Traffic of packets with non-homogeneously selected destinations in scale-free network

In this paper, we study the information traffic flow in communication networks with scale-free topology. We consider the situation arising when packets are delivered to non-homogeneously selected destinations. It is found that the network capacity R_c increases with the increase of 〈k〉 (average degree of destination nodes) under local routing strategy. In contrast, R_c is essentially independent of 〈k〉 under shortest path strategy. Based on this finding, an integrated routing strategy that can enhance network capacity is proposed by combining the two strategies.     

A lattice traffic model with consideration of preceding mixture traffic information

In this paper,the lattice model is presented,incorporating not only site information about preceding cars but also relative currents in front.We derive the stability condition of the extended model by considering a small perturbation around the homogeneous flow solution and find that the improvement in the stability of traffic flow is obtained by taking into account preceding mixture traffic information.Direct simulations also confirm that the traffic jam can be suppressed efficiently by considering the relative currents ahead,just like incorporating site information in front.Moreover,from the nonlinear analysis of the extended models,the preceding mixture traffic information dependence of the propagating kink solutions for traffic jams is obtained by deriving the modified KdV equation near the critical point using the reductive perturbation method.     

基于复杂网络的交通拥堵与传播动力学分析

本文运用改进的中观交通流模型,研究了网络拓扑结构对交通拥堵的影响,进而分析复杂网络上的交通传播动力学特征和传播规律.结论有助于提出消除交通拥堵的有效控制策略以及交通规划的合理方案. 关键词： 复杂网络 交通流 交通拥堵 传播特性     

Effects of the cascading failures on scale-free traffic networks

In this paper, we consider the artificial scale-free traffic network with dynamic weights (cost) and focus on how the removal strategies (flow-based removal, betweenness-based removal and mix-based removal) affect the damage of cascading failures based on the user-equilibrium (UE) assignment, which ensures the balance of flow on the traffic network. Experiment simulation shows that different removal strategies can bring large dissimilarities of the efficiency and damage after the intentional removal of an edge. We show that the mix-based removal of a single edge might reduce the damage of cascading failures and delay the breakdown time, especially for larger reserve capacity coefficient α. This is particularly important for real-world networks with a highly hetereogeneous distribution of flow, i.e., traffic and transportation networks, logistics networks and electrical power grids.     

Behavior patterns of online users and the effect on information filtering

Understanding the structure and evolution of web-based user-item bipartite networks is an important task since they play a fundamental role in online information filtering. In this paper, we focus on investigating the patterns of online users’ behavior and the effect on recommendation process. Empirical analysis on the e-commercial systems show that users’ taste preferences are heterogeneous in general but their interests for niche items are highly clustered. Additionally, recommendation processes are investigated on both the real networks and the reshuffled networks in which real users’ behavior patterns can be gradually destroyed. We find that the performance of personalized recommendation methods is strongly related to the real network structure. Detailed study on each item shows that most hot items are accurately recommended and their recommendation accuracy is robust to the reshuffling process. However, the accuracy for niche items is relatively low and drops significantly after removing users’ behavior patterns. Our work is also meaningful in practical sense since it reveals an effective direction to improve the accuracy and the robustness of the existing recommender systems.     

Scale-free properties of information flux networks in genetic algorithms

In this study, we present empirical analysis of statistical properties of mating networks in genetic algorithms (GAs). Under the framework of GAs, we study a class of interaction network model—information flux network (IFN), which describes the information flow among generations during evolution process. The IFNs are found to be scale-free when the selection operator uses a preferential strategy rather than a random. The topology structure of IFN is remarkably affected by operations used in genetic algorithms. The experimental results suggest that the scaling exponent of the power-law degree distribution is shown to decrease when crossover rate increases, but increase when mutation rate increases, and the reason may be that high crossover rate leads to more edges that are shared between nodes and high mutation rate leads to many individuals in a generation possessing low fitness. The magnitude of the out-degree exponent is always more than the in-degree exponent for the systems tested. These results may provide a new viewpoint with which to view GAs and guide the dissemination process of genetic information throughout a population.     

Effective usage of shortest paths promotes transportation efficiency on scale-free networks

With rapid economic and social development, the problem of traffic congestion is getting more and more serious. Accordingly, network traffic models have attracted extensive attention. In this paper, we introduce a shortest-remaining-path-first queuing strategy into a network traffic model on Barabási–Albert scale-free networks under efficient routing protocol, where one packet’s delivery priority is related to its current distance to the destination. Compared with the traditional first-in-first-out queuing strategy, although the network capacity has no evident changes, some other indexes reflecting transportation efficiency are significantly improved in the congestion state. Extensive simulation results and discussions are carried out to explain the phenomena. Our work may be helpful for the designing of optimal networked-traffic systems.     

优先随机慢化及预测间距对交通流的影响

在周期性边界条件下，通过引入预测车辆间距对Noise-First模型进行改进，数值模拟了不同参数下的交通情形. 研究表明：引入预测间距的Noise-First模型更能真实地反映实际交通状况，不仅再现了启止波、同步流，而且出现了亚稳态，非平衡相变等与实际道路交通一致的非线性现象. 关键词： 交通流 元胞自动机 同步流 亚稳态     

基于感知流量算法的复杂网络拥塞问题研究

研究了在具有感知流量的路由策略下,复杂网络的拓扑结构对网络中传输流量的影响.为了描述数据包传输过程的有效性,通过引入一个状态参数,利用由稳态到拥塞的指标流量相变值来刻画网络的吞吐量.基于每个节点的数据包处理能力与该节点的度或介数成比例提出两种模型并进行仿真.仿真结果表明,平均度相同的情况下,模型Ⅰ中,WS小世界网络比ER随机网络和BA无标度网络更容易产生拥塞;模型Ⅱ中,所有网络容量都得到较大的提高,尤其是WS小世界网络.但当网络的基本连接参数改变时,哪种模型更利于网络的流量传输,还要依据网络本身的结构特性 关键词： 复杂网络 无标度网络 感知流量 拥塞     

The effect of moving bottlenecks on a two-lane traffic flow

In this paper, we study the effect of moving bottlenecks on traffic flow. The full velocity difference （FVD） model is extended to the traffic flow on a two-lane highway, and new lane changing rule is proposed to reproduce the vehicular lane changing behavior. Using this model, we derive the fundamental current-density diagrams for the traffic flow with the effect of moving bottleneck. Moreover, typical time-space diagram for a two-lane highway shows the formation and dissipation of a moving bottleneck. Results demonstrate that the effect of moving bottleneck enlarges with the increase of traffic density, but the effect can be reduced by increasing the maximum velocity of heavy truck. The effects of multiple moving bottlenecks under different conditions are investigated. The effect becomes more remarkable when the coupling effect of multiple moving bottlenecks occurs.     

最相邻后车综合信息对交通流不稳定性的影响分析

基于抑制交通流不稳定性所需的条件,分析司机后视获得的最相邻后车综合信息对交通流不稳定性的影响. 在司机关注前车信息概率大于关注后车信息概率与司机敏感系数大于0的现实条件下,解析分析以及仿真分析得到了以下结论:1)最相邻后车车头距信息减小了交通流的不稳定性,且关注概率越大,减小作用越大;跟驰车与最相邻后车的速度差信息增加了交通流的不稳定性,且关注概率越大,增加作用越大. 2)最相邻后车综合信息对交通流的不稳定性的减小作用大于增加作用,即最相邻后车综合信息减小了交通流的不稳定性. 3)司机距离差敏感系数越大,最相邻后车综合信息对交通流的不稳定性减小作用越大. 4)司机速度差敏感系数越大,最相邻后车综合信息对交通流的不稳定性增加作用越大. 关键词： 交通堵塞 交通流不稳定性 最相邻后车综合信息 跟驰模型     

含重叠路段交通系统中信息反馈策略的比较研究

智能交通系统可以实时反馈路况信息、诱导出行,进而缓解交通拥堵.在时间反馈和平均速度反馈两种信息服务策略下,考虑重叠路段的影响,利用元胞自动机模型对人们的路径选择行为进行了模拟.结果表明,在含有重叠路段的网络中,相对时间反馈策略而言,平均速度反馈策略可以更有效地降低人们的出行时间,其优势在重叠部分长度占路径长度的比例很小和较大时十分明显,且随路径长度的增加而上升.在提高路网的利用效率方面,平均速度反馈策略的优势随路径长度的增加而逐渐下降,随重叠部分长度的增加而先降后升. 关键词： 交通流 元胞自动机 智能交通系统 信息反馈策略     

智能交通灯对交叉路口交通流的影响

以改进的NaSch模型为基础,采用开放边界条件,分别对普通交通灯和智能交通灯控制下的十字交叉路口交通流进行模拟.结果表明,主干道车辆较多时,智能交通灯控制下的主干道车辆平均速度和流量都比普通交通灯控制下高,并可减少车辆的无谓等待时间. 关键词： 元胞自动机 交叉路口 智能交通灯 NaSch模型     

交通灯控制下主干道的交通流研究

用元胞自动机模型模拟二维交通流.通过交叉口设置的红绿灯，研究交通激波的形成和传播 ；对于一定的红绿灯周期，交通流量出现多个极值现象；在交叉口间隔相同的情况下，对于 一定的红绿灯周期，在一定的车辆密度范围内，交通流量是一个与密度无关的常量；在车辆 密度较高的情况下，交叉口间距大于某一值后，交通流量保持恒定值. 关键词： 元胞自动机模型 交通流 交通激波     

混合状态下城市快速路交通流短时预测

建立交通流短时预测状态空间模型, 研究混合状态下城市快速路交通流短时预测. 结合城市快速路自由流状态、拥挤流状态和阻塞流状态下交通流参数的时间和空间分布特性, 基于交通流守恒方程和速度动态模型, 借鉴偏微分方程组求解时空离散的思想, 建立三种状态下交通流短时预测模型; 同时考虑进出口匝道、车道数变更以及道路坡度等因素的影响, 将交通流短时预测模型转化为交通流短时预测状态空间模型, 实现混合状态下交通流短时预测. 研究表明, 该方法能够实现混合状态下道路网内的交通流短时预测, 预测精度可达90.23%. 相同条件下, 经典自回归滑动平均模型的预测精度仅为81%. 关键词： 交通流短时预测 自由流状态 拥挤流状态 阻塞流状态     

基于预期流优化的空间网络引力模型

从优化预期流的角度提出了一个空间网络模型,其构建目标是使得整个网络的预期流总和最大化.通过改变表征网络对节点适应度和对地理因素依赖程度的两个参数α和γ的取值,网络的拓扑可从异质的星形类网络逐渐演变成同质的类似道路网的网络.利用所提出的引力模型仿真了中国城市航空网,并将其统计特征与实际网络进行了比较.最后,讨论了预期流和实际流（网络中实际的边权）之间的关系. 关键词： 空间网络 预期流 引力 中国城市航空网     

考虑延迟概率因素对混合车辆敏感驾驶交通流模型的研究

在交通流NS模型的基础上，考虑混合车辆之间存在的速度差异，不同车辆的驾驶员在对前车的敏感驾驶随机减速行为过程中其延迟概率是不同的，从而提出了一维多速混合车辆敏感驾驶元胞自动机交通流模型. 通过计算机数值模拟得到了混合车辆在不同参数下的 基本图.结果表明，与NS模型、SDNS模型相比，道路交通流量有较大的提高，而且还展现出 了亚稳态、相分离等复杂的实际交通行为现象.结合实际情况，对混合交通的特性进行了分析和讨论. 关键词： 元胞自动机 混合交通流模型 亚稳态 相分离 计算机模 拟     

考虑网络流量的无标度网络病毒免疫策略研究

考虑网络交通流量对病毒传播行为的影响,基于平均场理论研究无标度网络上的病毒免疫策略,提出一种改进的熟人免疫机理.理论分析表明,在考虑网络交通流量影响的情况下,当免疫节点密度较小时,随机免疫几乎不能降低病毒的传播速率,而对网络实施目标免疫则能够有效抑制病毒的传播,并且选择度最大的节点进行免疫与选择介数最大的节点进行免疫的效果基本相同.研究还发现,对于网络全局信息未知的情况,与经典熟人免疫策略相比,所提出的免疫策略能够获得更好的免疫效果.通过数值仿真对理论分析进行了验证. 关键词： 无标度网络 病毒传播 交通流量 免疫策略