accuracy of flood characteristic prediction and the computation time of the proposed model by considering different numbers of K cell grids in each ED-LSTM-TDS model. The final proposed inundation model could accurately predict the spatiotemporal flood characteristics within 1.5 min to acquire the same information that required approximately 1 h by the PDE models. Such rapid and accurate prediction by the proposed model is important for evacuation planning, upstream flood control decisions and flood damage reduction.

CFD modeling for urban blast simulation

In this paper we present a Computational Fluid Dynamics (CFD) simulations for the case of natural gas explosion in urban area. Blast simulations aid in the development of effective mitigation strategies. By understanding how blast waves propagate through an urban environment, authorities can implement measures such as barriers, protective shields, strategies for firefighting strategy or evacuation plans to minimize damage and casualties. Accurate simulations provide insights into the potential impact of a blast on surrounding infrastructure and populations. This information is crucial for emergency response planning, helping authorities to allocate resources and respond effectively to minimize the consequences of an explosion.

Pedestrian evacuation planning under dam-break flood disaster considering road risk and road pedestrian demand

When facing disasters, a well-planned evacuation of people can significantly reduce casualties and disaster losses. Hurricane Katrina struck New Orleans in 2005, causing multiple dam failures and resulting in Lake Pontchartrain flooding back into the city. Taking this disaster as an example, this paper evaluates the risk of pedestrian evacuation on roads during dam-break flood disasters. The evaluation considers three aspects: road evacuation difficulty, disaster risk and road pedestrian demand. The fuzzy VIKOR model was used to evaluate 8 indicators for 50 candidate shelters. A dual-objective planning model was proposed to consider the time-varying and diverse emergency needs of disaster victims. By assessing the risk of pedestrian evacuation on the road and delineating restricted areas, the optimal path distance is calculated. Pareto optimality is then employed to achieve a trade-off between the convenience, cost, and applicability of the evacuation plan. In a complex post-disaster environment, the model can adapt flexibly to emergency rescue situations at various stages by adjusting the parameters in the objective and constraint equation. The Pareto optimal frontier can assist in determining the most suitable solution for the current scenario. In the case of New Orleans, 22 and 18 shelters were respectively selected from 34 candidate shelters for evacuation based on the simulated scenarios.

Cross-jurisdictional disaster preparedness: A Nigeria-USA data-analytical approach

Disasters pose significant challenges globally, demanding effective preparedness strategies to mitigate their impact. This study proposes a novel approach to disaster preparedness through cross-jurisdictional collaboration between Nigeria and the United States, leveraging data analytics for enhanced readiness. Nigeria, with its vulnerability to various disasters, and the United States, known for its advanced disaster management systems, offer a unique opportunity for mutual learning and collaboration. This research aims to bridge the gap in disaster preparedness between these two countries by employing a data-driven analytical framework. Through the collection, analysis, and comparison of disaster-related data from both nations, this approach seeks to identify commonalities, differences, and best practices in disaster preparedness strategies. By leveraging the strengths of both countries, such as Nigeria's community resilience and the USA's technological advancements, this approach aims to develop comprehensive disaster preparedness models tailored to the specific needs and contexts of each jurisdiction. Key components of this approach include data collection from various sources, including historical disaster records, socio-economic indicators, infrastructure data, and community resilience metrics. Advanced analytical techniques, such as machine learning algorithms and spatial analysis, will be employed to identify patterns, assess vulnerabilities, and predict potential disaster scenarios. The findings will inform the development of targeted preparedness strategies, including early warning systems, evacuation plans, resource allocation, and capacity building initiatives. Furthermore, this research emphasizes the importance of multi-stakeholder collaboration and knowledge exchange in disaster preparedness. By involving government agencies, non-governmental organizations, academic institutions, and local communities from both Nigeria and the USA, this approach aims to foster a holistic and inclusive preparedness framework. Ultimately, this study seeks to contribute to the global discourse on disaster risk reduction by demonstrating the effectiveness of cross-jurisdictional collaboration and data-driven approaches in enhancing disaster preparedness. By leveraging the strengths and experiences of Nigeria and the USA, this research aims to develop scalable and adaptable models that can be applied in diverse contexts to build resilience and mitigate the impact of disasters.

Fire-Spread Characteristics and Evacuation Plan Optimization of Old Style Multi-Story Student Apartments

Fire is a major disaster event that can have a significant effect on public safety and social development. In a college or university, fire can seriously threaten the safety, lives, and property of those there due to the compact layout of apartment buildings and high population density. The ecological safety and sustainable development of buildings are also affected. In this study, PyroSim and Pathfinder software (version 2019) were used to simulate and analyze fire-spreading characteristics based on a multi-story university student apartment building. Additionally, the most effective safe evacuation plan from four fire evacuation drill schemes was identified by analyzing and comparing their performance. Results show that the spreading of fire smoke on different floors is significantly affected by the roof structure and the vertical and horizontal diffusion characteristics of smoke. While the smoke layer at the evacuation stairways has little effect on a safe evacuation, poor visibility due to smoke and ceiling temperatures has a significant effect. Safe evacuation becomes progressively more difficult at different floor levels from the top to the bottom of the building. The optimal safety scheme involves orderly evacuation through two open emergency exits. The number of emergency exits has a significant impact on the evacuation effectiveness. Measures and suggestions have been proposed to deal with apartment fires that address pre-event prevention, emergency loss reduction during the event, and post-event report-back. These proposals form an important theoretical reference for emergency evacuation and student apartment fire safety, providing important guidance for ecological safety protection of buildings and sustainable development.