Narrow Results

Standard order–disorder phase transition in the Ising model is described in terms of rates of processes of spin flips. This formulation allows to extend numerous results on phase transition for sciences other than physics of magnetism. We apply it to the problem of norm breaking. Two strategies: to cooperate or to defect, form an analogy to two spin orientations. An example is a crowd at an exit, where to defect means to push others, and to cooperate is to move slowly together.

In order to depict crowd dynamics in the case of smoke effect, a novel evacuation model is presented based on the extended cellular automaton. In the model, the moving probability of the target position in the next time step is given by combining distance gain and smoke gain at first. Through considering individual kinship and group effect, group disbanding and regrouping rules are defined, and the optimal evacuation route planning is determined. At last, the simulation was conducted to study the key factors influencing evacuation efficiency. The results showed that when the group size was small and the smoke spreading velocity was slow, priority selection of the shortest route strategy was good for reducing evacuation time, and large-scale group should be avoided as much as possible in evacuation process.

To understand crowd evacuation, a model based on a bacterial foraging algorithm (BFA) is proposed in this paper. Considering dynamic and static factors, the probability of pedestrian movement is established using cellular automata. In addition, given walking and queue times, a target optimization function is built. At the same time, a BFA is used to optimize the objective function. Finally, through real and simulation experiments, the relationship between the parameters of evacuation time, exit width, pedestrian density, and average evacuation speed is analyzed. The results show that the model can effectively describe a real evacuation.

For the evacuation dynamics in indoor space, a novel crowd flow model is put forward based on Linear Fractional Stable Motion. Based on position attraction and queuing time, the calculation formula of movement probability is defined and the queuing time is depicted according to linear fractal stable movement. At last, an experiment and simulation platform can be used for performance analysis, studying deeply the relation among system evacuation time, crowd density and exit flow rate. It is concluded that the evacuation time and the exit flow rate have positive correlations with the crowd density, and when the exit width reaches to the threshold value, it will not effectively decrease the evacuation time by further increasing the exit width.

Trajectory-clustering-based methods have shown a good performance in counting moving objects in densely crowded scenes. However, they still fall into trouble in complex scenes, such as with the close proximity of moving objects, freely moving parts of objects, and different object size in different locations of the scene. This paper proposes a new method combining velocity and location-specific spatial clues in trajectories to deal with these problems. We first extract the velocities of a trajectory over its life-time. To alleviate confusion around the boundary regions between close objects, extracted velocity information is utilized to eliminate unreal-world feature points on objects' boundaries. Then, a function is introduced to measure the similarity of the trajectories integrating both of the spatial and the velocity clues. This function is employed in the Mean-Shift clustering procedure to reduce the effect of freely moving parts of the objects. To address the problem of various object sizes in different regions of the scene, we suggest a technique to learn the location-specific size distribution of objects in different locations of a scene. The experimental results show that our proposed method achieves a good performance. Compared with other trajectory-clustering-based methods, it decreases the counting error rate by about 10%.

The percolation model is an effective tool to solve the problem of fluid flow in the pores; the situation of outsiders crossing the crowd is similar. This paper verifies the obvious percolation phenomenon in the randomly distributed crowd by $\mathrm{\text{FDS}}+\mathrm{\text{Evac}}$ simulation and reveals several characteristics and laws of the crowd percolation phenomena. Studies have shown that sites with different spatial dimensions have different densities of crowd percolation: when the actual density of the crowd is greater than the critical density of the crowd percolation, the outsider is difficult to pass through the crowd; otherwise, the outsider can pass through the crowd easily. Therefore, the critical density of crowd percolation can be one of the indicators of crowd management in public areas, which will provide important guidance for the design of public areas, site selection of public activities and crowd management.

Continuous monitoring and automatic detection of crowd activities is extremely helpful for management at public places to avoid any possible disaster. Analysis of crowded scene is a critical task as it typically involves poor resolution of objects, occlusions and complex dynamics. In this paper, we propose a novel, systematic and generalized method based on global motion analysis of people to detect Congestion situation in crowded scenes at entry/exit corridors. Our approach is tested on video footages acquired from surveillance cameras installed at exit corridors of public places. The results show the expediency of our approach.

This contribution proposes a method to make agents in a microscopic simulation of pedestrian traffic walk approximately along a path of estimated minimal remaining travel time to their destination. Usually models of pedestrian dynamics are (implicitly) built on the assumption that pedestrians walk along the shortest path. Model elements formulated to make pedestrians locally avoid collisions and intrusion into personal space do not produce motion on quickest paths. Therefore a special model element is needed, if one wants to model and simulate pedestrians for whom travel time matters most (e.g. travelers in a station hall who are late for a train). Here such a model element is proposed, discussed and used within the Social Force Model.