According to historical crowd accidents data, pedestrian panic behaviors have often triggered and further exacerbated crowd incidents, leading to severe casualties in transit hubs. To date, most existing panic behavior recognition models still fail to detect crowd panic quickly enough to guide timely pedestrian-control measures. To address this gap, we propose the pedestrian Panic Behavior Recognition Model (PBRM), which integrates audio-semantic signals, speech-derived panic keywords, 21-keypoint kinematic patterns associated with running and falls, and multi-inflection pedestrian trajectories. These heterogeneous cues are consolidated through a lightweight fusion mechanism—efficient enough for real-time execution on edge devices—to generate a unified panic score, thereby providing operators with a rapid and reliable basis for crowd-control decisions. Evaluated on the four-hour Hongqiao-Panic dataset collected at Hongqiao Railway Station, PBRM achieves 84.2% precision, 85.7% recall, 84.9% F1, and 1.18 s average alert lead, improving the best single-modality baseline by 3.0 pp F1 while keeping the false-alarm rate (FAR) below 8%. Ablation study shows that audio-semantic channels dominate early cues, where as posture-trajectory streams reinforce decision robustness under occlusion. These results demonstrate that PBRM can be deployed on edge devices to provide operators with a critical lead time for crowd-control interventions, advancing intelligent transportation safety from post-event surveillance to proactive prevention.

In public transportation hubs, the combined motion (CM) between a passenger and his luggage is a primary movement form. The state of CM always influences the stability of the movement of passenger crowds. While there is still a lack of systematic studies on the passenger-luggage combined motion, even most state-of-the-art literatures focus only on the impact of luggage on the overall efficiency of crowd evacuation. To fill this academic lag, this study investigated the motion relationship between passengers and luggage and then proposed a passenger-luggage combined motion model (PLCMM), which helps determine the relevance between passengers and luggage. Furthermore, a novel energy-based criterion for the stability of PLCMM and an abnormal state recognition model were established. Finally, the proposed model and criteria were validated in the waiting hall of Shanghai Hongqiao High-speed Railway Station, as a typical large public transportation scenario. The luggage abnormality recognition model can identify three abnormal states, i.e., luggage falling off, tail dumping, and sudden acceleration. A performance comparison between the proposed PLCMM and five other representative models was conducted from the aspects of scenarios, algorithm complexity, and research focus. The detection accuracy of the PLCCM was 96.154% and the algorithm complexity was considerably lower than the complexity exhibited by the purely computer vision-based approaches. The results demonstrated that PLCMM aligned closely with the ground truth, and outperformed other state-of-the-art models. The stability criterion can be employed to assess the impact of lateral perturbation forces on the stability of the passenger-luggage combined motion, to support the daily passenger flow control and management in more public traffic areas.

Panic behaviors in a pedestrian flow often lead to a state of chaos or disorder among the pedestrian crowd, resulting in a crowd accident with high possibility. To investigate the panic pedestrian dynamics and further prevent serious crowd accidents, simulation based on dynamics modeling and accident video data is a popular solution to date. Thereby, it is challenging but significant to improve the crowd dynamics model more consistent with the ground truth of real pedestrian movement scenarios, with consideration of both physical contact force and panic emotional propagation in a crowd. Therefore, this study proposed an extended social force model (ESFM) by applying the physical contact-force estimation during pedestrian collision based on non-smooth contact dynamics. Subsequently, the ESFM was integrated with an improved panic propagation model (IPPM) considering obstacle and promotion factors. Finally, taking the crowd panic accident happened in Nepal in 2015 as an experiment case, the simulation of panic crowd dynamics was conducted within Anylogic software. Four cases of SFM, ESFM, SFM+IPPM, and ESFM+IPPM were compared quantitatively and graphically. The experimental results showed that the pedestrian distribution obtained from the proposed ESFM+IPPM was the closest to the ground truth during the panic response period, with 28.8% lower of Hausdorff distance than the original SFM, and 21.6% lower the well-known BHSFM, respectively. This approach can help improve the panic crowd modeling and pedestrian distribution prediction in real scenarios.

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.

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.

The Internet-of-Things (IoT) technology realizes deep integration of the physical process and production information in a manufacturing workshop through the real-time acquisition of data and seamless interaction of equipment, showing valuable academic and applicable potential in upgrading the workshop environment and eliminating the effect of dynamics. This brings a new opportunity to increase workshop productivity. However, how to realize active perception, dynamic optimization, and real-time regulation of the manufacturing process based on IoT technology is a research hotspot facing technical and theoretical challenges. To address these issues, this study constructs a multiagent-based dynamic scheduling (MADS) workshop IoT architecture by embedding advanced ultrawide band (UWB) and 5th-generation (5G) communication technologies. Differing from conventional scheduling models, the proposed model optimally assigns processes to machines by designing a resource scheduling agency (RS Agency). The$NP$-complete of the multiobjective optimization dynamic job-shop scheduling problem is proved and reduced to a cooperative bargaining game negotiation model (CBGNM) to rationalize the allocation of workshop resources. Then, a multiobjective weights tuning scheme enables effective and efficient responses to workshop exceptions. The performance comparisons with public data of the Kacem$8\times 8$benchmark verify that the CBGNM has better results than the conventional scheduling methods and reaches maximum optimization of 41.7%, 39.3%, 47.2%, 32.9%, 54.5%, and 38.4% regarding makespan, critical machine workload, total processing time, fairness index, total energy consumption, and comprehensive index, respectively. The numerical experiments show that the game players have no significant coupling with their joint payoff and validate that their bargaining power varies with abnormal events.

Crowd stability analysis is an important yet challenging task, as it is difficult to obtain the quantitative information regarding a crowd in motion, for instance, the crowd count and crowd density in a pedestrian merging area. This paper proposes a novel model that can be used to accurately analyze the crowd stability based on images obtained from a real-time video surveillance system (VSS) in dense crowd scenarios. To enhance the accuracy of the human head recognition for the crowd counting and crowd density estimation, we improve the conventional convolution-neural-network (CNN) model with more columns and merged features, obtaining a four-column convolutional neural network (4C-CNN). Using more columns with receptive fields of deferent sizes, more merged features can be learned, to be adaptive to variations in pedestrian head size due to image resolution. Furthermore, the crowd density of different areas is calculated with image rectification against the perspective distortion. By utilizing the stability criterion based on crowd density, we propose a crowd stability analysis model (CSAM) with the capability of quantitative computation dynamically. The results of extensive experiments performed on public datasets indicate that this improved CNN model exhibits a better performance for crowd counting than the typical multi-column CNN models. In addition, the experiment results pertaining to Shanghai Hongqiao Railway Station demonstrate the effectiveness of the crowd stability analysis model. Thus, this integrated approach can simultaneously conduct the processes of crowd counting, image rectification, density map calculation and crowd stability analysis by using dense crowd images from a VSS.

With the increase in inevitable large-scale crowd aggregation, disastrous pedestrian stampedes occurred with increasing frequency over the past decade. To prevent these tragedies, it is significant to assess crowd accident-risk (CAR) and identify high-risk areas to control crowd flow dynamically. The cost function of a conventional fluid dynamics model is improved with new items of Gaussian white noise and protection factor, considering both the abnormal pedestrian movements and social distance control due to epidemic, thereby to establish an improved crowd flow model comprehensively. Different from conventional density-based pedestrian aggregation-risk models, this study proposes a hybrid crowd accident-risk assessment (HCRA) model based on internal energy and information entropy. Using the HCRA model, we can consider not only crowd density but also the modulus and direction of a crowd velocity vector simultaneously. Then this study designs a framework to realize crowd accident risk assessment based on the improved crowd-flow model and HCRA model. To validate the proposed models, case studies of CAR assessment in the large-scale waiting hall of the Shanghai Hongqiao railway station are conducted. The pedestrian social control distance-range of 1.0 m–2.0 m under the COVID-19 epidemic situation is verified numerically. Moreover, a valuable result is that this social control distance-range can be shortened to 1.0 m–1.9 m without increase of crow accident-risk. Subsequently, the down-limit of accommodation-capacity of this large waiting hall can be enhanced to 10.54% under this epidemic.

In emergencies close to a T-shaped street junction (TSJ), pedestrian overcrowding and trampling are typical unstable situations triggered by external disturbances, potentially leading to serious casualties during crowd movement. It is significant to analyze the pedestrian flow stability to achieve safe and efficient crowd evacuation. To analyze the stability of crowd flow, this study uses the two-dimensional Aw-Rascle dynamic-flow model to build the pedestrian merging dynamics at a TSJ area. Then the principle of the Lyapunov stability criterion in modern control theory is employed to analyze the stability of the pedestrian crowd. A novel stability criterion for an asymmetric pedestrian merging layout at a TSJ is then proposed. When the crowd acceleration in the local area exceeds the critical acceleration, the crowd will evolve to an unstable state. To validate this criterion, numerical simulations of a case study of a crowd stampede with serious casualties (1703 victims reported) occurring at a TSJ area in Mecca, Saudi Arabia, during the Hajj in 2015 is implemented. To build stable acceleration regimes, this paper tunes the key parameters of the number of pedestrians, width ratio of main street to branch street, and pedestrian drop-off location along the street 223, and discusses the stable ranges of acceleration to satisfy the pedestrian stability criterion. This criterion and the experimental results can supply new information to control pedestrian flow and keep it stable.

This study investigates a dynamic flow model for crowd evacuation at T-shaped street junctions (TSJs) from a macroscopic view. The Aw-Rascle traffic flow model is modified by constructing an impact matrix in the street intersection area to practically describe the crowd convergence mechanism at a TSJ. For coherence, this modified model is proved to be anisotropic, similar to the original Aw-Rascle traffic flow model. To describe real scenarios with higher crowd density and lower speeds during organized pilgrimages, the initial Gaussian distribution of the crowd is improved to a higher-order smoothing function. To validate the modified Aw-Rascle traffic flow model, we reconstruct the drastic stampede that occurred at the TSJ of streets 204 and 223 during the 2015 Mecca pilgrimage. Further, the main environmental parameters that potentially lead to a stampede are discussed with numerical simulations. A valuable suggestion is that the street width ratio should be extended from 1.1 to 1.4 to prevent stampedes, matching the expansion engineering of street 204 reported by BBC News. An interesting phenomenon is that the closer the bus unloading location on street 223 is to the TSJ center, the lower the maximum crowd density and the safer the pedestrians will be. With this modified Aw-Rascle flow model at TSJs, this paper provides strategic and technical suggestions for future crowd flow control to reduce the risk of crowd stampedes.

It is significant to discovery the impact of panic on crowd movement and study the panic propagation mechanism which can help real crowd control. This paper focuses on panic propagation dynamics in a high-density crowd based on information entropy theory and Aw-Rascle model. A novel concept of panic entropy is defined to quantify the confusion degree of crowd movement based on information entropy theory. The behavior characteristics are measured by the modulus and direction of the gridded crowd velocity vector in a two-dimension space. In order to study panic propagation dynamics, Aw-Rascle model is used to describe the crowd flow dynamics. Then, the dynamic model of panic propagation is proposed based on crowd flow. To validate the panic propagation model, numerical simulations are conducted based on one of stampedes happened in the Mecca Hajj in 2015. Simulation results show the relationship between the crowd panic entropy, density and velocity with visual panic distribution and different color contour diagrams. As a discussion result of the relationship between velocity and panic entropy, it is verified that: 1) moderate panic can help the crowd to keep relatively high velocity; 2) excessive panic can lead to irregular crowd movement and decrease crowd movement velocity especially in the over-crowded situation. The contribution of this paper is to provide a novel approach to quantify crowd panic degree and study panic propagation dynamics in a high-density crowd.