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.

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.

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.

Increasing maintenance costs will hinder the expansion of the wind power industry in the coming decades. Training personnel, field maintenance, and frequent boat or helicopter visits to wind turbines (WTs) is becoming a large cost. One reason for this cost is the routine turbine inspection repair and other stochastic maintenance necessitated by increasingly unbalanced figure loads and unequal turbine fatigue distribution in large-scale offshore wind farms (OWFs). In order to solve the problems of unbalanced fatigue loads and unequal turbine fatigue distribution, thereby cutting the maintenance cost, this study analyzes the disadvantages of conventional turbine fatigue definitions. We propose an improved fatigue definition that simultaneously considers the mean wind speed, wind wake turbulence, and electric power generation. Further, based on timed automata theory, a power dispatch approach is proposed to balance the fatigue loads on turbines in a wind farm. A control topology is constructed to describe the logical states of the wind farm main controller (WFMC) in an offshore wind farm. With this novel power control approach, the WFMC can re-dispatch the reference power to the wind turbines according to their cumulative fatigue value and the real wind conditions around the individual turbines in every power dispatch time interval. A workflow is also designed for the control approach implementation. Finally, to validate this proposed approach, wind data from the Horns Rev offshore wind farm in Denmark are used for a numerical simulation. All the simulation results with 3D and 2D figures illustrate that this approach is feasible to balance the loads on an offshore wind farm. Some significant implications are that this novel approach can cut the maintenance cost and also prolong the service life of OWFs.

Crowd stability is a research hotspot in the area of emergency management, while crowd density is the most direct and effective basis for crowd stability analysis. Therefore, it is significant to study the trend of crowd density and make some reliable predictions. This paper introduces advanced image processing technology, increasing the number of columns in a traditional convolutional neural network (CNN) to improve the accuracy of crowd counting, and further obtains high-precision crowd density values through image correction. Then, combined with the mature support vector regression (SVR) algorithm, this paper explores the kinematic characteristics of pedestrians, and proposes a novel crowd density prediction model. By predicting the position of group pedestrians in next few moments, the prediction of crowd density changes in public places can be achieved, which provides data support for crowd stability analysis. Finally, case study is carried out with actual scenario of Shanghai Hongqiao Airport, and the feasibility of the model is verified.