Search Results (7)

Pedestrian panic behavior is a primary cause of overcrowding and stampede accidents in public micro-road network areas with high pedestrian density. However, reliably detecting such behaviors remains challenging due to their inherent complexity, variability, and stochastic nature. Current detection models often rely on single-modality features, which limits their effectiveness in complex and dynamic crowd scenarios. To overcome these limitations, this study proposes a contour-driven multimodal framework that first employs a CNN (CDNet) to estimate density maps and, by analyzing steep contour gradients, automatically delineates a candidate panic zone. Within these potential panic zones, pedestrian trajectories are analyzed through LSTM networks to capture irregular movements, such as counterflow and nonlinear wandering behaviors. Concurrently, semantic recognition based on Transformer models is utilized to identify verbal distress cues extracted through Baidu AI’s real-time speech-to-text conversion. The three embeddings are fused through a lightweight attention-enhanced MLP, enabling end-to-end inference at 40 FPS on a single GPU. To evaluate branch robustness under streaming conditions, the UCF Crowd dataset (150 videos without panic labels) is processed frame-by-frame at 25 FPS solely for density assessment, whereas full panic detection is validated on 30 real Itaewon-Stampede videos and 160 SUMO/Unity simulated emergencies that include explicit panic annotations. The proposed system achieves 91.7% accuracy and 88.2% F1 on the Itaewon set, outperforming all single- or dual-modality baselines and offering a deployable solution for proactive crowd safety monitoring in transport hubs, festivals, and other high-risk venues. Full article

Due to the complexity of the architectural structure within comprehensive teaching buildings and the diversity of the student population, these buildings face more intricate fire safety challenges than ordinary teaching buildings. Current research primarily focuses on the analysis of single-building structures or individual factors, lacking an examination of the mechanism of multiple factors on emergency evacuation. Therefore, this study takes a comprehensive teaching building with a complex structure as a case study and, considering the behavioral characteristics of university personnel, conducts simulations using Pathfinder software. The model evaluates the effectiveness of pedestrian flow, vertical personnel distribution, horizontal functional zoning, priority ranking adjustments, and combination strategies on evacuation, as well as the impact of psychological factors on evacuation efficiency, providing a comparative analysis of the influence of each factor on evacuation. The results indicate that controlling the number of people in classrooms at the same time to reduce pedestrian flow can effectively shorten evacuation time, improving evacuation efficiency by 17.63%. The reasonable optimization of functional zoning and priority ranking can also effectively reduce evacuation time. In cases where there is high personnel density on upper floors and the teaching building’s functional zoning is unreasonable, the optimization effect of combination strategies is particularly significant, improving evacuation efficiency by 23.94%. Under panic conditions, leaders can effectively improve evacuation efficiency, and their role becomes increasingly significant as the level of panic rises. By considering the impact of various factors on evacuation, this research aims to enhance the evacuation efficiency of teaching buildings. The findings provide a scientific basis for emergency evacuation in complex buildings like teaching facilities. Full article

In public places, pedestrian panic behaviors have received increasing attention due to their dangerous impact on normal pedestrian flow. To address this issue, this study considered crowd panic behaviors as two-dimensional Gaussian disturbances quantitatively triggered by accidents and analyzed the stability of the pedestrian crowd based on Lyapunov criterion. First, this study established a two-dimensional static model for the disturbance pressure in a crowd. Then, a dynamic disturbance–propagation model (DPM) of crowd panic behaviors was proposed based on the conservation law of fluid dynamics. The anisotropy of the disturbance pressure propagation was proven with theoretical derivations and simulation experiments, which kept consistent with ground truth. Further, a stability criterion was proposed for pedestrian crowd flow under disturbances based on Lyapunov theory. To validate the proposed DPM, we simulated a disturbance scenario in the waiting hall of Shanghai Hongqiao Railway Station. Subsequently, the visual disturbance propagation dynamics and crowd state evolution due to a panic behavior disturbance in a pedestrian crowd were investigated; Finally, the experimental results demonstrated that disturbance pressures and pedestrian density fluctuated and diffused with the panic behavior outbreak point as the disturbance center, showing heterogeneous characteristics. This study shows how we can locate the high-risk areas affected by pedestrian panic behaviors in advance, and further help control crowd flow to keep a pedestrian crowd safe in public buildings. Full article

The dynamics of pedestrian crowds during exceptional tragic events are very complex depending on a series of human behaviors resulting from combinations of basic interaction principles and self-organization. The Alert–Panic–Control (APC) model is one of the mathematical models in the literature for representing such complicated processes, mainly focusing on psychologists’ points of view (i.e., emotion contagion). This work proposes a Hybrid APC (HAPC) model including new processes, such as the effect of resonance, the victims caused by people in state of panic, new interactions between populations based on imitation and emotional contagion phenomena and the ability to simulate multiple disaster situations. Results from simulated scenarios showed that in the first 5 min 54.45% of population move towards a state of alert, 13.82% enter the control state and 31.73% pass to the state of panic, highlighting that individuals respond to a terrible incident very quickly, right away after it occurs. Full article

When considering the concept of distributed intelligent control, three types of components can be defined: (i) fuzzy sensors which provide a representation of measurements as fuzzy subsets, (ii) fuzzy actuators which can operate in the real world based on the fuzzy subsets they receive, and, (iii) the fuzzy components of the inference. As a result, these elements generate new fuzzy subsets from the fuzzy elements that were previously used. The purpose of this article is to define the elements of an interoperable technology Fuzzy Applied Cell Control-soft computing language for the development of fuzzy components with distributed intelligence implemented on the DSP target. The cells in the network are configured using the operations of symbolic fusion, symbolic inference and fuzzy–real symbolic transformation, which are based on the concepts of fuzzy meaning and fuzzy description. The two applications presented in the article, Agent-based modeling and fuzzy logic for simulating pedestrian crowds in panic decision-making situations and Fuzzy controller for mobile robot, are both timely. The increasing occurrence of panic moments during mass events prompted the investigation of the impact of panic on crowd dynamics and the simulation of pedestrian flows in panic situations. Based on the research presented in the article, we propose a Fuzzy controller-based system for determining pedestrian flows and calculating the shortest evacuation distance in panic situations. Fuzzy logic, one of the representation techniques in artificial intelligence, is a well-known method in soft computing that allows the treatment of strong constraints caused by the inaccuracy of the data obtained from the robot’s sensors. Based on this motivation, the second application proposed in the article creates an intelligent control technique based on Fuzzy Logic Control (FLC), a feature of intelligent control systems that can be used as an alternative to traditional control techniques for mobile robots. This method allows you to simulate the experience of a human expert. The benefits of using a network of fuzzy components are not limited to those provided distributed systems. Fuzzy cells are simple to configure while also providing high-level functions such as mergers and decision-making processes. Full article

The door is a section prone to bottlenecks and is an important element in the study of pedestrian flow. Therefore, characteristics of doors (e.g., width, location, and the distance between doors) have been taken into consideration in the existing literature related to doors. According to several previous studies, it appears likely that the door opening process (DOP) influences pedestrian flow. However, the number of studies examining the DOP remains small. Therefore, to enhance understanding of pedestrian flow, we examined two door characteristics that could affect the DOP (opening direction (swing door: push or pull) and handle type (knob, lever, and panic bar)) and limited visibility. We conducted a walking experiment to take all variables (10 cases; 10 participants per case) into account. Statistical analysis was performed on the difference in movement times, and the results were as follows: (1) inclusion of the DOP affected pedestrian flow; (2) when visibility was limited, movement times with DOP inclusion increased significantly regardless of the door opening direction and handle type; (3) when the door opening direction was ‘push’, regardless of limited visibility and door handle type, movement times with DOP inclusion were significantly lower; and (4) the door handle type did not result in any significant difference in movement times with DOP inclusion. In addition, we calculated the delay time based on the experiment results, to include the DOP in pedestrian flow (push 1.96–2.88 s, pull 3.91–4.43 s; limited visibility: push 7.38–12.56 s, and pull 12.88–16.35 s). The results of this study could be used as basic data for the development of codes/regulations, engineering guidance, and egress models for doors. Full article

Most models designed to simulate pedestrian dynamical behavior are based on the assumption that human decision-making can be described using precise values. This study proposes a new pedestrian model that incorporates fuzzy logic theory into a multi-agent system to address cognitive behavior that introduces uncertainty and imprecision during decision-making. We present a concept of decision preferences to represent the intrinsic control factors of decision-making. To realize the different decision preferences of heterogeneous pedestrians, the Five-Factor (OCEAN) personality model is introduced to model the psychological characteristics of individuals. Then, a fuzzy logic-based approach is adopted for mapping the relationships between the personality traits and the decision preferences. Finally, we have developed an application using our model to simulate pedestrian dynamical behavior in several normal or non-panic scenarios, including a single-exit room, a hallway with obstacles, and a narrowing passage. The effectiveness of the proposed model is validated with a user study. The results show that the proposed model can generate more reasonable and heterogeneous behavior in the simulation and indicate that individual personality has a noticeable effect on pedestrian dynamical behavior. Full article