城市群城镇交通网络特性研究

为了研究城市群城镇交通网络特性,本文运用primal approach方法以城镇为节点,以其之间的道路为边构造长沙城镇交通网络、株洲城镇交通网络等城市群城镇交通网络模型,使用Matlab和VC++6.0分别对它们的平均度、网络密度、聚集系数、平均最短路径、随机网络聚集系数、随机网络平均最短路径、网络结构熵、全局系数等网络特性指标进行计算和比较分析,得到以下结论:(1)城市群城镇交通网络都具有小世界性,是小世界网络;(2)城市群城镇交通网络都不存在幂律特征,不存在无标度性,不是无标度网络;(3)城市群城镇交通网络都不存在指数特征,不是随机网络;(4)城市群城镇交通网络中,长株潭城市群市县交通网络整体效率最高、网络密度最大,长株潭城市群城镇交通网结构熵最小,表明其最有序。     

基于移动模型的VANETs网络动态拓扑特征研究

采用复杂网络理论研究VAENTs网络动态拓扑结构特性,以基于车辆换道功能的智能驾驶移动模型为 基础,应用VanetMobiSim仿真软件建立了车辆交通网络,详细分析了VAENTs网络瞬时拓扑特征及平均度、聚类系数和调和平均最短路径长度等复杂统计参数随时间的变化特征。通过仿真实验发现VAENTs网络动态拓扑不具有无标度属性,只在特定条件下,才呈现小世界网络的特征。     

复杂金融网络的自相似性研究

以股票为节点,选取适当阈值量化股票收益率序列间相关关系从而构建复杂金融网络。基于复杂网络的理论,讨论金融网络的度分布、平均最短路径和聚集系数,发现面向金融时间序列的股票网络具有小世界效应,无标度特性和一个很重要的特性—自相似性。该文用两种方法分析了网络的自相似性:一是提出用网络节点的度构造Hurst指数,定量分析金融网络的自相似性;二是金融网络的平均路径长度和聚集系数定性地分析了复杂网络的自相似性。     

图书漂流网络模型实证研究

通过收集整理图书漂流(bookcrossing)网站一个月内的图书漂流信息,建立图书与用户的数据库模型,并且构建两者间关系的二分图.从复杂网络的角度分析计算该网络的相关参数,如度分布、聚集系数、平均最短路径、节点项目度、项目大小、点强度及节点兴趣度,得到的图书漂流网络模型同时具有无标度特性和小世界网络的特性.     

藏文字同现网络的小世界效应和无标度特性

复杂网络具有自组织、自相似、吸引子、小世界、无标度中部分或全部性质,而语言文字作为人类智慧和文明的结晶,是经过漫长演化形成的复杂网络。该文对藏语诗歌、散文、政治、佛教、教材和口语等六类具有代表性的体裁语料,每类各取15篇共90篇文章构建了97个藏文字同现网络,分析了藏文字同现网络的最短路径长度、聚类系数和度分布,实验数据显示97个藏文字同现网络都具有小世界效应和无标度特性,表明藏文字同现网络都具有小世界效应和无标度特性。     

基于失匹配负波的大脑功能性网络分析

根据正常人与听力损伤患者的失匹配负波（MMN）数据建立大脑功能性网络,计算该大脑功能性网络的复杂网络统计特性,发现所建立的功能性网络相对于随机网络具有类似无标度特性,而且具有高聚类系数、小特征路径长度的小世界网络特性;另外,还计算了功能性网络的平均度和网络结构熵,结果发现正常人的功能性网络的平均度、聚类系数、结构熵等参数均高于听力损伤患者的相应参数,提示了听力损伤后脑功能网络连接减弱可能是声源分辨能力下降的中枢表现,同时也反映了平均度、聚类系数、结构熵等功能性网络参数可作为反应听力损伤后声源分辨能力下降的诊断标志。     

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.     

无标度网络中基于最短路径免疫策略的病毒传播研究

传统病毒免疫策略大多基于网络的全局拓扑信息。然而现实生活中的大部分复杂网络仅仅只能了解其局部 拓扑信息。鉴于许多实际复杂网络具有无标度特性,研究了在无标度复杂演化网络中基于网络局部拓扑信息最短路 径免疫策略的病毒传播现象。利用平均场理论建立含个体抵杭力重要因素的无标度网络病毒传播模型,并引入基于 最短路径的免疫策略。比较了随机免疫、目标免疫和最短路径免疫3种策略对无标度复杂网络病毒传播的影响,结果 表明了基于最短路径免疫策略的有效性。     

复杂网络研究

从复杂网络的三个主要度量特征量:平均路径长度、聚集系数、度分布的角度分别介绍了复杂网络中最主要的三种网络模型,即随机网络模型、小世界网络模型和无标度网络模型,并提出了进一步研究的一些方向。     

从复杂网络角度对即时消息网络进行拓扑建模

本文从复杂网络角度分析了即时消息网络,并对传统的BA模型进行扩展提出一种更适用于即时消息网络的VGBA模型。通过在nioki.com即时消息网络实例中验证和对比随机图,BA模型和VGBA模型的度分布指数,平均最短路径,聚集程度等拓扑度量值,表明VGBA模型很好的具备了复杂系统的无标度特性和小世界特性,并能更好的符合即时消息网络的特征。     

长株潭城市群交通网络结构洞分析

为了深入研究城市群交通网络结构特性,根据长株潭城市群交通网络构建其道路对偶网络,建立网络结构洞算法,运用VC++6.0连接Matlab7.0计算长株潭城市群交通网络的有效规模、效率、约束度、等级度等结构洞理论指标值,对网络使用n-派系算法计算长株潭城市群交通网1派系结构分布,长株潭城市群交通网络1-派系中包括五条道路以上的不存在,包括五条道路极少,占0.24%,包括四条道路较少,占13%。可以得出以下结论：按照等级度指标可将长株潭城市群交通网道路划分为核心、中间、边缘三类;长株潭城市群交通网络不存在着结构自治,在结构上不存在无效道路。     

基于删边扩容策略的城市交通网络优化

建立一个面向控制的城市交通网络模型,以结构矩阵的形式进行数学表示,引入序参量的概念来表征城市交通的拥堵状况,并提出了基于介数的具体删边扩容策略. 将此策略应用到已建立的交通网络模型中进行仿真验证,证明了该策略在不影响行驶路径长度的前提下可以显著改善城市交通状况,并得出了删边比例与介数最大值成反比例的对应关系的结论.     

小世界网络中的删边扩容效应

不同于无标度网络,小世界网络中体现负载特性的介数分布和体现连接特征的度分布这两种结构差异很大,依据这个差异,我们提出了有效的删边扩容方法并进行了仿真实验,并进一步从容量与最大介数的关系理论分析确证了实验结果.接着,我们分析了小世界网络中介数分布的差异性与删边扩容效果的相关性,把基于介数的基尼系数变化用于度量删边扩容的效果,指出了结构差异是导致删边扩容效应的必要条件.还指出了无论从容量与介数的定义、仿真结果、结构差异与删边扩容效果的关系来看,都应该从介数入手来实现删边扩容方法,这种方法反直觉,有很多变种,并且实现方便、节省成本,作为一类新的路由策略可以广泛地应用于各类通信网络和交通网络等领域.     

Robust network planning in nonuniform traffic scenarios

Robustness to the environmental variations is an important feature of any reliable communication network. This paper reports on a network theory approach to the design of such networks where the environmental changes are traffic fluctuations, topology modifications, and changes in the source of external traffic. Motivated by the definition of betweenness centrality in network science, we introduce the notion of traffic-aware betweenness (TAB) for data networks, where usually an explicit (or implicit) traffic matrix governs the distribution of external traffic into the network. We use the average normalized traffic-aware betweenness, which is referred to as traffic-aware network criticality (TANC), as our main metric to quantify the robustness of a network. We show that TANC is directly related to some important network performance metrics, such as average network utilization and average network cost. We prove that TANC is a linear function of end-to-end effective resistances of the graph. As a result, TANC is a convex function of link weights and can be minimized using convex optimization techniques. We use semi-definite programming method to study the properties of the optimization problem and derive useful results to be employed for robust network planning purposes.     

基于神经网络的城市快速路交通拥堵判别算法

针对城市快速路的常发性拥堵和偶发性交通拥堵,提出了一种基于神经网络的自动判别算法.该方法利用改进的自适应梯度算法优化神经网络的权值参数,既能保证神经网络参数收敛到全局最优值,又具有快的学习速度,提高了神经网络的检测效果.利用微观交通仿真软件PARAMICS建立了城市快速路网,通过多次仿真获得了包含各种交通拥堵的学习样本,增强了算法的鲁棒性.将训练好的神经网络对多种实际的交通数据进行了仿真试验.实验结果表明,该算法在城市快速路交通拥堵判别中具有较高的检测率和较低的误报率.     

Partitioning road networks using density peak graphs: Efficiency vs. accuracy

Road traffic networks are rapidly growing in size with increasing complexities. To simplify their analysis in order to maintain smooth traffic, a large urban road network can be considered as a set of small sub-networks, which exhibit distinctive traffic flow patterns. In this paper, we propose a robust framework for spatial partitioning of large urban road networks based on traffic measures. For a given urban road network, we aim to identify the different sub-networks or partitions that exhibit homogeneous traffic patterns internally, but heterogeneous patterns to others externally. To this end, we develop a two-stage algorithm (referred as FaDSPa) within our framework. It first transforms the large road graph into a well-structured and condensed density peak graph (DPG) via density based clustering and link aggregation using traffic density and adjacency connectivity, respectively. Thereafter we apply our spectral theory based graph cut (referred as α-Cut) to partition the DPG and obtain the different sub-networks. Thus the framework applies the locally distributed computations of density based clustering to improve efficiency and the centralized global computations of spectral clustering to improve accuracy. We perform extensive experiments on real as well as synthetic datasets, and compare its performance with that of an existing road network partitioning method. Our results show that the proposed method outperforms the existing normalized cut based method for small road networks and provides impressive results for much larger networks, where other methods may face serious problems of time and space complexities.     

A fast network partition method for large-scale urban traffic networks

In order to control the large-scale urban traffic network through hierarchical or decentralized methods, it is necessary to exploit a network partition method, which should be both effective in extracting subnetworks and fast to compute. In this paper, a new approach to calculate the correlation degree, which determines the desire for interconnection between two adjacent intersections, is first proposed. It is used as a weight of a link in an urban traffic network, which considers both the physical characteristics and the dynamic traffic information of the link. Then, a fast network division approach by optimizing the modularity, which is a criterion to distinguish the quality of the partition results, is applied to identify the subnetworks for large-scale urban traffic networks. Finally, an application to a specified urban traffic network is investigated using the proposed algorithm. The results show that it is an effective and efficient method for partitioning urban traffic networks automatically in real world.     

Research on economic benefits of multi-city logistics development based on data-driven analysis

This study aims to measure the logistics economic efficiency of major first-tier cities in China and propose the interactive development plan of the green logistics industry based on the division of the urban agglomeration. In this study, the evaluation system for the logistics input and economic output of urban agglomerations is firstly established based on data-driven analytics, and a green logistics economic efficiency model using the dataset from 2008 to 2017 is constructed to estimate the input–output efficiency, development trend, and spatial differentiation of urban agglomerations comprehensively. Finally, the Shapley value method is adopted to obtain the specific distribution plan of logistics investment. The results show that the logistics economic efficiency of the 9 major urban agglomerations is all greater than 1 under the constant returns-to-scale (CRS) hypothesis while the average logistics economic efficiency of Pearl River Delta region, Chengdu-Chongqing region, and Shandong Peninsula region is significantly less than 1 under the medium returns-to-scale (MRS) hypothesis. The comprehensive input–output efficiency of five out of 9 major Chinese urban agglomerations showed a downward trend, with the highest declining rate of 5.9% in the Yangtze River Delta urban agglomeration. The urban agglomeration with the highest increase rate in input–output efficiency from 2013 to 2017 is the Chengdu-Chongqing region, which reached 3.97%.     

SALA: A Self-Adaptive Learning Algorithm—Towards Efficient Dynamic Route Guidance in Urban Traffic Networks

In order to alleviate traffic congestion for vehicles in urban networks, most of current researches mainly focused on signal optimization models and traffic assignment models, or tried to recognize the interaction between signal control and traffic assignment. However, these methods may not be able to provide fast and accurate route guidance due to the lack of individual traffic demands, real-time traffic data and dynamic cooperation between vehicles. To solve these problems, this paper proposes a dynamic and real-time route selection model in urban traffic networks (DR2SM), which can supply a more accurate and personalized strategy for vehicles in urban traffic networks. Combining the preference for alternative routes with real-time traffic conditions, each vehicle in urban traffic networks updates its route selection before going through each intersection. Based on its historical experiences and estimation about route choices of the other vehicles, each vehicle uses a self-adaptive learning algorithm to play congestion game with each other to reach Nash equilibrium. In the route selection process, each vehicle selects the user-optimal route, which can maximize the utility of each driving vehicle. The results of the experiments on both synthetic and real-world road networks show that compared with non-cooperative route selection algorithms and three state-of-the-art equilibrium algorithms, DR2SM can effectively reduce the average traveling time in the dynamic and uncertain urban traffic networks.