Search

Pedestrian dynamics in emergent situations such as stampedes and fire hazards have been widely studied, while few studies are focusing on human behavior in terrorist attack events. The research is vitally important for guiding efficient evacuation of the attacked crowd and reducing casualties. However, the research is insufficient due to the lack of researching materials. Besides, it is unconvincing for researchers to carry out these types of experiments. In this paper, the recorded video of the 3 ⋅ 1 Kunming terrorist attack event in China is analyzed. The instantaneous velocity field of the attacked crowd in the ticket hall is extracted from the video material, which is based on an image processing method. By analyzing the statistical characteristics of crowd velocity, crowd movement frequency and crowd entropy, several phenomena are found including crowd oscillating phenomenon, self-organizing grouping phenomenon. These discoveries will benefit the understanding of crowd dynamic in close-range attacks which is essential for the evacuation guidance efficiency, better design of architecture layout, and better defending strategies against the terrorists. • Crowd velocity peak is oscillating back and forth within the attacked crowd. • The velocity entropy of crowd is shown oscillating around the mean value 6.7. • The characteristic frequency of crowd movement is about 0.4 Hz. • Crowd in different area who have different movement behavior patterns show grouping phenomenon. [ABSTRACT FROM AUTHOR]

There have generally been two kinds of approaches to the empirical study of human mobility. At the group level, some valuable information might be submerged in statistical noise, while due to the diversity of individual purpose and preference, there is still no general statistical regularity of human mobility at the individual level. In this paper, we considered group-level human mobility as the combination of several basic patterns and analyzed the collective mobility by category. Utilizing matrix factorization and correlation analysis, we extracted some of the stable/occasional components from the collective human mobility in the Beijing subway and found that the departure and arrival mobility patterns have different characteristics, both in time and space, under various conditions. We classified individual records into different patterns and analyzed the most likely trip distance by category. The proposed method can decompose stable/occasional mobility patterns from the collective mobility and identify passengers belonging to different patterns, helping us to better understand the origin of different mobility patterns and provide guidance for emergency management of large crowds. [ABSTRACT FROM AUTHOR]

We studied the distribution of entry time interval in Beijing subway traffic by analyzing the smart card transaction data, and then deduced the probability distribution function of entry time interval based on the Maximum Entropy Principle. Both theoretical derivation and data statistics indicated that the entry time interval obeys power-law distribution with an exponential cutoff. In addition, we pointed out the constraint conditions for the distribution form and discussed how the constraints affect the distribution function. It is speculated that for bursts and heavy tails in human dynamics, when the fitted power exponent is less than 1.0, it cannot be a pure power-law distribution, but with an exponential cutoff, which may be ignored in the previous studies. [ABSTRACT FROM AUTHOR]

Abstract: We investigate by mean-field analysis and extensive simulations the effects of social impact on epidemic spreading in various typical networks with two types of nodes: active nodes and passive nodes, of which the behavior patterns are modeled according to the social impact theory. In this study, nodes are not only the media to spread the virus, but also disseminate their opinions on the virus—whether there is a need for certain self-protection measures to be taken to reduce the risk of being infected. Our results indicate that the interaction between epidemic spreading and opinion dynamics can have significant influences on the spreading of infectious diseases and related applications, such as the implementation of prevention and control measures against the infectious diseases. [Copyright &y& Elsevier]

Abstract: The class of generative models has already attracted considerable interest from researchers in recent years and much expanded the original ideas described in BA model. Most of these models assume that only one node per time step joins the network. In this paper, we grow the network by adding interconnected nodes as a local structure into the network at each time step with each new node emanating new edges linking the node to the preexisting network by preferential attachment. This successfully generates key features observed in social networks. These include power-law degree distribution , where is a tuning parameter defined as the modularity strength of the network, nontrivial clustering, assortative mixing, and modular structure. Moreover, all these features are dependent in a similar way on the parameter . We then study the susceptible-infected epidemics on this network with identical infectivity, and find that the initial epidemic behavior is governed by both of the infection scheme and the network structure, especially the modularity strength. The modularity of the network makes the spreading velocity much lower than that of the BA model. On the other hand, increasing the modularity strength will accelerate the propagation velocity. [Copyright &y& Elsevier]

Menezes and Barabasi have once developed a framework by mean–variance analysis to investigate the relationship between the average flux and fluctuations and found a scaling-law behavior in two distinct phase. By then most related researches dedicated to understanding the scaling laws through the internal/external forces of the system. However, as for subway system, the demand of transporting a large amount of passengers during peak hours is a necessity. Thus, alternating the phase or reducing the impact of external forces by cutting down the total passengers is completely unrealistic. The more operable approaches are specifically regulating the distribution of passenger flow in order to drive the system into a more stable phase. In this paper, we carried out the mean–variance analysis on subway traffic and dedicated to discover the effect of spatial–temporal distributions of passenger flow on the fluctuations in subway traffic. By analyzing the control properties of the transit networks constructed according to different passenger flow threshold , it is found that the control mode of transit networks might differ the phase change of fluctuations, which proposed a new perspective of controlling the fluctuations in subway traffic. • Transit network of metro system was constructed to character the spatial–temporal distribution of traffic. • Clear scaling-law can be observed between the mean and variance of passenger flow in the transit network. • The scale exponent vary within a day and are dominated by the traffic variation and the control mode of the transit networks. • Traffic restrictions should be given in priority to stations with large inbound and outbound traffic at morning (evening) peak hours. [ABSTRACT FROM AUTHOR]

Information dissemination has drawn increasing attention of scholars and the government for its importance to disaster response. Understanding the dynamics of information dissemination can help the government disseminate information effectively. Considering people tend to share information with the intimate under disaster, social distance which measures the intimacy between individuals is introduced in this paper. An information dissemination model with preference selection based on social distance is also proposed. Through extensive simulations, we discover the information dissemination process will be suppressed if people prefer disseminating information to individuals with short social distance. In order to facilitate the information dissemination, award to spreaders is found useful for its significant acceleration in dissemination speed. In addition, we examine the efficiency of strategies the government adopts to disseminate information. Comparing to inform-way (q fraction of nodes informed in descending order of H-index), the government is recommended to disseminate information by broadcast-way (all nodes accepting information with a probability λ b). Our work contributes to understanding the dynamics of information dissemination, and provides the government with suggestions to disseminate information effectively under disaster. [ABSTRACT FROM AUTHOR]

Evidence suggests that a human domino effect may be formed based on microscopic human behaviors during interactions among individuals in a dense crowd and probably induces crowd disasters. We established a human domino model to simulate such a domino process. With respect to a high-density crowd, the initial impulse tended to keep growing until the boundary, which perhaps resulted in crowd disasters. We can also use the repeatability of the domino process to predict the impulse exerted on individuals, which provides a prediction of crowd disasters. In addition, hand contact is conducive to maintaining the balance of the individual, but significantly intensifies the impulse in the entire process and causes even greater risk. The effect of the fixed boundary was significant especially in the case of a very high-density crowd, resulting in the violent crushing on individuals. • In high-density crowd, the impulse tends to keep growing until the boundary. • The hand contact magnifies the impulse exerted on all individuals. • Effect of the fixed boundary on individual is stronger than that of open boundary. [ABSTRACT FROM AUTHOR]

Abstract: Introducing the force concept of a social force model into the lattice gas (LG) model, a new LG-based discrete model entitled “multi-grid model” is composed. In the new model, finer lattice is used; thus each pedestrian occupies multiple grids instead of one, and the rules of interactions among pedestrians or pedestrians and constructions are built. The interaction forces including extrusion, repulsion and friction are considered as passive factors for evacuation. The strength of the drift, or the intensity of the pedestrians to move toward the exit rapidly, is considered an active factor. A simple situation is studied in which pedestrians try to evacuate from a large room with only one door. The influences of interaction forces and drift on evacuation time are analyzed. The mutual restriction relation of the two factors in the course of evacuating is found. [Copyright &y& Elsevier]

Crime is pervasive all around the world. Understanding the influence of social features on crime occurrences of a city is a hot topic among researchers. Correlations between crime and other social characteristics have been studied by large amounts of statistical models, including Ordinary Least Square (OLS) linear regression model, Random Forest (RF) regression model, Artificial Neural Network (ANN) model and so on. However, results of these studies, such as the prediction accuracy, are not satisfying and many contradictory conclusions are achieved in previous research works. These controversies are triggered by several factors, including the non-Gaussian distributions and multicollinearity of urban social data, inaccuracy and inadequacy of the processed data, etc. To fill these gaps, we analyzed the influence of 18 urban indicators within 6 categories including geography, economy, education, housing, urbanization and population structure on crime risk in China's major prefecture-level cities by year. We used the big data algorithm, Least Absolute Shrinkage and Selection Operator (LASSO) and Ext remely- ra ndomized Trees (Extra-Trees), to predict the crime risk and quantify the influence of urban parameters on crime. 83% of accuracy on crime risk prediction can be obtained from our fitted model and the importance of urban indicators is ranked. Results show that area of land used for living, number of subscribers of mobile telephone, employed population are the three main factors on the crime occurrences in China. Our research makes contributions to better understanding of the effects of urban indicators on crime in a socialist nation, and providing instructions and strategies for crime prediction and crime rate control with governments, in this big-data era. • Heteroscedasticity, multicollinearity and abnormal distribution characteristics are validated in social urban metrics. • 18 urban indicators within 6 categories are considered and analyzed. • Prediction accuracy reaches 83% and quantification of urban indicators importance is unveiled. [ABSTRACT FROM AUTHOR]