Search Results (67)

This paper reviews the variability of passenger service time (PST) at the platform–train interface (PTI), a critical performance indicator in metro systems shaped by the infrastructure design, affecting passenger behavior and accessibility. Despite its operational importance, PST remains underexplored in relation to crowd management strategies. This review synthesizes findings from empirical and experimental research to clarify the main factors influencing PST and their implications for platform-level interventions. Key contributors to PST variability include door width, gap dimensions, crowd density, and user characteristics such as mobility impairments. Design elements—such as platform edge doors, yellow safety lines, and vertical handrails—affect flow efficiency and spatial dynamics during boarding and alighting. Advanced tracking and simulation tools (e.g., PeTrack and YOLO-based systems) are identified as essential for evaluating pedestrian behavior and supporting Level of Service (LOS) analysis. To complement traditional LOS metrics, the paper introduces Level of Interaction (LOI) and a multidimensional LOS framework that captures spatial conflicts and user interaction zones. Control strategies such as platform signage, seating arrangements, and visual cues are also reviewed, with experimental evidence showing that targeted design interventions can reduce PST by up to 35%. The review highlights a persistent gap between academic knowledge and practical implementation. It calls for greater integration of empirical evidence into policy, infrastructure standards, and operational contracts. Ultimately, it advocates for human-centered, data-informed approaches to PTI planning that enhance efficiency, inclusivity, and resilience in high-demand transit 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

The Hajj pilgrimage involves high crowd density within limited time and space, making traditional crowd control methods insufficient for real-time alerts or predictive safety measures. This research proposes a machine learning-based system to enhance crowd management by detecting abnormal behavior and forecasting future conditions. The study utilizes the Hajjv2 dataset, which consists of annotated video frames capturing various crowd behaviors across multiple Hajj locations. After data preprocessing and feature extraction, including crowd density, speed, direction, and object area, two models are employed: the Isolation Forest algorithm for anomaly detection and a Long Short-Term Memory (LSTM) neural network for forecasting crowd behavior. The system integrates the results of both models to issue real-time alerts based on predefined thresholds. Evaluation results indicate that the Isolation Forest model achieved an average accuracy of 91% across all test sets, effectively identifying abnormal movement patterns. The LSTM model produced reliable predictions of average crowd speed with a low Mean Squared Error (MSE) of 0.000439. Together, these models form a robust alert mechanism that enables early identification of risks. In summary, this study presents an intelligent, scalable solution for enhancing crowd safety during the Hajj. It illustrates the practical value of machine learning in enabling proactive and informed crowd management strategies. Full article

Large-scale crowd flow prediction is a critical task in urban management and public safety. However, achieving accurate and efficient prediction remains challenging. Most existing models overlook spatial heterogeneity, employing unified parameters to fit diverse crowd flow patterns across different spatial units, which limits their accuracy. Meanwhile, the massive spatial units significantly increase the computational cost, limiting model efficiency. To address these limitations, we propose a novel model for large-scale crowd flow prediction, namely the Stratified Compressive Sensing Network (SCS-Net). First, we develop a spatially stratified module that posterior adaptively extracts the underlying spatially stratified structure, effectively modeling spatial heterogeneity. Then, we develop compressive sensing modules to compress redundant information from massive spatial units and learn shared crowd flow patterns, enabling efficient prediction. Finally, we conduct experiments on a large-scale real-world dataset. The results demonstrate that SCS-Net outperforms deep learning baseline models by 35.25–139.2% in MAE and 26.3–112.4% in RMSE while reducing GFLOPs by 53–1067 times and shortening training time by 3.1–83.2 times compared to prevalent spatio-temporal prediction models. Moreover, the spatially stratified structure extracted by SCS-Net offers valuable interpretability for spatial heterogeneity in crowd flow patterns, providing deeper insights into urban functional layouts. Full article

Ensuring rapid and efficient evacuation in high-density environments, such as stadiums, is critical for public safety during fire emergencies. Traditional fire alarm systems rely on reactive detection mechanisms, often resulting in delayed response times, increased panic, and overcrowding. This study introduces an AI-driven predictive fire alarm and evacuation model that leverages machine learning algorithms and real-time environmental sensor data to anticipate fire hazards before ignition, improving emergency response efficiency. To detect early fire risk indicators, the system processes data from 62,630 sensor measurements across 15 ecological parameters, including temperature, humidity, total volatile organic compounds (TVOC), CO₂ levels, and particulate matter. A comparative analysis of six machine learning models—Logistic Regression, Support Vector Machines (SVM), Random Forest, and proposed EvacuNet—demonstrates that EvacuNet outperforms all other models, achieving an accuracy of 99.99%, precision of 1.00, recall of 1.00, and an AUC-ROC score close to 1.00. The predictive alarm system significantly reduces false alarm rates and enhances fire detection speed, allowing emergency responders to take preemptive action. Moreover, integrating AI-driven evacuation optimization minimizes bottlenecks and congestion, reduces evacuation times, and improves structured crowd movement. These findings underscore the necessity of intelligent fire detection systems in high-occupancy venues, demonstrating that AI-based predictive modeling can drastically improve fire response and evacuation efficiency. Future research should focus on integrating IoT-enabled emergency navigation, reinforcement learning algorithms, and real-time crowd management systems to further enhance predictive accuracy and minimize casualties. By adopting such advanced technologies, large-scale venues can significantly improve emergency preparedness, reduce evacuation delays, and enhance public safety. 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

With increasing incidents due to congestion at events, effective pedestrian guidance has become a critical safety concern. Recent research has explored the application of reinforcement learning to crowd simulation, where agents learn optimal actions through trial and error to maximize rewards based on environmental states. This study investigates the use of reinforcement learning and simulation techniques to mitigate pedestrian congestion through improved guidance systems. We employ the Multi-Agent Deep Deterministic Policy Gradient (MA-DDPG), a multi-agent reinforcement learning approach, and propose an enhanced method for learning the Q-function for actors within the MA-DDPG framework. Using the Mojiko Fireworks Festival dataset as a case study, we evaluated the effectiveness of our proposed method by comparing congestion levels with existing approaches. The results demonstrate that our method successfully reduces congestion, with agents exhibiting superior cooperation in managing crowd flow. This improvement in agent coordination suggests the potential for practical applications in real-world crowd management scenarios. Full article

This study systematically reviews the application of virtual reality (VR) in building evacuation scenarios in disaster contexts, highlighting its transformative potential to enhance preparedness, evacuation strategies, and safety training. Disasters such as fires, earthquakes, and multi-hazard emergencies pose significant challenges in densely populated urban environments, requiring innovative solutions beyond traditional methods. Analyzing 48 peer-reviewed studies (2014–2024) following PRISMA guidelines, this review focuses on VR applications in public buildings, transportation hubs, and high-risk workplaces, with VR simulations emerging as the predominant methodology. Key findings demonstrate VR’s ability to simulate realistic scenarios, improve spatial navigation, and optimize crowd dynamics and mobility accessibility. VR enhances evacuation efficiency and safety compliance by enabling adaptive training for diverse populations, including students, professionals, and vulnerable groups. In public and high-risk environments, VR addresses challenges such as visibility limitations, structural complexity, and the need for customized evacuation protocols. However, gaps remain in exploring multi-hazard environments and mixed-use spaces and ensuring scalability. Future research should integrate VR with artificial intelligence and machine learning for predictive and adaptive evacuation models. Expanding VR applications to underrepresented groups, including individuals with disabilities and the elderly, and collaborating with policymakers and urban planners are vital for translating research into practice. Overall, VR provides a scalable, adaptable, and inclusive solution for building evacuation preparedness, offering actionable insights to enhance resilience and safety in diverse architectural and disaster contexts. Its ability to transform evacuation strategies positions VR as a pivotal tool in advancing disaster management. Full article

Crowd management at turnstiles in metro stations is a critical task for ensuring safety, efficiency, and comfort for passengers. A methodology based on observation and microsimulation provides an advanced understanding and optimization of crowd flow through these turnstiles. The aim is to optimize crowd management and prevent overcrowding and delays at metro turnstiles through innovative solutions. The methodology is based on simulating passenger movements through turnstiles to observe and optimize crowd behavior. The results show that passenger decisions (e.g., choosing which turnstile to use, adjusting pace) are based on perceived crowd density, level of service, and usage of space. For instance, the number of turnstiles, their location, and the layout are important variables to be considered in the decision-making sequence. These decisions can be influenced by parameters like turnstile availability, walking paths, and real-time data (e.g., density of passengers). The methodology can help metro operators decide where to place additional turnstiles or adjust operational schedules. By simulating crowd behavior, operators can make informed decisions to reduce congestion and improve the efficiency of turnstile usage. This methodology could be implemented in various metro systems to optimize operations during different crowd conditions and peak times, ensuring smooth, safe, and efficient passenger flow. Full article

The spread of panic can swiftly trigger group behaviors, leading to public security incidents and significant social hazards. Increasing attention is being paid to the impact of human psychology and behavior on the evolution and management of emergencies. Drawing on the Weber–Fechner Law, we proposed an emotional contagion model to explore the dynamics of crowd panic during metro emergencies, focusing on the interplay of emotional levels and stimuli. Key influencing factors such as crowd density, personality traits, official interventions, and evacuation rates are analyzed. Additionally, a case study is conducted to validate the model’s effectiveness in quantifying emotions and characterizing the emotional contagion of crowd panic. Numerical results reveal that the initial intensity of panic stimuli significantly impacts peak panic levels, while contagion duration plays a minor role. Panic intensifies with increased crowd density, with sensitive individuals being more susceptible to extreme emotions, escalating negative contagion. Official intervention proves crucial in mitigating panic, though its effect is transient in enclosed environments. Evacuation rate minimally affects emotional contagion during the train’s motion but becomes pivotal post-arrival. Highly panicked passengers evacuate quickly, necessitating timely interventions to prevent secondary panic on platforms. This highlights the importance of immediate, effective control measures to manage panic dynamics and ensure public safety. Full article

With public safety receiving widespread attention from society, the question of how to effectively evacuate crowds has become a key issue. Leaders can provide pedestrians with clear and accurate route information and play an important role in daily crowd management and emergency safety evacuation. In this study, an evacuation model with static guidance considering the leader’s influence and the pedestrians’ decision-making behavior is proposed. The model is validated using experimental data, including evacuation behavior, evacuation time, and the percentage of the cumulative number of evacuees over time, and the simulation results match the experimental results well. Then, the model is applied to investigate the effect of different locations, numbers of static leaders, and different pedestrian distributions on evacuation efficiency in a room with unavailable exits. The results show that a leader located in the center of each potential exit can improve the overall evacuation efficiency, and the farther the guided pedestrian was from the correct exit, the better the overall evacuation performance of pedestrians. The distance parameter of multiple leaders is defined, and an optimal number of leaders exists in each specific scenario due to the overlap of leaders’ influencing areas. Furthermore, whether the pedestrians are uniformly or non-uniformly distributed, the evacuation time is shorter when the guided pedestrians are located farther from the correct exit. These findings can provide suggestions for crowd management and the arrangement of leaders in emergency evacuations. Full article

Effective monitoring of road conditions is crucial for ensuring safe and efficient transportation systems. By leveraging the power of crowd-sourced smartphone sensor data, road condition monitoring can be conducted in real-time, providing valuable insights for transportation planners, policymakers, and the general public. Previous studies have primarily focused on the use of pre-trained machine learning models and threshold-based methods for anomaly classification, which may not be suitable for real-world scenarios that require incremental detection and classification. As a result, there is a need for novel approaches that can adapt to changing data environments and perform effective classification without relying on pre-existing training data. This study introduces a novel, real-time road condition monitoring technique harnessing smartphone sensor data, addressing the limitations of pre-trained models that lack adaptability in dynamic environments. A hybrid anomaly detection method, combining unsupervised and supervised learning, is proposed to effectively manage concept drift, demonstrating a significant improvement in accuracy and robustness with a 96% success rate. The findings underscore the potential of incremental learning to enhance model responsiveness and efficiency in distinguishing various road anomalies, offering a promising direction for future transportation safety and resource optimization strategies. Full article

Background: In recent decades, the size and passenger capacity of cruise/passenger ships has been associated with noticeable growth; in turn, this has created significant concerns regarding the adequacy of existing evacuation protocols during an “abandon the ship” situation (life threatening emergency). This study provides a systematic overview of related weaknesses and challenges, identifying critical factors that influence evacuation efficiency, and also proposes innovative/interdisciplinary solutions to address those challenges. It further emphasizes the growing complexity of cruise/passenger ship evacuations due to increased vessel size/heavy density of human population, as well as identifying the necessity of addressing both technical and human-centered elements to enhance safety and efficiency of those specific operations. Methods: Guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) approach, a comprehensive systematic literature search was conducted across academic databases, including Scopus, Science Direct, Google Scholar, and a limited number of academic journals that are heavily maritime-focused in their mission. Emphasis was placed on peer-reviewed articles and certain gray studies exploring the impacts of ship design, human behavior, group dynamics, and environmental conditions on evacuation outcomes. This review prioritized research incorporating advanced simulation models, crowd management solutions (applied in various disciplines, such as stadiums, airports, malls, and ships), real-world case studies, and established practices aligned with contemporary maritime safety standards. Results: The key findings identify several critical factors influencing the overall evacuation efficiency, including ship heeling angles, staircase configurations, and passenger (physical) characteristics (with their mobility capabilities and related demographics clearly standing out, among others). This effort underscores the pivotal role of group dynamics, including the influence of group size, familiarity among the group, and leader-following behaviors, in shaping evacuation outcomes. Advanced technological solutions, such as dynamic wayfinding systems, real-time monitoring, and behavior-based simulation models, emerged as essential tools for optimizing an evacuation process. Innovative strategies to mitigate identified challenges, such as phased evacuations, optimized muster station placements, and tailor made/strategic passenger cabin allocations to reduce congestion during an evacuation and enhance the overall evacuation flow, are also highlighted. Conclusions: Protecting people facing a life-threatening situation requires timely preparations. The need for a holistic evacuation strategy that effectively integrates specific ship design considerations and human factors management, along with inputs related to advanced information technology-related solutions, is the best way forward. At the same time, the importance of real-time adaptive management systems and interdisciplinary approaches to address the challenges of modern cruise/passenger ship evacuations clearly stands out. These findings provide a robust foundation for future research and practical applications, contributing to advancements in maritime safety and the development of efficient evacuation protocols for large-in-size cruise/passenger vessels. Full article

This paper introduces a simulation model designed as a decision-making tool to assess and analyze various crowd management strategies with a focus on enhancing sustainability in airport operations. This model specifically addresses the challenges and risks associated with managing passenger flows within airport terminals. By simulating different scenarios, the model aims to provide valuable insights into how to effectively handle crowd dynamics and enhance overall terminal efficiency, safety, and sustainability. This case study was conducted at Henri Coanda International Airport, ARENA 12 simulation software being used in order to model the passenger flows within the airport terminal. Two scenarios were considered: The first one involves maintaining a fixed number of security and check-in desks for the two airline groups. In contrast, the second scenario allows for a variable number of security and check-in desks for the same airline groups. By optimizing resource allocation and minimizing waiting time, this model contributes to more sustainable airport management operations. Three measures of performance (MOPs) were selected to assess the system activity: the average passenger waiting time, the average passenger number queue length, and the average utilization rate. Comparing the results, we concluded that the second scenario shows a relative improvement in almost all performance measures when compared to the first scenario. Full article