Search Results (50)

Human error in evacuation guidance systems can significantly affect evacuation performance, particularly in complex underground environments where large numbers of occupants are concentrated. While previous studies have focused on optimizing evacuation routes and modeling crowd dynamics, the direct quantitative impact of human error in evacuation guidance has not been sufficiently addressed. This study aims to evaluate the effects of human error on evacuation efficiency in underground stations using a node–link-based evacuation model. A virtual three-level underground station was modeled, and evacuation simulations were conducted using two representative pathfinding algorithms, Dijkstra and A*, to compare classical and heuristic routing approaches under both normal and error conditions. Three scenarios were considered: a normal condition with accurate guidance, a misguidance scenario with incorrect information on exit availability, and a delayed evacuation scenario in which a subset of evacuees started evacuation later than others. In addition, congestion effects were incorporated by adjusting walking speeds based on crowd density. The results show that human error significantly increases evacuation time and alters congestion patterns. Compared to the normal condition, the misguidance scenario increased evacuation time by approximately 17.6%, while the delayed evacuation scenario resulted in an increase of up to 37.9%, indicating that delayed response has the most critical impact due to the interaction between late-starting evacuees and existing congestion. Although the A* algorithm demonstrated higher computational efficiency, its advantage did not consistently translate into improved evacuation performance under dynamic conditions. These findings highlight that evacuation performance is highly sensitive to the accuracy and timing of evacuation guidance, rather than being determined solely by optimal pathfinding. Therefore, improving the reliability and timeliness of evacuation guidance systems is essential for enhancing safety in underground environments. Full article

Emergency evacuation in complex and dynamic building environments requires robust and adaptive routing strategies capable of responding to evolving hazards, blocked passages, and changing crowd behaviour. Most existing evacuation planners rely on static geometric representations and lack semantic awareness of the environment, limiting their ability to perform informed re-planning and backtracking when routes become unsafe. This paper proposes a neuro-symbolic evacuation planning framework that integrates Lifelong Planning A* (LPA*) with ontology-driven semantic reasoning and a Bidirectional Long Short-Term Memory (BiLSTM) prediction model. The building’s spatial and semantic knowledge is represented using the Web Ontology Language (OWL) and Resource Description Framework (RDF), enabling automated inference of implicit connections and enforcement of safety policies. The BiLSTM model learns temporal patterns from ontology-consistent evacuation trajectories and provides guidance for remaining-cost estimation and early prediction of routes likely to require backtracking, which is combined with a bounded semantic heuristic to preserve admissibility and optimality guarantees. Simulation results in a multi-floor academic building show that the proposed BiLSTM-guided semantic LPA* framework reduces average evacuation time by up to 9.6%, decreases node expansions by up to 32%, and increases evacuation success rates to 96.2% compared with a purely semantic baseline. The BiLSTM model also achieves strong predictive performance, with a test AUC of 0.92 for backtracking prediction and a next-state accuracy of 87.1%. The proposed framework is designed to support explainable, policy-compliant, and incrementally adaptable evacuation guidance under rapidly evolving emergency conditions. Full article

The emergency evacuation of visually impaired individuals during fire incidents presents critical challenges that require innovative technological solutions. While existing evacuation systems provide static route guidance, they fail to adapt dynamically to evolving fire conditions, blocked passages, or dangerous zones in buildings with multiple routes and exits. This paper presents a comprehensive implementation of a mobile application built with Flutter/Dart that addresses these limitations by enabling real-time, dynamic route computation based on live sensor data. The presented system operates in a decentralized manner, performing all critical computations on-device to ensure its functionality even when some parts of the building infrastructure fail. A dynamic route calculation modified Dijkstra’s algorithm was implemented on each user’s phone for guidance. If initial path adjustments are needed, they are computed from sensor data to evaluate fire evolution and other relevant factors, including the user’s current position and crowd congestion. An audio–visual interface was designed to provide navigation instructions and to help users follow safety routes efficiently. Field testing with visually impaired participants demonstrated significant improvements in evacuation efficiency, with shorter evacuation times than traditional static guidance approaches. The system architecture complies with international fire safety standards while maintaining user privacy through a no-tracking design philosophy. This work contributes to both theoretical advances in adaptive evacuation algorithms and practical insights for deploying assistive technologies in emergency scenarios. Full article

To address the limitations of conventional social force models in simulating high-density pedestrian crowds, this study proposes an enhanced model that incorporates visual perception constraints, group-type labeling, and collective avoidance mechanisms. Pedestrian trajectories were extracted from a bidirectional commercial street scenario using OpenCV, with YOLOv8 and DeepSORT employed for multiple object tracking. Analysis of pedestrian grouping patterns revealed that 52% of pedestrians walked in pairs, with distinct avoidance behaviors observed. The improved model integrates three key mechanisms: a restricted 120° forward visual field, group-type classification based on social relationships, and an exponentially formulated inter-group repulsive force. Simulation results in MATLAB R2023b demonstrate that the proposed model outperforms conventional approaches in multiple aspects: speed distribution (error < 8%); spatial density overlap (>85%); trajectory similarity (reduction of 32% in Dynamic Time Warping distance); and avoidance behavior accuracy (82% simulated vs. 85% measured). This model serves as a quantitative simulation tool and decision-making basis for the planning of pedestrian spaces, crowd organization management, and the optimization of emergency evacuation schemes in high-density pedestrian areas such as commercial streets and subway stations. Consequently, it contributes to enhancing pedestrian mobility efficiency and public safety, thereby supporting the development of a sustainable urban slow transportation system. Full article

This study developed a fire evacuation simulation model for a six-level underground station to evaluate evacuation efficiency under both dynamic and static conditions, including structural damage, smoke propagation, and real-time crowd congestion. Two representative pathfinding algorithms, Dijkstra’s and A*, were applied to analyze evacuation performance across eight fire scenarios occurring at different locations within the station. When only static factors were considered, both algorithms yielded identical maximum evacuation times, indicating comparable performance. However, the A* algorithm exhibited a significantly shorter computation time than Dijkstra’s, demonstrating higher operational efficiency. When dynamic variables such as real-time congestion and smoke-induced visibility reduction were introduced, the maximum evacuation times varied irregularly between the two algorithms. This outcome suggests that, under dynamic fire conditions, route guidance based solely on current information rather than predictive modeling may lead to suboptimal evacuation outcomes. Therefore, this study emphasizes the importance of establishing a predictive disaster management system capable of forecasting fire and smoke propagation, as well as a centralized control system that can dynamically distribute evacuees to enhance evacuation efficiency in deep underground stations. Full article

Pedestrian evacuation efficiency is paramount to public safety and sustainable urban resilience. This study utilizes an agent-based model simulating evacuation dynamics in a built environment to assess the impact of route familiarity, interpersonal interactions, and storage layout on evacuation efficiency. The model incorporates an evolutionary game theory framework to capture strategic decision-making, featuring both symmetric and asymmetric interactions among evacuees with varying levels of exit information (complete, partial, or none). Results show that familiarity with exit location is the most decisive element for evacuation, significantly outweighing the influence of crowd interactions, imitation behaviors, group composition, or storage layout. Furthermore, the crowd composition exerts a significant moderating effect, so that asymmetric group structures yield superior evacuation performance compared to symmetric ones. The optimal storage layout for evacuation is contingent upon the availability of exit information. An orderly layout is superior when information is known, whereas a random layout proves more effective in the absence of information by preventing misleading paths. Thus, providing clear information, adaptable spatial designs and consciously constructing a heterogeneous population structure are more critical for evacuation. This work provides actionable insights for architects and safety planners, contributing directly to the development of safer, more sustainable built environments and supporting Sustainable Development Goal (SDG) 11, particularly Target 11.5. Full article

With the rapid development of metro–commercial integration, ensuring the safety of building operations has become increasingly critical. This study proposes a comprehensive safety evaluation framework tailored to integrated metro–commercial complexes. The framework establishes a hierarchical indicator system encompassing risk management, human safety management, facility and equipment safety, intelligent information management, and integrated crowd and operational risk. By combining historical records, real-time sensor data, and management logs, secondary indicators are quantified and normalized, while a hybrid weighting method integrating expert judgment and statistical analysis ensures both theoretical validity and empirical robustness. A case study demonstrates the framework’s applicability, yielding an overall operational safety score of 0.601, which corresponds to a “Moderate” level. Detailed analysis identifies deficiencies in flood resilience, intelligent monitoring reliability, and crowd-related fire risks, underscoring the complexity of safety challenges in such facilities. Targeted optimization measures—including enhanced drainage redundancy, condition-based equipment maintenance, improved intelligent monitoring, evacuation corridor expansion, and catering fire safety upgrades—are shown to substantially improve the composite safety index and operational resilience. This study contributes a dynamic, data-driven, and interpretable evaluation methodology that not only supports scientific safety management in metro–commercial buildings but also provides a reference for broader applications in multifunctional urban infrastructure. Full article

Global optimization problems, ubiquitous scientific research, and engineering applications necessitate sophisticated algorithms adept at navigating intricate, high-dimensional search landscapes. The Escape (ESC) algorithm, inspired by the complex dynamics of crowd evacuation behavior—where individuals exhibit calm, herding, or panic responses—offers a compelling nature-inspired paradigm for addressing these challenges. While ESC demonstrates a strong intrinsic balance between exploration and exploitation, opportunities exist to enhance its inter-agent communication and search trajectory diversification. This paper introduces an advanced bio-inspired algorithm, termed Crisscross Escape Algorithm (CCESC), which strategically incorporates a Crisscross (CC) information exchange mechanism. This CC strategy, by promoting multi-directional interaction and information sharing among individuals irrespective of their behavioral group (calm, herding, panic), fosters a richer exploration of the solution space, helps to circumvent local optima, and accelerates convergence towards superior solutions. The CCESC’s performance is extensively validated on the demanding CEC2017 benchmark suites, alongside several standard engineering design problems, and compared against a comprehensive set of prominent metaheuristic algorithms. Experimental results consistently reveal CCESC’s superior or highly competitive performance across a wide array of benchmark functions. Furthermore, CCESC is effectively applied to a complex reservoir production optimization problem, demonstrating its capacity to achieve significantly improved Net Present Value (NPV) over other established methods. This successful application underscores CCESC’s robustness and efficacy as a powerful optimization tool for tackling multifaceted real-world problems, particularly in reservoir production optimization within complex sedimentary environments. Full article

Emergencies can occur unexpectedly and require immediate action, especially in aviation, where time pressure and uncertainty are high. This study focused on improving emergency evacuation in airport and aircraft scenarios using real-time decision-making support. A system based on the Asynchronous Advantage Actor–Critic (A3C) algorithm, an advanced deep reinforcement learning method, was developed to generate faster and more efficient evacuation routes compared to traditional models. The A3C model was tested in various scenarios, including different environmental conditions and numbers of agents, and its performance was compared with the Deep Q-Network (DQN) algorithm. The results showed that A3C achieved evacuations 43.86% faster on average and converged in fewer episodes (100 vs. 250 for DQN). In dynamic environments with moving threats, A3C also outperformed DQN in maintaining agent safety and adapting routes in real time. As the number of agents increased, A3C maintained high levels of efficiency and robustness. These findings demonstrate A3C’s strong potential to enhance evacuation planning through improved speed, adaptability, and scalability. The study concludes by highlighting the practical benefits of applying such models in real-world emergency response systems, including significantly faster evacuation times, real-time adaptability to evolving threats, and enhanced scalability for managing large crowds in high-density environments including airport terminals. The A3C-based model offers a cost-effective alternative to full-scale evacuation drills by enabling virtual scenario testing, supports proactive safety planning through predictive modeling, and contributes to the development of intelligent decision-support tools that improve coordination and reduce response time during emergencies. Full article

Architectural design seeks to address many challenges, one of which is creating buildings that can quickly and safely evacuate people. Therefore, it is even more important to pay attention to the safety of personnel evacuation. Past disasters have shown that the number of casualties in large sports stadiums can be as severe as those caused by plane crashes. This study uses a case study approach to analyze the evacuation of spectators in a 40,000-seat stadium, comparing the practical application of three performance verification methods. The results indicate that Simulex’s visual dynamic simulation effectively reflects how walking speeds decrease in crowded conditions and how bottlenecks form along evacuation routes. People tend to gather at corners, leading to congestion and uneven distribution of evacuees, with several escape staircases being underutilized. The Guide to Safety at Sports Grounds is suitable for the early planning stages of architectural design, while the “Verification Guideline of Buildings Evacuation Safety Performance-based Design” is better suited for the detailed design phase to ensure compliance with the safety standard of evacuating spectators within 8 min. Compared to planning and designing based solely on regulations or empirical verification formulas, using visualization software allows for effective adjustments to evacuation routes before finalizing the design, balancing crowd flow across all safety exits and improving evacuation efficiency during the operational phase. Full article

Existing studies on ontology evolution lack automated mechanisms to balance semantic coherence and adaptability under real-time uncertainties, particularly in resolving spatiotemporal asymmetry and multidimensional coupling imbalances in dam-break scenarios. Traditional methods such as WordNet’s tree symmetry and FrameNet’s frame symmetry fail to formalize dynamic adjustments through quantitative metrics, leading to path dependency and delayed responses. This study addresses this gap by introducing a novel symmetry-entropy-constrained matrix fusion algorithm, which integrates algebraic direct sum operations and Hadamard product with entropy-driven adaptive weighting. The original contribution lies in the symmetry entropy metric, which quantifies structural deviations during fusion to systematically balance semantic stability and adaptability. This work formalizes ontology evolution as a symmetry-driven optimization process. Experimental results demonstrate that shared concepts between ontologies (s = 3) reduce structural asymmetry by 25% compared to ontologies (s = 1), while case studies validate the algorithm’s ability to reconcile discrepancies between theoretical models and practical challenges in evacuation efficiency and crowd dynamics. This advancement promotes the evolution of traditional emergency management systems towards an adaptive intelligent form. Full article

Mass shootings result in significant casualties. Due to the complexity of buildings, capturing crowd dynamics during mass shooting incidents is particularly challenging. Therefore, it is necessary to study crowd dynamics and the key mechanisms of mass shooting incidents and explore optimal building design solutions to mitigate the damage caused by terrorist attacks and enhance urban safety. In this study, we focused on the Bataclan Shooting (13 November 2015) as the target case. We used an agent-based model (ABM) to model both the attacking force (shooting) and counterforce (anti-terrorism response). According to the real situation, the dynamic behavior of three types of agents (civilians, police, and shooters) during the shooting accident was modeled to explore the key mechanism of individual behavior. Taking civilian casualties, police deaths, and shooter deaths as the real target values, we obtained combinations for optimal solutions fitting the target values. Under the optimal solutions, we verified the effectiveness and robustness of the model. We also used artificial neural networks (ANNs) to detect the predictive stability of the ABM model’s parameters. In addition, we studied the counterfactual situation to explore the impact of police anti-terrorism strategies and building exits on public safety evacuation. The results show that for the real cases, the optimal anti-terrorism size was four police and the optimal response time was 40 ticks. For double-layer buildings, it was necessary to set exits on each floor, and the uniform distribution of exits was conducive to evacuation under emergencies. These findings can improve police patrol routes and the location of police stations and promote the creation of public safety structures, enhancing the urban emergency response capacity and the level of public safety governance. Full article

To address the significant problems of high fire risk and low evacuation efficiency in large and complex medical buildings, this study uses Ezhou Hospital as the empirical object to construct a multi-dimensional threat and risk assessment and fire evacuation dynamic coupling model and proposes a systematic optimization scheme to improve personnel evacuation safety. This study proposes an innovative full-chain analysis framework of “threat and risk assessment-dynamic coupling-multi-strategy optimization”. The specific methods employed include the following: (1) Using the probabilistic threat and risk assessment (PRA) method and the risk index (RII) method to identify the most unfavorable scenarios where the fire source is located in the outpatient hall (risk value C2 = 9.86). (2) Combining PyroSim and Pathfinder to construct a dynamic coupling model of fire smoke diffusion and personnel evacuation. Multiple groups, such as patients with mobility problems and rescue personnel, are added to address the limitations of traditional single-factor simulations. (3) Considering the failure of fire shutters, a two-stage optimization strategy is proposed for when the number of personnel is at its peak: the evacuation time is shortened by 23% by using internal intelligent guidance to shunt the congestion node crowd, and the addition of external fire ladders forms a multi-channel coordinated evacuation that further reduces the total evacuation time from 1780 s to 1266 s and improves the efficiency by 29%. The results show that the coupled multi-path coordination strategy and three-dimensional rescue facilities can significantly reduce the bottleneck associated with a single channel. This study provides a multi-dimensional dynamic evaluation framework and comprehensive optimization paradigm for the design of the evacuation of high-rise medical buildings and has important theoretical and technical reference values for improving the fire safety performance of public buildings and the intelligence of emergency management. Full article

Street-type spaces, characterized by their relative closedness and propensity for human congregation, inherently carry potential safety evacuation risks. In order to study the influence of slopes on the evacuation efficiency of pedestrians in street-type public spaces under the state of passenger flow surge during holidays, this study systematically analyzes the changing rules and behavioral characteristics of pedestrian evacuation in downhill movement through a three-phase analysis of the risk of crowd gathering in urban street-type spaces (before, during, and after) and evacuation simulation experiments combining variables such as slope, street width, obstacle layout, disability type, and group movement. The findings indicate that, in the structural design of street-type spaces, slopes of more than 4° should be minimized to maintain the smooth flow of pedestrians. Areas in streets with widths narrower than 2 m are high-risk zones for crowd gathering and should be better supervised. The number and location of obstacles should be reasonably arranged under the condition of satisfying the safety of pedestrians’ passage. The differences in the ability of evacuees should be taken into account to improve evacuation system deficiencies and ensure that everyone can evacuate safely. Ultimately, we propose a preventive mechanism for the safe evacuation of urban street-type public spaces to reduce the risk of crowd gathering and safeguard pedestrians. This study provides a theoretical framework for understanding the dynamics of pedestrian evacuation in inclined street-type spaces, thereby guiding urban planners and public safety managers to enhance the design and management of such spaces. Full article

To explore the influence of emergency evacuation signs on passengers’ behavior during subway fires and to enhance evacuation efficiency sustainably, this study proposes a dynamic emergency evacuation sign scheme. Utilizing a building information modeling (BIM) and virtual reality (VR) technology simulation platform, two schemes—current static signage and a novel dynamic signage system—are developed and evaluated. The research focuses on four scenarios combining varying crowd conditions (2:8 and 5:5) with signage types. Through experiments, we compare the performance of the current signage and the new dynamic signage in terms of evacuation efficiency and wayfinding difficulty. The results indicate that the dynamic identification system significantly improves evacuation efficiency, reduces incorrect route choices, and minimizes passenger confusion. Particularly in a complex scenario with a 2:8 crowd state, the dynamic signage effectively helps passengers avoid the negative impacts of group decision errors. Additionally, individual characteristics such as age, gender, spatial ability, and evacuation training experience significantly influence evacuation performance. By reducing risks, enhancing urban resilience, and optimizing evacuation processes, this study contributes to sustainable urban infrastructure safety. The findings provide a theoretical basis for designing sustainable emergency signage systems that address the social, economic, and environmental aspects of resilience in urban transportation. Full article