复杂网络在城市公交网络中的实证分析

实证分析是复杂网络研究的一个重要的方向。采用复杂网络研究方法,以上海、北京等城市的公交线路的部分站点和路线为例,分别从公交停靠站点网络、公交换乘网络和公交线路网络角度总结了城市公交网络的复杂网络特性。对复杂网络的静态特征值如平均路径长度、聚类系数、节点度分布等方面进行了统计。结果显示北京和上海的公交网络具有小世界特性,度分布都符合指数分布。北京和上海居民外出的平均换乘次数分别为1.54次和1.9次。     

贵阳公共交通网络复杂性分析

城市公共交通系统可以抽象为由公交线路和停靠站点构成的网络,公交网络构成了一个典型的复杂网络.本文以贵阳市的公共汽车交通系统中的停靠站点网络为研究对象,对其进行了复杂性分析,计算了网络的度分布、平均路径长度、聚集系数等指标.结果表明,贵阳市公共交通网络的度分布符合幂率分布,并且具有较小的平均路径长度和较高的聚集系数.从而证实贵阳市公交网络的小世界特性和无标度特性.     

基于Space-P复杂网络模型的城市公交网络特性分析

针对城市公交网络中换乘网络的整体性能分析问题,提出一种基于复杂网络理论的分析方法。首先,基于图论思想,将公交网络建模成由Space-P方法表示的公交换乘网络拓扑模型;然后,统计分析了公交换乘网络的度分布、平均最短路径长度、聚类系数、紧密中心性和介数中心性等特性。以北京市的公交网络为例进行了相关分析,从宏观角度说明北京公交网络具有小世界网络特点,市民出行需要换乘的概率较大,但换乘较为便捷;同时,给出了相关站点的具体地理信息,为公交规划部门优化公交网络提供了参考。     

济南市公共交通网络的拓扑特性研究

采用复杂网络的研究方法,运用图论及相应的网络分析工具对济南市公交站点网络和公交换乘网络进行了实证研究.对网络静态几何量进行了统计,并以此为依据分析了公交网络的拓扑结构特性,揭示了公交网络的复杂性及其演化机理.研究结果表明,两种公交网络的拓扑结构模型都具有无标度网络特性.其中公交换乘网络还具有小世界特性.此项研究结果对于优化公交线路,解决交通问题等提出了一些建议.     

城市公交复杂网络研究

文中基于复杂网络理论,以公交停靠站点为节点,公交线路作为网络边,构成一个复杂的城市公交网络,研究该城市公交网络上的一些统计特征,就网络的度分布、介数、平均路径长度、聚类系数进行计算并对其进行复杂性分析.结果表明:该城市公共交通网络具有幂率分布特征下的度分布,且具有较小的平均路径长度和较高的聚类系数,说明该城市公共交通网络符合复杂网络无尺度特性,可揭示网络中的Hub节点,加以防范保护,为城市交通系统的正常运转和维护提供科学性指导.     

基于复杂网络的城市公共交通网络研究

将北京、上海和杭州3个大城市的公共交通网络(常规公交和快速公交)抽象成复杂网络,结合网络图论思想,把公交站点作为节点,站点间的连线作为边,在大量统计数据的基础上,通过Space L和Space P方法研究3大城市的复杂网络特性。统计分析表明,3个城市的公交网络均具有较小的平均路径长度,即典型的小世界特性。其节点的度分布,在Space L方法的描述下具有无标度特性,在Space P方法的描述下具有指数分布特性。通过对Space L和Space P两种描述方法的比较,可以发现对于同样的公交网络,Space P方法描述的网络具有更大的聚类系数和更小的平均路径长度,即具有更强的小世界效应。     

基于关联的城市公交换乘查询算法

公交换乘是旅游信息系统中的热点问题。当前流行的GIS软件不能人性化地直接解决公交换乘的问题，在对现有的城市公交运行线路、站点分布和城市道路网络特点进行了研究之后，结合数据库技术和Web技术，针对大型城市中，由于公交线路多，公交换乘查询中数据量大，算法复杂的问题，设计了一个包含公交数据表、数据关系和算法步骤的关系型查询分析解决方案。     

基于WebGIS的公交查询信息系统研究

本文通过分析实际的公交乘车过程,将双层公交网络模型划分为几何网络和逻辑网络,定义公交线路和站点的空间类和换乘查询所需的属性类以及表达站点之间的连通关系,并基于Web GIS与Java Script/Ajax技术以及SQL Server 2000数据库技术设计并开发了一个公交查询信息系统,该系统实现了站点查询、线路查询、换乘查询等基本功能,并且提供了地图显示、漫游和缩放等功能。     

基于节点可达度的公交多路径搜索算法

针对公交网络路径搜索问题,以复杂网络的角度进行了相关研究。根据出行者实际需求,提出一种基于节点可达度的公交多路径搜索算法。采用复杂二分网络模型来描述公交网络,将公交线路和公交站点分别看做一类节点,每条公交线路与它所经过的公交站点之间存在连边;在分析网络社团结构的基础上定义了节点可达度,算法根据节点可达度逐步搜索直至目的节点,搜索过程保留可能存在的多条最佳路径。实验结果表明,该方法能够得到最小换乘的多条有效路径。     

数据库系统中公交网络换乘线路的优化选择模型

本文给出了超大城市公交网络换乘线路的优化选择模型。以北京市为实例,运用动态SQL查询技术．在数据库SQL Server中,实现了公交线路换乘查询系统。求出了北京市公交网络的换乘次数上确界,可以查询出任意两个站点之间的换乘线路,给出了上下行线路的处理模型,优化了查询能力,具有实用性。     

家庭自动化网络中的无线网络技术

家庭自动化网络是伴随着信息时代的到来应运而生的一种新产品。本文主要介绍了最新无线网络技术在家庭自动化网络中的应用 ,并对未来数字家居的发展进行了展望     

Interconnection Networks

Interconnection networks are a natural result of advances in computer technology that brought about demands for improved system performance. As computer systems evolved from the batch-processing models of the 1960's to the time-sharing models of the 1970's, their evolution was basically confined within the von Neumann architectural model, with hardware costs being a significant limiting factor. However, contemporary IC technology is creating an entirely new atmosphere; it is now economically feasible to construct a multiple-processor computer system by interconnecting a large number of off-the-shelf processor and memory modules. In addition, because of today's increased performance requirements, the number of functional modules (homogeneous or heterogeneous) in the multiple-processor system normally keeps rising as the domain of applications grows. Trying to counter this trend presents quite a challenge to computer architects.     

机会网络

大量具备短距离通信能力的智能设备的出现推动了无线自组网应用的迅速发展.但在许多实际应用环境中,节点移动、网络稀疏或信号衰减等各种原因通常导致形成的网络大部分时间不连通.传统的移动自组织网络传输模式要求通信源和目标节点之间存在至少一条完整的路径,因而无法在这类环境中运行.机会网络利用节点移动形成的通信机会逐跳传输消息,以"存储-携带-转发"的路由模式实现节点间通信,这种完全不同于传统网络通信模式的新兴组网方式引起了研究界极大的兴趣.首先介绍机会网络的概念和理论基础,并给出了当前机会网络的一些典型应用,然后详细阐述了机会网络研究的热点问题,包括机会转发机制、移动模型和基于机会通信的数据分发和检索等,并简要叙述了机会网络的通信中间件、协作和安全机制以及机会网络新的应用等其他研究问题,最后进行总结并展望了机会网络未来一段时间内的研究重点.     

Functional Networks

In this letter we present functional networks. Unlike neural networks, in these networks there are no weightsassociated with the links connecting neurons, and the internal neuron functions are not fixed but learnable. These functions are not arbitrary, but subject to strong constraints to satisfy the compatibility conditions imposed by the existence of multiple links going from the last input layer to the same output units. In fact, writing the values of the output units in different forms, by considering these different links, a system of functional equations is obtained. When this system is solved, the numberof degrees of freedom of these initially multidimensional functions is considerably reduced. One example illustrates the process and shows that multidimensional functions can be reduced to functions with a single argument. To learn the resulting functions, a method based on minimizing a least squares error function is used, which, unlike the functions used in neural networks, has a single minimum.     

Assortifying Networks

Since its introduction, the concept of assortativity has proved to be a fundamental metric for understanding the structure and function of complex networks. It has been shown to have a significant impact on many processes on networks, including epidemic thresholds, spreading, and longevity, congestion relief, and information cascades. In a number of these results, the degree distribution (usually a power-law distribution) plays a critical role. We describe a simple but effective method for modifying a given network so as to either increase or decrease its assortativity while preserving the degree distribution of the network. The process is easily controlled to yield desired assortativities. A modification is given which not only preserves the degree of every vertex but also respects a given community structure on the network. Both algorithms are supported by detailed empirical results. The constructions should be of particular value to investigators seeking to measure the impact of assortativity in various applications without disturbing the overall degree distribution or community decompositions.     

Support-Vector Networks

Thesupport-vector network is a new learning machine for two-group classification problems. The machine conceptually implements the following idea: input vectors are non-linearly mapped to a very high-dimension feature space. In this feature space a linear decision surface is constructed. Special properties of the decision surface ensures high generalization ability of the learning machine. The idea behind the support-vector network was previously implemented for the restricted case where the training data can be separated without errors. We here extend this result to non-separable training data.High generalization ability of support-vector networks utilizing polynomial input transformations is demonstrated. We also compare the performance of the support-vector network to various classical learning algorithms that all took part in a benchmark study of Optical Character Recognition.     

Zero-Automatic Networks

We continue the study of zero-automatic queues first introduced in Dao-Thi and Mairesse (Adv Appl Probab 39(2):429–461, 2007). These queues are characterized by a special buffering mechanism evolving like a random walk on some infinite group or monoid. The simple M/M/1 queue and Gelenbe’s G-queue with positive and negative customers are the two simplest 0-automatic queues. All stable 0-automatic queues have an explicit “multiplicative” stationary distribution and a Poisson departure process (Dao-Thi and Mairesse, Adv Appl Probab 39(2):429–461, 2007). In this paper, we introduce and study networks of 0-automatic queues. We consider two types of networks, with either a Jackson-like or a Kelly-like routing mechanism. In both cases, and under the stability condition, we prove that the stationary distribution of the buffer contents has a “product-form” and can be explicitly determined. Furthermore, the departure process out of the network is Poisson.