A Two-Lane Cellular Automata Model with Influence of Next-Nearest Neighbor Vehicle

In this paper, we propose a new two-lane cellular automata model in which the influence of the next-nearest neighbor vehicle is considered. The attributes of the traffic system composed of fast-lane and slow-lane are investigated by the new traffic model. The simulation results show that the proposed two-lane traffic model can reproduce some traffic phenomena observed in real traffic, and that maximum flux and critical density are close to the field measurements.Moreover, the initial density distribution of the fast-lane and slow-lane has much influence on the traffic flow states.With the ratio between the densities of slow lane and fast lane increasing the lane changing frequency increases, but maximum flux decreases. Finally, the influence of the sensitivity coefficients is discussed.     

A novel macroscopic traffic model based on generalized optimal velocity model

In this paper, we adopt the coarse graining method proposed by Lee H K et al. to develop a macroscopic model from the microscopic traffic model-GOVM. The proposed model inherits the parameter p which considers the influence of next-nearest car introduced in the GOVM model. The simulation results show that the new model is strictly consistent with the former microscopic model. Using this macroscopic model, we can avoid considering the details of each traffic on the road, and build more complex models such as road network model easily in the future.     

Dynamics-driven evolution to structural heterogeneity in complex networks

The mutual influence of dynamics and structure is a central issue in complex systems. In this paper we study by simulation slow evolution of network under the feedback of a local-majority-rule opinion process. If performance-enhancing local mutations have higher chances of getting integrated into its structure, the system can evolve into a highly heterogeneous small-world with a global hub (whose connectivity is proportional to the network size), strong local connection correlations and power-law-like degree distribution. Networks with better dynamical performance are achieved if structural evolution occurs much slower than the network dynamics. Structural heterogeneity of many biological and social dynamical systems may also be driven by various dynamics-structure coupling mechanisms.     

A weighted network evolution with traffic flow

In this paper, we propose a simple weighted network model that generalizes the complex network model evolution with traffic flow previously presented to investigate the relationship between traffic flow and network structure. In the model, the nodes in the network are represented by the traffic flow states, the links in the network are represented by the transform of the traffic flow states, and the traffic flow transported when performing the transform of the traffic flow states is considered as the weight of the link. Several topological features of this generalized weighted model, such as the degree distribution and strength distribution, have been numerically studied. A scaling behavior between the strength and degree s∼klogk is obtained. By introducing some constraints to the generalized weighted model, we study its subnetworks and find that the scaling behavior between the strength and degree is conserved, though the topology properties are quite sensitive to the constraints.     

Phase Diagram of the Antiferromagnetic Heisenberg Model on a Cubic Lattice

Phase transitions in the antiferromagnetic Heisenberg model on a cubic lattice with intralayer next-nearest neighbor interactions are studied using the replica Monte Carlo algorithm. The magnitude of next-nearest neighbor interactions varies in the range of 0.0 ≤ r ≤ 1.0. The characteristics of the phase transitions are analyzed by the histogram and Binder cumulant techniques. The phase diagram relating the transition temperature and the magnitude of next-nearest neighbor interactions is constructed. It is shown that a second order phase transition occurs in the r range under study. In this model, it is found that the intralayer next-nearest neighbor interactions do not change the order of the phase transition.

     

Pareto and Boltzmann-Gibbs behaviors in a deterministic multi-agent system

A deterministic system of interacting agents is considered as a model for economic dynamics. The dynamics of the system is described by a coupled map lattice with nearest neighbor interactions. The evolution of each agent results from the competition between two factors: the agent’s own tendency to grow and the environmental influence that moderates this growth. Depending on the values of the parameters that control these factors, the system can display Pareto or Boltzmann-Gibbs statistical behaviors in its asymptotic dynamical regime. The regions where these behaviors appear are calculated on the space of parameters of the system. Other statistical properties, such as the mean wealth, the standard deviation, and the Gini coefficient characterizing the degree of equity in the wealth distribution are also calculated.     

On the critical packet injection rate of a preferential next-nearest neighbor routing traffic model on Barabási-Albert networks

Recently, Yin et al. [Eur. Phys. J. B 49, 205 (2006)] introduced an efficient small-world network traffic model using preferential next-nearest neighbor routing strategy with the so-called path iteration avoidance (PIA) rule to study the jamming transition of internet. Here we study their model without PIA rule by a mean-field analysis which carefully divides the message packets into two types. Then, we argue that our mean-field analysis is also applicable in the presence of PIA rule in the limit of a large number of nodes in the network. Our analysis gives an explicit expression of the critical packet injection rate R_c as a function of a bias parameter of the routing strategy α in their model with or without PIA rule. In particular, we predict a sudden change in R_c at a certain value of α. These predictions agree quite well with our extensive computer simulations.     

有限理性视野下网络交通流逐日演化规律研究

基于有限理性的前提假设,建立了"有限理性二项logit"模型,用以描述出行者路径选择行为,并以一个由2条路径构成的路网为例,探讨了理性程度不同群体的网络交通流逐日动态演化过程,通过数值实验,分析了网络交通流的演化特征,发现网络交通流演化最终状态除了与出行者群体对费用的敏感程度、对实际费用的依赖程度有关,还与出行者群体的理性程度有很大关系.在一定的情况下,出行者理性程度很高或很低均可以使系统稳定,而恰恰是理性程度一般的群体不容易达到稳定.     

Fractal Aggregation with Next-Nearest Neighbor Interactions in External Fields

In this paper, we present a particle-cluster model for a two-dimensional fractal aggregation with the nearest neighbor and next-nearest neighbor interactions in external fields. The external field is described by the probability P. Our computer simulation results show that the threshold concentration is P_c^a = 0.59 ± 0.02 and the fractal dimension is D_c = 1.67 ± 0.03 at this concentration. In comparison with previous results with nearest neighbor interaction only, we find that D_c is the same, but P_c^a is smaller, which is reasonable, since the connectivity has been changed significantly.     

具有双峰特性的双层超网络模型

随着社会经济的快速发展,社会成员及群体之间的关系呈现出了更复杂、更多元化的特点.超网络作为一种描述复杂多元关系的网络,已在不同领域中得到了广泛的应用.服从泊松度分布的随机网络是研究复杂网络的开创性模型之一,而在现有的超网络研究中,基于ER随机图的超网络模型尚属空白.本文首先在基于超图的超网络结构中引入ER随机图理论,提出了一种ER随机超网络模型,对超网络中的节点超度分布进行了理论分析,并通过计算机仿真了在不同超边连接概率条件下的节点超度分布情况,结果表明节点超度分布服从泊松分布,符合随机网络特征并且与理论推导相一致.进一步,为更准确有效地描述现实生活中的多层、异质关系,本文构建了节点超度分布具有双峰特性,层间采用随机方式连接,层内分别为ER-ER,BA-BA和BA-ER三种不同类型的双层超网络模型,理论分析得到了三种双层超网络节点超度分布的解析表达式,三种双层超网络在仿真实验中的节点超度分布均具有双峰特性.     

Evolutionary games on scale-free networks with a preferential selection mechanism

Considering the heterogeneity of individuals’ influence in the real world, we introduce a preferential selection mechanism to evolutionary games (the Prisoner’s Dilemma Game and the Snowdrift Game) on scale-free networks and focus on the cooperative behavior of the system. In every step, each agent chooses an individual from all its neighbors with a probability proportional to k^α indicating the influence of the neighbor, where k is the degree. Simulation results show that the cooperation level has a non-trivial dependence on α. To understand the effect of preferential selection mechanism on the evolution of the system, we investigate the time series of the cooperator frequency in detail. It is found that the cooperator frequency is greatly influenced by the initial strategy of hub nodes when α>0. This observation is confirmed by investigating the system behavior when some hub nodes’ strategies are fixed.     

Role of connectivity in congestion and decongestion in a communication network

We study network traffic dynamics in a two dimensional communication network with regular nodes and hubs. If the network experiences heavy message traffic, congestion occurs due to finite capacity of the nodes. We discuss strategies to manipulate hub capacity and hub connections to relieve congestion and define a coefficient of betweenness centrality (CBC), a direct measure of network traffic, which is useful for identifying hubs which are most likely to cause congestion. The addition of assortative connections to hubs of high CBC relieves congestion very efficiently. An erratum to this article is available at .     

Urban road network evolution mechanism based on the ‘direction preferred connection’ and ‘degree constraint’

The urban road network is a complex system that exhibits the properties of self-organization and emergence. Recent theoretical and empirical studies have mainly focused on the structural properties of the urban road networks. This research concentrates on some important parameters such as degree, average degree, meshedness coefficient, betweeness, etc. These parameters of the real road network exhibit specific statistical properties. Some studies show that perhaps these specific statistical properties are caused by a compromise mechanism of the formation of a minimum spanning tree and the greedy triangulation. Inspired by these results, we propose a principle to construct the network (we call it a MG network in this paper) whose structure is located between the minimum spanning tree and the greedy triangulation at first. The structural properties of the MG network are analyzed. We find the formation mechanism of the MG network cannot explain the urban road network evolution well. Then, based on the formation mechanism of the MG network, we add the ‘direction preferred connection’ and ‘degree constraint’ principles to the urban road network evolution simulation process. The result of the simulation network turns out to be a planar network that is in accordance with reality. Compared with the real road network’s structural properties, we find the simulation results are so consistent with it. It indicates the validation of the model and also demonstrates perhaps the ‘direction preferred connection’ and ‘degree constraint’ principle can explain the urban road network evolution better.     

Phase transitions in the antiferromagnetic Heisenberg model on a layered triangular lattice with the next-nearest neighbor interactions

Phase transitions in the three-dimensional antiferromagnetic Heisenberg model on a layered triangular lattice with the next-nearest neighbor interactions have been studied by the histogram Monte Carlo method. Phase transitions in this model have been studied in the range of the next-nearest neighbor interactions from 0.0 to 1.0. The first-order phase transition has been revealed in the considered interval in the studied model.     

The effects of degree correlations on network topologies and robustness

Complex networks have been applied to model numerous interactive nonlinear systems in the real world. Knowledge about network topology is crucial to an understanding of the function, performance and evolution of complex systems. In the last few years, many network metrics and models have been proposed to investigate the network topology, dynamics and evolution. Since these network metrics and models are derived from a wide range of studies, a systematic study is required to investigate the correlations among them. The present paper explores the effect of degree correlation on the other network metrics through studying an ensemble of graphs where the degree sequence (set of degrees) is fixed. We show that to some extent, the characteristic path length, clustering coefficient, modular extent and robustness of networks are directly influenced by the degree correlation.     

Scale-Free Properties in Traffic System

In this work, we propose a new model of evolution networks, which is based on the evolution of the traffic flow. In our method, the network growth does not take into account preferential attachment, and the attachment of new node is independent of the degree of nodes. Our aim is that employing the theory of evolution network, we give a further understanding about the dynamical evolution of the traffic flow. We investigate the probability distributions and scaling properties of the proposed model The simulation results indicate that in the proposed model, the distribution of the output connections can be well described by scale-free distribution. Moreover, the distribution of the connections is largely related to the traffic flow states, such as the exponential distribution （i.e., the scale-free distribution） and random distribution etc.     

准一维强关联Zigzag型材料的自旋动力学

在t-J模型和Fermion-spin理论框架下，研究了准一维强关联Zigzag型材料的自旋动力学.说明了当掺杂一定时，随着次近邻耦合强度的增大，系统出现公度到非公度的转变，而对于耦合强度一定的情况，随着掺杂浓度的增大，系统亦发生从公度到非公度的转变，并且非公度峰的权重随能量的增大而降低. 关键词： t-J模型')" href="#">t-J模型 Fermion-spin理论 Zigzag型材料     

The Stability of Multi-modal Traffic Network

There is an explicit and implicit assumption in multimodal traffic equilibrium models, that is, if the equilibrium exists, then it will also occur. The assumption is very idealized; in fact, it may be shown that the quite contrary could happen, because in multimodal traffic network, especially in mixed traffic conditions the interaction among traffic modes is asymmetric and the asymmetric interaction may result in the instability of traffic system. In this paper, to study the stability of multimodal traffic system, we respectively present the travel cost function in mixed traffic conditions and in traffic network with dedicated bus lanes. Based on a day-to-day dynamical model, we study the evolution of daily route choice of travelers in multimodal traffic network using 10000 random initial values for different cases. From the results of simulation, it can be concluded that the asymmetric interaction between the cars and buses in mixed traffic conditions can lead the traffic system to instability when traffic demand is larger. We also study the effect of travelers' perception error on the stability of multimodal traffic network. Although the larger perception error can alleviate the effect of interaction between cars and buses and improve the stability of traffic system in mixed traffic conditions, the traffic system also become instable when the traffic demand is larger than a number. For all cases simulated in this study, with the same parameters, traffic system with dedicated bus lane has better stability for traffic demand than that in mixed traffic conditions. We also find that the network with dedicated bus lane has higher portion of travelers by bus than it of mixed traffic network. So it can be concluded that building dedicated bus lane can improve the stability of traffic system and attract more travelers to choose bus reducing the traffic congestion.     

The Stability of Multi-modal Traffic Network

There is an explicit and implicit assumption in multimodal traffic equilibrium models, that is, if the equilibrium exists, then it will also occur. The assumption is very idealized; in fact, it may be shown that the quite contrary could happen, because in multimodal traffic network, especially in mixed traffic conditions the interaction among traffic modes is asymmetric and the asymmetric interaction may result in the instability of traffic system. In this paper, to study the stability of multimodal traffic system, we respectively present the travel cost function in mixed traffic conditions and in traffic network with dedicated bus lanes. Based on a day-to-day dynamical model, we study the evolution of daily route choice of travelers in multimodal traffic network using 10000 random initial values for different cases. From the results of simulation, it can be concluded that the asymmetric interaction between the cars and buses in mixed traffic conditions can lead the traffic system to instability when traffic demand is larger. We also study the effect of travelers' perception error on the stability of multimodal traffic network. Although the larger perception error can alleviate the effect of interaction between cars and buses and improve the stability of traffic system in mixed traffic conditions, the traffic system also become instable when the traffic demand is larger than a number. For all cases simulated in this study, with the same parameters, traffic system with dedicated bus lane has better stability for traffic demand than that in mixed traffic conditions. We also find that the network with dedicated bus lane has higher portion of travelers by bus than it of mixed traffic network. So it can be concluded that building dedicated bus lane can improve the stability of traffic system and attract more travelers to choose bus reducing the traffic congestion.     

Scale-Free Properties in Traffic System

In this work, we propose a new model of evolution networks, which is based on the evolution of the traffic flow. In our method, the network growth does not take into account preferential attachment, and the attachment of new node is independent of the degree of nodes. Our aim is that employing the theory of evolution network, we give a further understanding about the dynamical evolution of the traffic flow. We investigate the probability distributions and scaling properties of the proposed model. The simulation results indicate that in the proposed model, the distribution of the output connections can be well described by scale-free distribution. Moreover, the distribution of the connections is largely related to the traffic flow states, such as the exponential distribution (i.e., the scale-free distribution) and random distribution etc.