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A fall is an abnormal behavior that rarely occurs, however, once it occurs in a crowded scenario, it is prone to cause local disturbance, density-velocity fluctuations, and crowd instability even leading to a stampede accident. Currently, research on fall behavior mainly depends on single-point detection approach but lacks investigation on disturbing mechanism in a crowd flow. To address this issue, this paper first proposed a pressure dynamics model based on limb-level contact to analyze pedestrian behavioral characteristics. Then, based on the random Brownian motion, the disturbance distribution of fall behavior was determined theoretically. Using two parameters (a pressure coefficient and disturbance intensity), the pressure term of the fluid dynamics Aw-Rascle model was improved, and the internal disturbance propagation model (DPM) of fall behavior was established, as a primary contribution of this study. Further, to eliminate the disturbance propagation in a crowd, damping motion theory was employed. The disturbance elimination mechanism of abnormal behavior was discussed to suppress disturbance propagation caused by a pedestrian fall behavior. To validate the proposed model, both field experiment and numerical simulation were conducted respectively. The stability performance of the proposed DPM was evaluated with standard deviation (less than 0.1631) based on 50 repetitive experiments. Results showed that this study could help discover the disturbance propagation dynamics and elimination mechanisms of pedestrian fall behavior in a crowded scenario.

Pedestrian group detection is a challenging but significant issue in pedestrian flow control and public safety management. To address the issue that most conventional pedestrian grouping models (PGMs) can only identify a pedestrian group at a limited distance of less than 2 m, this study extended the pedestrian distance constraint of conventional PGMs with a reconstruction of the normal group detection criterion and development of a novel group detection criterion suitable for long-span space. To measure the movement behavior similarity with normal distance, five necessary constraints: velocity difference, moving direction offset, distance limitation, distance fluctuation, and group-keeping duration were studied quantitatively to form the criterion to detect normal groups. Meanwhile, a long-span group detection criterion was proposed with extended distance and direction consistency constraints. Therefore, this study proposed an improved PGM that considers long-span spaces (PGMLS). In the PGMLS workflow, the MMTrack algorithm was used to obtain pedestrian trajectories. A difference measurement method based on sequential pattern analysis (SPA) was adopted to analyze the velocity similarity of pedestrians. To validate the proposed grouping model, experiments based on pedestrian movement videos in the exit hall of the Shanghai Hongqiao International Airport were conducted. The results indicate that the proposed model can detect both normal and widely separated pedestrian groups, with a long span range of 2–12 m.

With the increasing number of video surveillance cameras in public buildings, it has become challenging, yet significant to detect abnormal pedestrian behaviors in crowd management, to prevent crowd accidents. Although current advancements in human action recognition based on computer vision can help detect abnormal behaviors after their incidence, majority of them lack the ability to detect potential characteristics prior to the occurrence of real abnormal behaviors. Hence, in this study, we addressed this issue by proposing a novel dynamic centroid model (DCM) of a human body, and rebuilding pedestrian joint sub-segments from human skeleton key nodes obtained in camera images. We built a weighted centroid-combined force model based on Newton’s second law, considering acceleration, mass inertial of human body sub-segments, and internal constraints. Thereafter, pedestrian kinematic and dynamic parameters were analyzed, such as speed, trajectory, force. Furthermore, abnormal behavior detection criteria were constructed for typical abnormal-behavior cases: U-turn and fall-down. Comparative experiments between the proposed DCM and the state-of-the-art methods were conducted. The experimental results showed that the model was capable of detecting abnormal behaviors, with mean values of lead time of 277 ms in U-turn behavior, and 562 ms in fall-down behavior, prior to the captured occurrence of these two abnormal behaviors. Finally, a de-occlusion algorithm was designed and jointly used with DCM, validated by a fall-down detecting experiment including partial occlusion. Therefore, this study holds significant value for the prevention of abnormal pedestrian behaviors in public places.

Understanding the effects of panic on crowd dynamics in emergency situations has long been considered necessary for pedestrian evacuation control. In the case of disasters, stampedes caused by panic behaviors occur with high possibility, and pedestrians are crushed or trampled, leading to enormous casualties. To eliminate the computational errors accumulated in the traditional macromodel, a macro-microconversion model based on the SF (social force) model and the AR (Aw-Rascle) model is proposed in this paper. The purpose is to use the crowd parameters of the microscopic model as the input part of the macroscopic model and to combine the advantages of the two models to ensure accuracy and improve calculation performance. The concept of the “pressure term” is defined to measure the panic level of the crowd. In addition, a flowchart of the numerical simulation is designed based on the road network conditions at the trampling site. To validate the conversion model, a numerical simulation is conducted in a case study of the Mecca Hajj stampede in 2015. The simulation results display the whole process of crowd marching and meeting with the dynamic variations of the “pressure term.” The simulation results are compared with the traditional simulation results based on a Gaussian distribution, which verifies that the simulation results obtained by the proposed method are closer to the real situation. Moreover, in this study, a new micromacro transformation method for crowd evaluation dynamics, which can enhance computing speed and execution efficiency, is provided.

To reduce the redundant knowledge discovered from the evolution of crowd evacuation in case of emergencies, this paper aims at knowledge reduction model of crowd evacuation stability evolution. First, the evolutionary mechanism of evacuation stability state is analysed from the perspective of knowledge engineering, and the characteristics of evacuation scenario elements such as physical properties of crowd, structure of evacuation scenario and psychological behaviour of evacuees are analysed and extracted by using self-organizing mapping network algorithm; Secondly, the evolutionary characteristics of evacuation scenario elements and evacuation stability state are discretized and mapped to conditional attributes and decision attributes in rough set universe respectively. Thereby a knowledge representation method of evacuation stability evolution mechanism based on rough set decision information table is constructed. Then, the improved attribute reduction algorithm based on discernibility matrix is used to reduce the knowledge of decision information table processed by self-organizing network algorithm. Finally, a knowledge reduction model of crowd evacuation stability based on rough sets is proposed. It is validated effective to achieve the decision-making of crowd stability, and which provides theoretical basis and methodological support for scientific guidance of emergency evacuation.