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为分析公交复杂网络的拓扑性质,本文以北京市为例,选取截止到2010年7月的北京全市(14区、2县)的1165条公交线路和9618个公交站点为样本数据,运用复杂网络理论构建起基于邻接站点的有向加权复杂网络模型.该方法以公交站点作为节点,相邻站点之间的公交线路作为边,使得网络既具有复杂网络的拓扑性质同时节点(站点)又具有明确的地理坐标.对网络中节点度、点强度、强度分布、平均最短路径、聚类系数等性质的分析显示,公交复杂网络的度和点强度分布极为不均,网络中前5%和前10%节点的累计强度分布分别达到22.43%和43.02%;点强度与排列序数、累积强度分布都服从幂律分布,具有无标度和小世界的网络特点,少数关键节点在网络中发挥着重要的连接作用.为分析复杂网络中的关键节点,本文通过承载压力分析和基于“掠夺”的区域中心节点提取两种方法,得到了公交复杂网络中两类不同表现的关键节点.这些规律也为优化城市公交网络及交通规划发展提供了新的参考建议.
In order to analyze the topological properties of public transport complex networks, this paper takes Beijing as an example, selects 1165 bus lines and 9618 bus stops in Beijing’s entire city (14 districts and 2 counties) as of July 2010 as sample data, and uses complex networks In theory, a directed weighted complex network model based on adjoining stations is constructed. The method takes bus station as node and bus routes between adjacent stations as edges, which makes the network not only have the topological property of complex network but also the nodes The analysis of the properties of node degree, point intensity, intensity distribution, average shortest path, clustering coefficient and other properties in the network shows that the degree and intensity distribution of bus complex networks is extremely uneven. The top 5% and the top 10 The cumulative intensity distributions of the% nodes reach 22.43% and 43.02% respectively. The point intensity, the arrangement order and the cumulative intensity distribution follow the power-law distribution and have the features of scale-free and small-world networks. A few key nodes play an important role in the network In order to analyze the key nodes in a complex network, this paper presents two methods of bearing pressure analysis and extraction based on "predatory AC complex network of two types of different manifestations of critical nodes. These laws also optimize the urban public transport network transport planning and development of a new reference recommendations.