Showing papers in "Physica A-statistical Mechanics and Its Applications in 2012"
TL;DR: Simulations on four real networks show that the proposed semi-local centrality measure can well identify influential nodes and is a tradeoff between the low-relevant degree centrality and other time-consuming measures.
Abstract: Identifying influential nodes that lead to faster and wider spreading in complex networks is of theoretical and practical significance. The degree centrality method is very simple but of little relevance. Global metrics such as betweenness centrality and closeness centrality can better identify influential nodes, but are incapable to be applied in large-scale networks due to the computational complexity. In order to design an effective ranking method, we proposed a semi-local centrality measure as a tradeoff between the low-relevant degree centrality and other time-consuming measures. We use the Susceptible–Infected–Recovered (SIR) model to evaluate the performance by using the spreading rate and the number of infected nodes. Simulations on four real networks show that our method can well identify influential nodes.
898 citations
TL;DR: The social structure of Facebook “friendship” networks at one hundred American colleges and universities at a single point in time is studied, finding for example that common high school is more important to the social organization of large institutions and that the importance of common major varies significantly between institutions.
Abstract: We study the social structure of Facebook “friendship” networks at one hundred American colleges and universities at a single point in time, and we examine the roles of user attributes–gender, class year, major, high school, and residence–at these institutions. We investigate the influence of common attributes at the dyad level in terms of assortativity coefficients and regression models. We then examine larger-scale groupings by detecting communities algorithmically and comparing them to network partitions based on user characteristics. We thereby examine the relative importance of different characteristics at different institutions, finding for example that common high school is more important to the social organization of large institutions and that the importance of common major varies significantly between institutions. Our calculations illustrate how microscopic and macroscopic perspectives give complementary insights on the social organization at universities and suggest future studies to investigate such phenomena further.
739 citations
TL;DR: A simple roulette-wheel selection algorithm is presented, which typically has O(1) complexity and is based on stochastic acceptance instead of searching, which might be suitable for highly heterogeneous weight distributions, found, for example, in some models of complex networks.
Abstract: Roulette-wheel selection is a frequently used method in genetic and evolutionary algorithms or in modeling of complex networks Existing routines select one of N individuals using search algorithms of O ( N ) or O ( log N ) complexity We present a simple roulette-wheel selection algorithm, which typically has O ( 1 ) complexity and is based on stochastic acceptance instead of searching We also discuss a hybrid version, which might be suitable for highly heterogeneous weight distributions, found, for example, in some models of complex networks With minor modifications, the algorithm might also be used for sampling with fitness cut-off at a certain value or for sampling without replacement
579 citations
TL;DR: The model extends the classical Susceptible-Infected-Removed (SIR) rumor spreading model by adding a direct link from ignorants to stiflers and a new kind of people-Hibernators and derive mean-field equations that describe the dynamics of the SIHR model in social networks.
Abstract: There are significant differences between rumor spreading and epidemic spreading in social networks, especially with consideration of the mutual effect of forgetting and remembering mechanisms. In this paper, a new rumor spreading model, Susceptible-Infected-Hibernator-Removed (SIHR) model, is developed. The model extends the classical Susceptible-Infected-Removed (SIR) rumor spreading model by adding a direct link from ignorants to stiflers and a new kind of people-Hibernators. We derive mean-field equations that describe the dynamics of the SIHR model in social networks. Then a steady-state analysis is conducted to investigate the final size of the rumor spreading under various spreading rate, stifling rate, forgetting rate, and average degree of the network. We discuss the spreading threshold and find the relationship between the final size of the rumor and two probabilities. Also Runge–Kutta method is used for numerical simulation which shows that the direct link from the ignorants to the stiflers advances the rumor terminal time and reduces the maximum rumor influence. Moreover, the forgetting and remembering mechanisms of hibernators postpone the rumor terminal time and reduce the maximum rumor influence.
325 citations
TL;DR: In this paper, the eigenvalues and eigenvectors of correlations matrices of some of the main financial market indices in the world were used to investigate financial market crises that occurred in the years 1987 (Black Monday), 1998 (Russian crisis), 2001 (Burst of the dot-com bubble and September 11), and 2008 (Subprime Mortgage Crisis).
Abstract: Using the eigenvalues and eigenvectors of correlations matrices of some of the main financial market indices in the world, we show that high volatility of markets is directly linked with strong correlations between them. This means that markets tend to behave as one during great crashes. In order to do so, we investigate financial market crises that occurred in the years 1987 (Black Monday), 1998 (Russian crisis), 2001 (Burst of the dot-com bubble and September 11), and 2008 (Subprime Mortgage Crisis), which mark some of the largest downturns of financial markets in the last three decades.
272 citations
TL;DR: This paper builds models for 20 million trajectories with fine granularity collected from more than 10 thousand taxis in Beijing, indicating the bursty nature of human mobility, similar to many other human activities.
Abstract: As a significant factor in urban planning, traffic forecasting and prediction of epidemics, modeling patterns of human mobility draws intensive attention from researchers for decades. Power-law distribution and its variations are observed from quite a few real-world human mobility datasets such as the movements of banking notes, trackings of cell phone users’ locations and trajectories of vehicles. In this paper, we build models for 20 million trajectories with fine granularity collected from more than 10 thousand taxis in Beijing. In contrast to most models observed in human mobility data, the taxis’ traveling displacements in urban areas tend to follow an exponential distribution instead of a power-law. Similarly, the elapsed time can also be well approximated by an exponential distribution. Worth mentioning, analysis of the interevent time indicates the bursty nature of human mobility, similar to many other human activities.
233 citations
TL;DR: Wang et al. as discussed by the authors investigated whether and how patterns of human mobility inside cities are affected by two urban morphological characteristics: compactness and size, and found that the distribution of human's intra-urban travel in general follows the exponential law.
Abstract: This paper provides a new perspective on human motion with an investigation of whether and how patterns of human mobility inside cities are affected by two urban morphological characteristics: compactness and size. Mobile phone data have been collected in eight cities in Northeast China and used to extract individuals’ movement trajectories. The massive mobile phone data provides a wide coverage and detailed depiction of individuals’ movement in space and time. Considering that most individuals’ movement is limited within particular urban areas, boundaries of urban agglomerations are demarcated based on the spatial distribution of mobile phone base towers. Results indicate that the distribution of human’s intra-urban travel in general follows the exponential law. The exponents, however, vary from city to city and indicate the impact of city sizes and shapes. Individuals living in large or less compact cities generally need to travel farther on a daily basis, and vice versa. A Monte Carlo simulation analysis based on Levy flight is conducted to further examine and validate the relation between intra-urban human mobility and urban morphology.
216 citations
TL;DR: The community detection is solved as a multiobjective optimization problem by using the multiobjectives evolutionary algorithm based on decomposition, which maximizes the density of internal degrees, and minimizes thedensity of external degrees simultaneously.
Abstract: Community structure is an important property of complex networks. Most optimization-based community detection algorithms employ single optimization criteria. In this study, the community detection is solved as a multiobjective optimization problem by using the multiobjective evolutionary algorithm based on decomposition. The proposed algorithm maximizes the density of internal degrees, and minimizes the density of external degrees simultaneously. It can produce a set of solutions which can represent various divisions to the networks at different hierarchical levels. The number of communities is automatically determined by the non-dominated individuals resulting from our algorithm. Experiments on both synthetic and real-world network datasets verify that our algorithm is highly efficient at discovering quality community structure.
211 citations
TL;DR: It is shown that the proposed improved PSVG is less sensitive to noise and therefore more robust compared with PSVG, and used in the wavelet-chaos neural network model of Adeli and c-workers in place of the Katz fractality dimension results in a more accurate diagnosis of autism, a complicated neurological and psychiatric disorder.
Abstract: Recently, the visibility graph (VG) algorithm was proposed for mapping a time series to a graph to study complexity and fractality of the time series through investigation of the complexity of its graph. The visibility graph algorithm converts a fractal time series to a scale-free graph. VG has been used for the investigation of fractality in the dynamic behavior of both artificial and natural complex systems. However, robustness and performance of the power of scale-freeness of VG (PSVG) as an effective method for measuring fractality has not been investigated. Since noise is unavoidable in real life time series, the robustness of a fractality measure is of paramount importance. To improve the accuracy and robustness of PSVG to noise for measurement of fractality of time series in biological time-series, an improved PSVG is presented in this paper. The proposed method is evaluated using two examples: a synthetic benchmark time series and a complicated real life Electroencephalograms (EEG)-based diagnostic problem, that is distinguishing autistic children from non-autistic children. It is shown that the proposed improved PSVG is less sensitive to noise and therefore more robust compared with PSVG. Further, it is shown that using improved PSVG in the wavelet-chaos neural network model of Adeli and c-workers in place of the Katz fractality dimension results in a more accurate diagnosis of autism, a complicated neurological and psychiatric disorder.
166 citations
TL;DR: In this paper, the authors present two new statistical tools that help to determine the overall correlation for the whole multivariate set on a scale-by-scale basis, which can be interpreted as perfect integration of these Euro stock markets at the longest time scales.
Abstract: Statistical studies that consider multiscale relationships among several variables use wavelet correlations and cross-correlations between pairs of variables. This procedure needs to calculate and compare a large number of wavelet statistics. The analysis can then be rather confusing and even frustrating since it may fail to indicate clearly the multiscale overall relationship that might exist among the variables. This paper presents two new statistical tools that help to determine the overall correlation for the whole multivariate set on a scale-by-scale basis. This is illustrated in the analysis of a multivariate set of daily Eurozone stock market returns during a recent period. Wavelet multiple correlation analysis reveals the existence of a nearly exact linear relationship for periods longer than the year, which can be interpreted as perfect integration of these Euro stock markets at the longest time scales. It also shows that small inconsistencies between Euro markets seem to be just short within-year discrepancies possibly due to the interaction of different agents with different trading horizons.
164 citations
TL;DR: The analysis shows that a random financial network can be more resilient than a scale free one in case of agents’ heterogeneity and which network architecture can make the financial system more resilient to random attacks and how systemic risk spreads over the network.
Abstract: In this paper we develop an interbank market with heterogeneous financial institutions that enter into lending agreements on different network structures. Credit relationships (links) evolve endogenously via a fitness mechanism based on agents’ performance. By changing the agent’s trust on its neighbor’s performance, interbank linkages self-organize themselves into very different network architectures, ranging from random to scale-free topologies. We study which network architecture can make the financial system more resilient to random attacks and how systemic risk spreads over the network. To perturb the system, we generate a random attack via a liquidity shock. The hit bank is not automatically eliminated, but its failure is endogenously driven by its incapacity to raise liquidity in the interbank network. Our analysis shows that a random financial network can be more resilient than a scale free one in case of agents’ heterogeneity.
TL;DR: The DCCA cross-correlation coefficient ρDCCA shows that, in general, the data are influenced by seasonal components.
Abstract: In this paper we propose, analyze and also quantify cross-correlations between climatological data. For this purpose we adopt the DCCA cross-correlation coefficient ρ D C C A . In order to accomplish this goal, we calculate the cross-correlation between time series of air temperature and relative humidity. This analysis was performed taking into account several stations (cities) around the world. The results found here, depending on the station location, may exhibit one of the following behaviors, i.e., negative, positive, or null cross-correlations. It is noteworthy that, the level of cross-correlation between air temperature and relative humidity is quantified in these cases. Finally, DCCA cross-correlation coefficients show that, in general, the data are influenced by seasonal components.
TL;DR: In this article, the use of the Hurst exponent, dynamically computed over a weighted moving time-window, to evaluate the level of stability/instability of financial firms was investigated.
Abstract: We investigate the use of the Hurst exponent, dynamically computed over a weighted moving time-window, to evaluate the level of stability/instability of financial firms Financial firms bailed-out as a consequence of the 2007–2008 credit crisis show a neat increase with time of the generalized Hurst exponent in the period preceding the unfolding of the crisis Conversely, firms belonging to other market sectors, which suffered the least throughout the crisis, show opposite behaviors We find that the multifractality of the bailed-out firms increase at the crisis suggesting that the multi fractal properties of the time series are changing These findings suggest the possibility of using the scaling behavior as a tool to track the level of stability of a firm In this paper, we introduce a method to compute the generalized Hurst exponent which assigns larger weights to more recent events with respect to older ones In this way large fluctuations in the remote past are less likely to influence the recent past We also investigate the scaling associated with the tails of the log-returns distributions and compare this scaling with the scaling associated with the Hurst exponent, observing that the processes underlying the price dynamics of these firms are truly multi-scaling
TL;DR: The analysis shows that the bivariate information flow between world markets is strongly asymmetric with a distinct information surplus flowing from the Asia–Pacific region to both European and US markets.
Abstract: a b s t r a c t In this paper, we quantify the statistical coherence between financial time series by means of the Renyi entropy. With the help of Campbell's coding theorem, we show that the Renyi entropy selectively emphasizes only certain sectors of the underlying empirical distribution while strongly suppressing others. This accentuation is controlled with Renyi's parameter q. To tackle the issue of the information flow between time series, we formulate the concept of Renyi's transfer entropy as a measure of information that is transferred only between certain parts of underlying distributions. This is particularly pertinent in financial time series, where the knowledge of marginal events such as spikes or sudden jumps is of a crucial importance. We apply the Renyian information flow to stock market time series from 11 world stock indices as sampled at a daily rate in the time period 02.01.1990-31.12.2009. Corresponding heat maps and net information flows are represented graphically. A detailed discussion of the transfer entropy between the DAX and S&P500 indices based on minute tick data gathered in the period 02.04.2008-11.09.2009 is also provided. Our analysis shows that the bivariate information flow between world markets is strongly asymmetric with a distinct information surplus flowing from the Asia-Pacific region to both European and US markets. An important yet less dramatic excess of information also flows from Europe to the US. This is particularly clearly seen from a careful analysis of Renyi information flow between the DAX and S&P500 indices.
TL;DR: This paper takes the power and water systems of a major city in China as an example and develops a framework for the analysis of the vulnerability of interdependent infrastructure systems, finding it helpful for the system owners to make better decisions on infrastructure design and protection.
Abstract: Infrastructure systems such as power and water supplies make up the cornerstone of modern society which is essential for the functioning of a society and its economy. They become more and more interconnected and interdependent with the development of scientific technology and social economy. Risk and vulnerability analysis of interdependent infrastructures for security considerations has become an important subject, and some achievements have been made in this area. Since different infrastructure systems have different structural and functional properties, there is no universal all-encompassing ‘silver bullet solution’ to the problem of analyzing the vulnerability associated with interdependent infrastructure systems. So a framework of analysis is required. This paper takes the power and water systems of a major city in China as an example and develops a framework for the analysis of the vulnerability of interdependent infrastructure systems. Four interface design strategies based on distance, betweenness, degree, and clustering coefficient are constructed. Then two types of vulnerability (long-term vulnerability and focused vulnerability) are illustrated and analyzed. Finally, a method for ranking critical components in interdependent infrastructures is given for protection purposes. It is concluded that the framework proposed here is useful for vulnerability analysis of interdependent systems and it will be helpful for the system owners to make better decisions on infrastructure design and protection.
TL;DR: A resampling method is introduced that greatly outperforms the standard approach to characterizing uncertainty with MPLE and is introduced in an application to modeling cosponsorship networks in the United States Senate.
Abstract: Exponential random graph models (ERGMs) are powerful tools for formulating theoretical models of network generation or learning the properties of empirical networks. They can be used to construct models that exactly reproduce network properties of interest. However, tuning these models correctly requires computationally intractable maximization of the probability of a network of interest—maximum likelihood estimation (MLE). We discuss methods of approximate MLE and show that, though promising, simulation based methods pose difficulties in application because it is not known how much simulation is required. An alternative to simulation methods, maximum pseudolikelihood estimation (MPLE), is deterministic and has known asymptotic properties, but standard methods of assessing uncertainty with MPLE perform poorly. We introduce a resampling method that greatly outperforms the standard approach to characterizing uncertainty with MPLE. We also introduce ERGMs for dynamic networks—temporal ERGM (TERGM). In an application to modeling cosponsorship networks in the United States Senate, we show how recently proposed methods for dynamic network modeling can be integrated into the TERGM framework, and how our resampling method can be used to characterize uncertainty about network dynamics.
TL;DR: In this article, the generalized Hurst exponent approach was used to study the multi-scaling behavior of different financial time series, where an apparent increase in multifractality is measured in time series generated from shuffled returns, where all time-correlations are destroyed, while the return distributions are conserved.
Abstract: In this paper, we use the generalized Hurst exponent approach to study the multi-scaling behavior of different financial time series. We show that this approach is robust and powerful in detecting different types of multi-scaling. We observe a puzzling phenomenon where an apparent increase in multifractality is measured in time series generated from shuffled returns, where all time-correlations are destroyed, while the return distributions are conserved. This effect is robust and it is reproduced in several real financial data including stock market indices, exchange rates and interest rates. In order to understand the origin of this effect we investigate different simulated time series by means of the Markov switching multifractal model, autoregressive fractionally integrated moving average processes with stable innovations, fractional Brownian motion and Levy flights. Overall we conclude that the multifractality observed in financial time series is mainly a consequence of the characteristic fat-tailed distribution of the returns and time-correlations have the effect to decrease the measured multifractality.
TL;DR: Based on the daily price data of the Chinese Yuan (RMB)/US dollar exchange rate and the Shanghai Stock Composite Index, Wang et al. as discussed by the authors conducted an empirical analysis of the cross-correlations between the Chinese exchange market and stock market using the multifractal crosscorrelation analysis method.
Abstract: Based on the daily price data of the Chinese Yuan (RMB)/US dollar exchange rate and the Shanghai Stock Composite Index, we conducted an empirical analysis of the cross-correlations between the Chinese exchange market and stock market using the multifractal cross-correlation analysis method. The results demonstrate the overall significance of the cross-correlation based on the analysis of a statistic. Multifractality exists in cross-correlations, and the cross-correlated behavior of small fluctuations is more persistent than that of large fluctuations. Moreover, using the rolling windows method, we find that the cross-correlations between the Chinese exchange market and stock market vary with time and are especially sensitive to the reform of the RMB exchange rate regime. The previous reduction in the flexibility of the RMB exchange rate in July 2008 strengthened the persistence of cross-correlations and decreased the degree of multifractality, whereas the enhancement of the flexibility of the RMB exchange rate in June 2010 weakened the persistence of cross-correlations and increased the multifractality. Finally, several relevant discussions are provided to verify the robustness of our empirical analysis.
TL;DR: Results show that degree-based attacks are more efficient than random attacks on network structural controllability and cascade failures in directed Erdos–Renyi and scale-free networks under attacks and cascading failures.
Abstract: Structural controllability, which is an interesting property of complex networks, attracts many researchers from various fields. The maximum matching algorithm was recently applied to explore the minimum number of driver nodes, where control signals are injected, for controlling the whole network. Here we study the controllability of directed Erdos–Renyi and scale-free networks under attacks and cascading failures. Results show that degree-based attacks are more efficient than random attacks on network structural controllability. Cascade failures also do great harm to network controllability even if they are triggered by a local node failure.
TL;DR: In this paper, the concepts and the applications of Cayley trees and Bethe lattices in statistical mechanics are reviewed in a tentative effort to remove widespread misuse of these simple, but yet important, and different-ideal graphs.
Abstract: We review critically the concepts and the applications of Cayley Trees and Bethe Lattices in statistical mechanics in a tentative effort to remove widespread misuse of these simple, but yet important–and different–ideal graphs. We illustrate, in particular, two rigorous techniques to deal with Bethe Lattices, based respectively on self-similarity and on the Kolmogorov consistency theorem, linking the latter with the Cavity and Belief Propagation methods, more known to the physics community.
TL;DR: Zunino et al. as mentioned in this paper presented the Centro de Investigaciones Opticas of the Consejo Nacional de Investigación Cientifica y Tecnologia Conicet -La Plata, Argentina.
Abstract: Fil: Zunino, Luciano Jose. Consejo Nacional de Investigaciones Cientificas y Tecnicas. Centro Cientifico Tecnologico Conicet - La Plata. Centro de Investigaciones Opticas. Provincia de Buenos Aires. Gobernacion. Comision de Investigaciones Cientificas. Centro de Investigaciones Opticas. Universidad Nacional de La Plata. Centro de Investigaciones Opticas; Argentina. Universidad Nacional de La Plata. Facultad de Ingenieria; Argentina
TL;DR: The analysis on over 50 real-world networks reveals a power-law scaling of network density and size under adequate renormalization technique, yet irrespective of network type and origin, and implies an existence of a scale-free density also within–among different self-similar scales of–complex real- world networks.
Abstract: Despite their diverse origin, networks of large real-world systems reveal a number of common properties including small-world phenomena, scale-free degree distributions and modularity. Recently, network self-similarity as a natural outcome of the evolution of real-world systems has also attracted much attention within the physics literature. Here we investigate the scaling of density in complex networks under two classical box-covering renormalizations–network coarse-graining–and also different community-based renormalizations. The analysis on over 50 real-world networks reveals a power-law scaling of network density and size under adequate renormalization technique, yet irrespective of network type and origin. The results thus advance a recent discovery of a universal scaling of density among different real-world networks [P.J. Laurienti, K.E. Joyce, Q.K. Telesford, J.H. Burdette, S. Hayasaka, Universal fractal scaling of self-organized networks, Physica A 390 (20) (2011) 3608–3613] and imply an existence of a scale-free density also within–among different self-similar scales of–complex real-world networks. The latter further improves the comprehension of self-similar structure in large real-world networks with several possible applications.
TL;DR: It is found that strength and range of anticipation significantly affect pedestrian dynamics, and there is an optimal strength of anticipation to realize the smoothest counter flow.
Abstract: In this paper, we propose the anticipation floor field (AFF) as an extension of the floor field (FF) model, which is one of the successful models in describing pedestrian dynamics. The AFF focuses on non-local interaction between pedestrians, which has not been taken into account in the FF model based on local rules. We have conducted several experiments, as well as simulations of counter flow to show the validity of our model. It is found that strength and range of anticipation significantly affect pedestrian dynamics, and there is an optimal strength of anticipation to realize the smoothest counter flow.
TL;DR: A dynamic time warping method to study the topology of similarity networks among 35 major currencies in international foreign exchange markets, measured by the minimal spanning tree (MST) approach, confirms that USD and EUR are the predominant world currencies.
Abstract: In this study, we employ a dynamic time warping method to study the topology of similarity networks among 35 major currencies in international foreign exchange (FX) markets, measured by the minimal spanning tree (MST) approach, which is expected to overcome the synchronous restriction of the Pearson correlation coefficient. In the empirical process, firstly, we subdivide the analysis period from June 2005 to May 2011 into three sub-periods: before, during, and after the US sub-prime crisis. Secondly, we choose NZD (New Zealand dollar) as the numeraire and then, analyze the topology evolution of FX markets in terms of the structure changes of MSTs during the above periods. We also present the hierarchical tree associated with the MST to study the currency clusters in each sub-period. Our results confirm that USD and EUR are the predominant world currencies. But USD gradually loses the most central position while EUR acts as a stable center in the MST passing through the crisis. Furthermore, an interesting finding is that, after the crisis, SGD (Singapore dollar) becomes a new center currency for the network.
TL;DR: Results from exact calculation of a discrete version and numerical simulations of the continuous version of the model indicate the existence of a universal continuous phase transition at p=pc below which a consensus is reached.
Abstract: We propose a model of continuous opinion dynamics, where mutual interactions can be both positive and negative. Different types of distributions for the interactions, all characterized by a single parameter p denoting the fraction of negative interactions, are considered. Results from exact calculation of a discrete version and numerical simulations of the continuous version of the model indicate the existence of a universal continuous phase transition at p = p c below which a consensus is reached. Although the order–disorder transition is analogous to a ferromagnetic–paramagnetic phase transition with comparable critical exponents, the model is characterized by some distinctive features relevant to a social system.
TL;DR: The experimental results of susceptible-infectious-recovered (SIR) dynamics suggest that the proposed all-around distance can act as a more accurate, stable indicator of influential nodes.
Abstract: Identifying the most influential nodes in complex networks provides a strong basis for understanding spreading dynamics and ensuring more efficient spread of information. Due to the heterogeneous degree distribution, we observe that current centrality measures are correlated in their results of nodes ranking. This paper introduces the concept of all-around nodes, which act like all-around players with good performance in combined metrics. Then, an all-around distance is presented for quantifying the influence of nodes. The experimental results of susceptible-infectious-recovered (SIR) dynamics suggest that the proposed all-around distance can act as a more accurate, stable indicator of influential nodes.
TL;DR: A system of self-moving particles whose motion is governed by the social-force model is employed to investigate the effect of complex building architecture on the uncoordinated crowd motion during urgent evacuation and how the room door size, the size of the main exit, the desired speed and the friction coefficient affect the evacuation time.
Abstract: Panic during emergency building evacuation can cause crowd stampede, resulting in serious injuries and casualties. Agent-based methods have been successfully employed to investigate the collective human behavior during emergency evacuation in cases where the configurational space is extremely simple–usually one rectangular room–but not in evacuations of multi-room or multi-floor buildings. This implies that the effect of the complexity of building architecture on the collective behavior of the agents during evacuation has not been fully investigated. Here, we employ a system of self-moving particles whose motion is governed by the social-force model to investigate the effect of complex building architecture on the uncoordinated crowd motion during urgent evacuation. In particular, we study how the room door size, the size of the main exit, the desired speed and the friction coefficient affect the evacuation time and under what circumstances the evacuation efficiency improves.
TL;DR: This work uses a novel technique, based on one-dimensional cellular automata components, for modelling network infrastructure and its occupancy by vehicles for traffic flow modelling of motorised and non-motorised traffic on urban networks.
Abstract: As ‘greening’ of all aspects of human activity becomes mainstream, transportation science is also increasingly focused around sustainability. Modal co-existence between motorised and non-motorised traffic on urban networks is, in this context, of particular interest for traffic flow modelling. The main modelling problems here are posed by the heterogeneity of vehicles, including size and dynamics, and by the complex interactions at intersections. Herein we address these with a novel technique, based on one-dimensional cellular automata components, for modelling network infrastructure and its occupancy by vehicles. We use this modelling approach, together with a corresponding vehicle behaviour model, to simulate combined car and bicycle traffic for two elemental scenarios—examples of components that would be used in the building of an arbitrary network. Results of simulations performed on these scenarios, (i) a stretch of road and (ii) an intersection causing conflict between cars and bicycles sharing a lane, are presented and analysed.
TL;DR: In this paper, a modified lattice hydrodynamic model of traffic flow is proposed by introducing the density difference between the leading and the following lattice, which leads to the stabilization of the system.
Abstract: A modified lattice hydrodynamic model of traffic flow is proposed by introducing the density difference between the leading and the following lattice. The stability condition of the modified model is obtained through the linear stability analysis. The results show that considering the density difference leads to the stabilization of the system. The Burgers equation and mKdV equation are derived to describe the density waves in the stable and unstable regions respectively. Numerical simulations show that considering the density difference not only could stabilize traffic flow but also makes the lattice hydrodynamic model more realistic.
TL;DR: In this article, the authors analyzed auto-correlations in the absolute returns for each of 30 Dow Jones Industrial Average (DJIA) constituents, S i, and cross-relations between the DJIA and each S i.
Abstract: Employing detrended fluctuation analysis (DFA) and detrended cross-correlations analysis (DCCA), we analyze auto-correlations in the absolute returns for each of 30 Dow Jones Industrial Average (DJIA) constituents, S i , and cross-correlations in the absolute returns between the DJIA and each S i . We find that each DJIA member follows the DJIA in absolute returns, since the DCCA curve for each pair ( S i , DJIA i ) exhibits strong cross-correlations, with average DCCA exponent 〈 λ 〉 = 1.03 ± 0.04 . This value for 〈 λ 〉 implies that the power-law cross-correlations are of the 1 / f functional form. For the financial firms comprising the DJIA, we also find that the DFA and DCCA exponents controlling the duration of firm risk are somewhat larger than the corresponding values for the rest of the US financial industry.