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Collective movement of animal in emergency condition has attracted growing attentions among researchers. However, many rules still need to be confirmed with adequate explanation. Study of collective behavior of mice can improve our understanding about the dynamics of pedestrian movement. However, its rules still need to be confirmed with adequate explanation. In this paper, collective behavior of mice passing through an exit under panic was investigated. The results showed that the total evacuation time decreased with exit width increasing in a certain range. Based on the different tendency of the curve in temporal evolution, the process of mice flow was divided into three stages. The density of mice near the exit peaks at a certain horizontal offset and starts to decrease over time. With the increase of the exit width, the duration of the higher density state decreased. We found that the frequency of time intervals obeyed a lognormal distribution or an exponential decay for different exit widths. In addition, the relationship between the group size and the group flow rate in different scenarios was analyzed. The phenomena found in our experiments show the collective behavioral characteristic of mice under panic. Our analysis in this paper will deepen our understanding of crowd dynamics in emergency condition. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the dynamical behavior of a stochastic SEI epidemic model with saturation incidence and logistic growth. Firstly, by constructing a suitable stochastic Lyapunov function, we establish sufficient conditions for the existence and uniqueness of an ergodic stationary distribution of the solutions to the model. Then we obtain sufficient conditions for extinction of the disease in two cases, that is, the first case is that the susceptible population survival and infected and exposed populations extinction; the second case is that all the populations extinction. • A stochastic SEI epidemic model with saturation incidence and logistic growth is proposed and studied. • We establish sufficient conditions for the existence of a unique ergodic stationary distribution. • We obtain sufficient conditions for extinction of the disease in two cases. [ABSTRACT FROM AUTHOR]

Investigating the dynamics of long-term memory in online rating behaviors is significant for understanding the evolution mechanism of collective behaviors and trust formation for online social networks. Since users are allowed to deliver ratings in many online systems, ratings can well reflect the user’s opinions. In this paper, we empirically investigate the long-term memory, measured by the Detrended Fluctuation Analysis, in collective rating behaviors before and after the trust formation. The results for the Epinions data set show that, comparing with the null model generated by the reshuffle process, the Hurst exponent of trustors (trustees) decreases 7.12% (9.05%) before and increases 7.36% (9.20%) after trust formation, which stably remains close to 0.5 in null model I and 0.6 in null model II, suggesting that the collective rating behavior plays an important role for the trust formation. Furthermore, we divide users into 8 groups according to the user degree and find that the correlation of the user degree and the variation of Hurst exponent, measured by the Pearson Correlation Coefficient, is 0.8629 and 0.8620 before and after trust formation respectively, reflecting a significant correlation between user degrees and collective rating behavior patterns. Finally, we select the users without creating other trust relations around the trust formation time and the result suggests that the collective rating behaviors indeed change for the trust formation. This work helps deeply understand the intrinsic feedback effects between collective behaviors and trust relationship. [ABSTRACT FROM AUTHOR]

This paper introduces new methods for analysing the extreme and erratic behaviour of time series to evaluate the impact of COVID-19 on cryptocurrency market dynamics. Across 51 cryptocurrencies, we examine extreme behaviour through a study of distribution extremities, and erratic behaviour through structural breaks. First, we analyse the structure of the market as a whole and observe a reduction in self-similarity as a result of COVID-19, particularly with respect to structural breaks in variance. Second, we compare and contrast these two behaviours, and identify individual anomalous cryptocurrencies. Tether (USDT) and TrueUSD (TUSD) are consistent outliers with respect to their returns, while Holo (HOT), NEXO (NEXO), Maker (MKR) and NEM (XEM) are frequently observed as anomalous with respect to both behaviours and time. Even among a market known as consistently volatile, this identifies individual cryptocurrencies that behave most irregularly in their extreme and erratic behaviour and shows these were more affected during the COVID-19 market crisis. • This paper analyses the 51 largest cryptocurrencies by market capitalisation. • We introduce new methods of analysis of extreme and erratic behaviour of time series. • Hierarchical clustering of extremal distributions and change points is applied. • Inconsistency matrices between these behaviours and time periods are introduced. • Anomalous cryptocurrencies with respect to behaviour and time are identified. [ABSTRACT FROM AUTHOR]

The individual behavior model is a contributory factor to improve the accuracy of agent-based simulation in different scenarios. However, few studies have considered moving threat agents, which often occur in terrorist attacks caused by attackers with close-range weapons (e.g., sword, stick). At the same time, many existing behavior models lack validation from cases or experiments. This paper builds a new individual behavior model based on seven behavioral hypotheses. The driving-forces model is an extension of the classical social force model considering scenarios including moving threat agents. An experiment was conducted to validate the key components of the model. Then the model is compared with an advanced Elliptical Specification II social force model, by calculating the fitting errors between the simulated and experimental trajectories, and being applied to simulate a specific circumstance. Our results show that the driving-forces model reduced the fitting error by an average of 33.9% and the standard deviation by an average of 44.5%, which indicates the accuracy and stability of the model in the studied situation. The new driving-forces model could be used to simulate individual behavior when analyzing the risk of specific scenarios using agent-based simulation methods, such as risk analysis of close-range terrorist attacks in public places. [ABSTRACT FROM AUTHOR]

This paper investigates the revenue-neutral tradable credit charge and reward scheme without initial credit allocations that can reassign network traffic flow patterns to optimize congestion and emissions. First, we prove the existence of the proposed schemes and further decentralize the minimum emission flow pattern to user equilibrium. Moreover, we design the solving method of the proposed credit scheme for minimum emission problem. Second, we investigate the revenue-neutral tradable credit charge and reward scheme without initial credit allocations for bi-objectives to obtain the Pareto system optimum flow patterns of congestion and emissions; and present the corresponding solutions are located in the polyhedron constituted by some inequalities and equalities system. Last, numerical example based on a simple traffic network is adopted to obtain the proposed credit schemes and verify they are revenue-neutral. [ABSTRACT FROM AUTHOR]

Fractal analysis is increasingly used to describe, and provide further understanding to, zooplankton swimming behavior. This may be related to the fact that fractal analysis and the related fractal dimension D have the desirable properties to be independent of measurement scale and to be very sensitive to even subtle behavioral changes that may be undetectable to other behavioral variables. As early claimed by Coughlin et al. (1992), this creates “ the need for fractal analysis ” in behavioral studies, which has hence the potential to become a valuable tool in zooplankton behavioral ecology. However, this paper stresses that fractal analysis, as well as the more elaborated multifractal analysis, is also a risky business that may lead to irrelevant results, without paying extreme attention to a series of both conceptual and practical steps that are all likely to bias the results of any analysis. These biases are reviewed and exemplified on the basis of the published literature, and remedial procedures are provided not only for geometric and stochastic fractal analyses, but also for the more complicated multifractal analysis. The concept of multifractals is finally introduced as a direct, objective and quantitative tool to identify models of motion behavior, such as Brownian motion, fractional Brownian motion, ballistic motion, Lévy flight/walk and multifractal random walk. I finally briefly review the state of this emerging field in zooplankton behavioral research. [ABSTRACT FROM AUTHOR]

Abstract: Occupant behavior which is very complex affects evacuation efficiency and route choice a lot. The psychology and behavior of going with the crowd is very common in daily life and also in occupant evacuation. In this paper, a two-dimensional Cellular Automata model is applied to simulate the process of evacuation considering the psychology of going with the crowd with different room structure or occupant density. The psychology of going with the crowd (the abbreviation is GWC) is classified into directional GWC (DGWC) and spatial GWC (SGWC). The influence of two such kinds of psychology on occupant evacuation is discussed in order to provide some useful guidance on the emergency management of evacuation. [Copyright &y& Elsevier]

Abstract: This paper develops a multivariate long-memory stochastic volatility model which allows the multi-asset long-range dependence in the volatility process. The motivation is from the fact that both autocorrelations and cross-correlations of some proxies of exchange rate volatility exhibit strong evidence of long-memory behavior. The statistical properties of the new stochastic volatility model provide theoretical explanation to the common findings that long memory volatility properties are more apparent if we use absolute return as a volatility proxy than squared return. Results of the real data application show that our model outperforms an existing multivariate stochastic volatility model. [Copyright &y& Elsevier]

2D competitive systems shows a large variety of solid and aggregate patterns, re-entrant fluid phase, a continuous melting as predicted by the KTHNY theory and, in some cases, waterlike anomalies. In this paper, we employ Langevin Dynamics simulations of a ramp-like core-softened fluid that have two characteristic length scales and all the features mentioned above. Analyzing the fluid phase of this system, Bordin and Barbosa (2018) reported the existence of two waterlike anomalous region. Now, we revisit this problem focusing in the low temperature regime and a larger range of densities looking for a relation between the origin of the anomalous behavior, the solid phases and the re-entrant melting. Now, not two, but three anomalous regions were observed. The extra anomalous regions are related to the re-entrant melting of a amorphous to a cluster fluid. They are ruled not only by the competition between the characteristic length scales in the potential, but also by extra competitions induced by the variety of particles conformations. These competitions extends from the solid to the fluid phase, reflecting in the structural waterlike anomaly. Our results shade some light in the complex behavior of two length scales competitive potential, and helps to elucidate the relation between the large number of solid phases and the existence of more than one waterlike anomalies region in these systems. • We observe three distinct waterlike structural anomalous regions at low temperatures related to the re-entrant melting. • We perform the analysis of the ordered/disordered solid transitions in a core-softened fluid. • We show the relation between this transition, the re-entrant melting and the waterlike anomalies. • We show that the solid structure are related to the fluid clustering [ABSTRACT FROM AUTHOR]

An improved cellular automata model based on car–truck combination effect oriented to faithfully reproducing the characteristics of heterogeneous traffic flow is presented in this paper. The model focuses on reflecting complex car-following and lane-changing behavior of cars and trucks by incorporating the difference of physical performance and driving behavior between cars and trucks and the behavioral change caused by car–truck interaction. The update rules of cars and trucks were constructed separately based on the analysis results of NGSIM and SP survey. The impact of the truck in front was introduced into the model, which can affect the lane-changing probability, safety expectation and randomization probability. As revealed by the simulation results, the driving of vehicles in the heterogeneous traffic (i.e. car–truck, truck–car, car–car and truck–truck) is reproduced properly through the model. In addition, the model is capable of reproducing the hysteresis phenomenon and identifying three typical traffic states, namely free flow, synchronized flow and jam. Under certain conditions, trucks can help to eliminate the propagation of congestion waves. Moreover, the percentage of trucks and truck impact have a significant influence on the fundamental diagram, congestion rate, lane-changing rate and traffic stability. The findings of this study can provide a basis for further research on the heterogeneous traffic. • The behavioral characteristics caused by car–truck interaction are considered. • The CA model is improved based on car–truck combination effect. • Trucks can help dissipate some congestion propagation. • The influence of truck impact on the heterogeneous traffic flow are studied. [ABSTRACT FROM AUTHOR]

Emergency evacuation in case of an emergency is a crucial problem in high-rise buildings, as many occupants are in a limited amount of space. To improve building safety design and evacuation strategies, it is essential to understand how individuals behave during an evacuation in high-rise buildings. This paper surveys the recently available literature on evacuation in high-rise buildings with the following objectives: (1) to review the high-rise building evacuation experiment methods; (2) to review the wayfinding and impact factors in horizontal evacuation; and (3) to review the individual and crowd behaviors in vertical evacuation. The review highlights the application of the virtual reality technology in evacuation experiments and the two-side effect of the group behavior in high-rise buildings. Future research should focus on quantitative pre-evacuation behavior study, the elevator's assistant function, and the impact of group relations on evacuation. As the height of high-rise buildings continues to increase, individual characteristics, such as mobility issues and fatigue, warrant further study. • Review the individual and crowd behavior in high-rise building. • Make an integration of experiment methods about evacuation in high-rise building. • Discuss the horizontal evacuation. Mainly from the two following aspects: the wayfinding and impact factors. • Discuss the vertical evacuation. Mainly contains the behavior in stairs, in elevators, on refuge floor, in group and others. • Discuss the challenges of high-rise buildings evacuation. [ABSTRACT FROM AUTHOR]

In this paper, a two-lane cellular automata traffic flow model based on regular hexagonal cells is proposed. The realistic lane changing maneuvers with the lateral movement process, the lane changing duration, and the unsuccessful lane changing are considered by the new model. The vehicle trajectories from NGSIM dataset are used to extract the realistic lane changing maneuvers and calibrate the lane changing angle. The numerical results showed that the flow in the intermediate density range of the new model is lower than that of the traditional model. While the lane changing frequency in the same density range of the new model is higher than that of the traditional model. The lane changing duration will increase as the density grows. And the unsuccessful lane changing rate grows to large values in high density range. As to the heterogeneous traffic flow, the lane changing frequency is obviously greater than that in homogeneous condition. The trucks are more likely to make unsuccessful lane changing. And the lane changing duration of trucks is longer than that of cars in free flow region, but this is not the case in congested flow. • Modeling the lateral movement during the lane changing process. • Proposing the velocity related lane changing duration. • Considering the unsuccessful lane changing behavior. [ABSTRACT FROM AUTHOR]

Automated Fare Collection (AFC) systems in rail transit services collect enormous amounts of detailed data on on-board transactions. A better understanding of travelers' commuting and transfer behavior based on those massive volumes of AFC data would enable the rail service operators to evaluate their service quality and optimize operation strategies. This paper proposes an efficient and effective data mining procedure to figure out the association rules, aiming to extract connectivity and correlation of passenger flow among different services lines in urban rail transit networks. A case study based on the Beijing Subway network is conducted to demonstrate the applicability of the proposed method. Using up to 28 million AFC smart card transaction data, we match and analyze travelers' trip chains to investigate the commuting trip patterns in terms of spatio-temporal distribution characteristics. An innovational non-nigh-to-five commuting behavior and traditional nine-to-five commuting behavior are divided by the obtained associated rules to ensure a more nuanced description of commuting behaviors. Further, the results indicated by stronger association rules (2-frequent itemset and 3-frequent itemset) also provide a better understanding of transfer behaviors, like the frequent transfers among different service lines, and potentially vulnerable stations in the network. The research outcomes can be used to assist rail transit service operators in developing optimal operation strategies like timetabling design to enhance the transfer performance between different rail lines. • An efficient and effective data mining procedure is developed to figure out the association rules, aiming to extract connectivity and correlation of passenger flow among different services lines. • An innovational non-nigh-to-five commuting behavior and traditional nine-to-five commuting behavior are divided by the obtained associated rules to ensure a more nuanced description of commuting behaviors. • A case study based on the Beijing Subway network is conducted to demonstrate the applicability of the proposed method. • Experimental results indicated by strong association rules provide a better understanding of transfer coordination in urban rail transit networks. [ABSTRACT FROM AUTHOR]

In this paper we analyze the spatial distribution of elements around points of interest. Based on a spatial exclusion principle we model the system by means of a Fermi–Dirac distribution defined by two easily interpretable parameters. By means of image analysis, two real cases are studied and compared to the theory: people in an open-air concert and cars in a mall parking-lot. We show that they closely obey the proposed fermion-like statistics. • The spatial distribution of elements around points of interest follows the Fermi–Dirac distribution. • A simple and intuitive model is proposed based on an spatial exclusion principle. • Empirical examples show very good agreement with model (people in a concert and cars in a parking-lot). [ABSTRACT FROM AUTHOR]

More consideration on individual attribute or behavior has been recognized as the important segment for a comprehensive understanding of information propagation. In this paper, we investigate the impacts of individual attention-decay effect on dynamical process under the framework of activity-driven network. In order to explicitly describe the attention-decay, a modified model incorporating sub-infected (or decay) status based on SIR is proposed. In the current model, infected individuals may reduce their attentions towards information at a state-dependent decay rate formulated by threshold model, which is determined by the time-varying state of their neighbors. Whereafter, outbreak thresholds of information dynamics under different circumstances are obtained and the comparison with the classic model is also carried out accordingly. The results show that the introduction of attention-decay effect greatly interferes with the outbreak threshold and prevalence scale of information. Extensive numerical simulations are presented to illustrate the validity and efficiency of our theoretical results. • A novel propagation model with individual attention-decay effect is established. • Outbreak thresholds of the information are provided by theoretical calculations. • The impacts of attention-decay effect on outbreak threshold are investigated. [ABSTRACT FROM AUTHOR]

In many real networks, temporary and fluctuant high load on nodes does not always lead to the complete failure of them. The recovery of nodes' traffic function and the corresponding cascading congestion phenomenon can be observed. In this paper, we quantitatively associate local node capacity with external network load, and reveal the cascading congestion phenomenon of networks by modeling the traffic recovery behavior of nodes under fluctuant load. A hard traffic recovery model describing the general traffic recovery behavior of nodes and an adaptive traffic recovery model containing a local flow-adjusting strategy are proposed. We apply the two models to artificial networks and real networks. The network cascading congestion process and the limitation of network delivery ability under given node capacity are revealed. A hierarchical and load-dependent distribution of accepting probability is proved to be beneficial for the adaptive traffic recovery model in enhancing the network robustness against cascading congestion. Moreover, the critical node capacity corresponding to the maximal network load can be determined by our models. This function of our models is significant for obtaining the maximal network delivery ability with the lowest cost of node buffer in real applications. • Node traffic recovery behavior and network cascading congestion are modeled. • Network robustness is enhanced by an adaptive traffic recovery model. • The critical node capacity allows for maximal network load is determined. • The performance of our model is tested on BA networks and real networks. [ABSTRACT FROM AUTHOR]

Group behaviors widely exist in the passengers' boarding process at the platform of high speed railway (HSR) and have significant influences on the boarding efficiency. In this paper, we propose a cellular automaton (CA) model to explore the impacts of group behavior during the boarding process at the platform of HSR station. In the proposed method, the leader–follower behavior rules are explicitly modeled and discussed. The numerical results indicate that the proposed model can effectively reproduce the actions of group passengers at the platform of HSR and that the group behaviors have prominent negative impacts on the boarding efficiency. Finally, the negative impact degrees of group behaviors are verified by testing three schemes of seat arrangement, which provides new insights into the management of boarding process. • A CA model is proposed to simulate the pedestrian flow of the HSR platform. • The CA model considers the group behavior in the boarding process. • The negative impacts of group behavior are deduced from the numerical results. • The scheme of seat arrangement can decrease the negative impacts of group behavior. [ABSTRACT FROM AUTHOR]

In this paper, we are concerned with the threshold-one contact process on Z d. We give a limit theorem of the probability of the event that the origin is occupied for the upper invariant measure of the process as the dimension d grows to infinity while the infection rate is inversely proportional to d. A branching process and a SIR model are introduced as auxiliary processes for the proof of our main result. • We study the upper invariant measure of the threshold-one contact process on the lattice. • We gives a limit theorem of the probability of the event that the origin is occupied for the upper invariant measure. • A branching process is introduced as an auxiliary model for the proof of the subcritical case. • A SIR model is introduced as an auxiliary model for the proof of the supercritical case. [ABSTRACT FROM AUTHOR]