城市公共交通系统是一个典型的具有复杂性特征的动态系统，其包括由线路、站点以及线路站点之间的联系组成的拓扑网络以及站点与站点之间流动形成的加权网络。近些年来，随着对复杂网络的研究越来越深入，复杂网络理论逐渐被广泛地应用与分析城市公共交通网络的结构特征。既有的研究多是将公共交通的站点作为节点、线路作为连边来构建公共交通系统的静态拓扑网络，并在此基础上分析网络拓扑结构特征。实际上交通网络具有动态性和时空复杂性现有研究忽略了静态网络上流量信息的动态行为研究，难以反映公交复合动态网络的实质特性。现有研究中基于真实的公交刷卡数据对交通系统的动态网络结构特征进行分析的研究相对较少，而针对一天内不同时段的客流网络结构特征的动态变化研究则更少。本文基于实际的公交刷卡数据，通过对一天的不同时间段内公交客流量的空间分布以及网络结构特征进行比较，旨在探讨在不同时间段内客流量空间分布特征的变化，同时，还探讨了客流量组成的动态加权网络在不同时间段内的结构特征差异。 本文以2015年8月13号数据为基础，首先分析了公交卡出行者交通流的流量的空间分布状态，研究时间段分为两类，一类是一天内交通流的整体空间分布状态，另一类是将一天分为5个时段，分别为2:00-4:00、7:00-9:00、14:00-16:00、18:00-20:00、21:00-23:00，这样时段的划分有助于更加全面地分析交通流空间分布变化特征。其次，本文借助于度中心性、加权度中心性等指标来刻画公交卡交通流的网络结构特征，还分析了节点中心性空间分布格局的分时段变化特征。另外，本文还将节点中心性与商住类、企业类和医疗类这三类POI密度进行相关性分析。 通过一系列的分析比较，本文得到以下结论：1、不同时间段内栅格与栅格间流量在空间分布上差异较大，尤其是流量排名前10%的高流量分布极其不均匀。特别是在7-9点和18-20点的交通流中，其高流量的分布多以某一站点为核心，呈现出特定方向的发散式的分布格局，如六里桥、东直门、沙河等站点。2、本文发现全天及不同时间段公交客流网络特征呈现出小世界网络特征，但并不是无标度的。3、度中心性与加权度中心性基本上呈现出不均衡的空间分布状态，且北京二环到四环间一直是度中心性和加权度中心性高值分布的区域。度和加权度排名前十的栅格所对应的站点主要是位于大型居住区或就业相对密集的周边，或者是枢纽站点。4、中介中心性和接近中心性均与度中心性呈现出正向关系。5、综合来看，这四类节点中心性与住宅、就业、医疗这三类POI密度的相关系数呈现出较大的波动性，且不同节点中心性与POI密度间的相关系数存在较大的差异。与商住类、企业类与医疗类POI密度最相关的指标为加权度中心性，但时间段存在差异；而2-4点客流网络中四类节点中心性与商住类、企业类与医疗类POI密度相关系数均为最低。

Urban public transportation system is a typical dynamic system with complex characteristics, which includes a topological network composed of lines, stations and the connections between the lines and stations, as well as a weighted network formed by flows between stations. In recent years, with the deepening of the research on complex network, the theory of complex network has been widely applied and analyzed the structural characteristics of urban public transport network. Most of the existing researches build the static topological network of the public transportation system by taking the stations of public transportation as nodes and lines as links, and then analyze the characteristics of the network topological structure on this basis. In fact, traffic network is dynamic and spatiotemporal complexity. Existing studies ignore the dynamic behavior of traffic information on static network, which is difficult to reflect the essential characteristics of bus composite dynamic network. In the existing studies, there are relatively few studies that analyze the dynamic network structure characteristics of the traffic system based on the real bus card data, and even fewer studies that analyze and compare the structure characteristics of the passenger flow network at different times of the day. This article is based on the actual bus card data, and through the different period of a day of bus traffic space distribution and the characteristics of network structure, aims to explore volume in different period the change of the spatial distribution characteristics, at the same time, we can also study traffic dynamic network structure characteristics of the differences in different periods. In this paper, based on 13 August 2015 data, analyzes the public transportation card traveler on the spatial distribution of the traffic flow of traffic condition, study period is divided into two kinds, one kind is a day of traffic flow of the whole space distribution state, another kind is a day can be divided into five periods, respectively at 2:00-4:00 PM, 7:00-9:00 PM 16:00, 18:00-20:00, 21:00 23:00, this period of time division contributes to a more comprehensive analysis of traffic flow space distribution characteristics. Secondly, this paper describes the network structure characteristics of bus card traffic flow with the help of degree centrality, weighted degree centrality and other indicators, and analyzes the time-segment variation characteristics of node centrality spatial distribution pattern. In addition, this paper also analyzed the correlation between node centrality and POI density of employment, residence and medical care. Through a series of analysis and comparison, the following conclusions are drawn in this paper: 1. The spatial distribution of the flow between grids in different time periods is quite different, especially the high flow distribution in the top 10% of the flow ranking is extremely uneven. Especially in the traffic flow between 7-9 o 'clock and 18-20 o 'clock, the distribution of high flow is mostly centered on a certain station, showing a divergent distribution pattern in a specific direction, such as liuliqiao, dongzhimen, shahe and other stations.2. This paper finds that the characteristics of bus passenger flow network throughout the day and in different time periods show the characteristics of small-world network, but it is not scale-free.3. Degree centrality and weighted degree centrality basically present an unbalanced spatial distribution state, and the area between the second ring and the fourth ring of Beijing has always been the distribution area with high value of degree centrality and weighted degree centrality. The top 10 grids in terms of degree and weighting correspond to sites located either near large residential areas, near relatively employment-intensive areas, or at hubs.4. Mediating centrality and near-centrality were positively correlated with degree centrality.5. From a comprehensive perspective, the correlation coefficients between the centrality of these four types of nodes and POI densities of housing, employment and medical care are highly volatile, and the correlation coefficients between the centrality of different nodes and POI densities are quite different. The weighted centrality of nodes is most relevant to housing, employment and residential density, but there are differences in time periods. The correlation coefficient between the centrality of four types of nodes in the 2-4 passenger flow network and residence, employment and residence density is the lowest.