交通运输系统工程与信息

交通运输系统工程与信息 ›› 2021, Vol. 21 ›› Issue (2): 224-230.

• 工程应用与案例分析 • 上一篇    下一篇

基于复杂网络的城市交通拥塞因子风险传播机理及其应用研究

胡立伟*，范仔健，张苏航，郭治，殷秀芬   

  1. 昆明理工大学，交通工程学院，昆明 650500
  • 收稿日期:2020-12-04 修回日期:2021-01-24 出版日期:2021-04-25 发布日期:2021-04-25
  • 作者简介:胡立伟(1978- )，男，山东潍坊人，教授，博士。
  • 基金资助:

    国家自然科学基金/National Natural Science Foundation of China(61863019)。

Risk Propagation Mechanism and Application of Urban Traffic Congestion Factors Based on Complex Networks

HU Li-wei* , FAN Zi-jian, ZHANG Su-hang, GUO Zhi, YIN Xiu-fen   

  1. Faculty of Transportation Engineering, Kunming University of Science and Technology, Kunming 650500, China
  • Received:2020-12-04 Revised:2021-01-24 Online:2021-04-25 Published:2021-04-25

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摘要：

为了解城市交通拥塞因子风险传播特性，提升拥塞风险控制能力，依据昆明市路网拥塞实际调查数据，利用Pearson相关系数分析风险影响因子间的相关性，构建交通拥塞因子风险复杂网络。通过软件gephi0.9.2计算复杂网络各指标，验证网络的可行性和适用性。计算网络节点的相关指标进而引入网络节点重要度 k 的概念，据此将网络节点划分为核心节点、一般节点和边缘节点，同时引入直接免疫率 ρ 共同构建风险传播模型。对筛选出的核心节点进行直接免疫控制，免疫概率 ρ 分别取0.028，0.056，0.112后计算分析可知，免疫概率 ρ 取值基本与感染节点峰值比例值成反比。结果显示，识别出网络中重要度较大的节点并进行免疫控制后，交通拥塞因子风险的传播规模和传播速率将得到较好控制，对现实生活中治理城市道路路网交通拥塞有较好的指导意义。

关键词: 城市交通, 风险传播, 复杂网络, 交通拥塞因子, SIR模型, 免疫控制

Abstract:

In order to understand the characteristics of risk propagation of urban traffic congestion factors and improve the ability of congestion risk control, this paper studies the risk propagation mechanism. Based on the actual survey data of road network congestion in Kunming, the Pearson correlation coefficient is used to analyze the correlation between risk impact factors. A complex network of traffic congestion factors is constructed, and various indicators of the complex network are calculated by the software gephi0.9.2 to verify the feasibility and applicability of the network. The importance of nodes k was introduced to calculate the relevant indexes of nodes, and then the nodes were divided into core nodes, general nodes, and edge nodes. Meanwhile, the direct immunity rate ρ was introduced to jointly construct the risk propagation model. The immune probability ρ of the selected core nodes was 0.028, 0.056, and 0.112, respectively. After calculation and analysis, it was found that the immune probability ρ value was inversely proportional to the peak proportion of infected nodes. The results show that the propagation scale and propagation rate of traffic congestion factor risk will be better controlled by identifying the nodes with greater importance in the network and carrying out immune control, which has better guiding significance for the management of traffic congestion in the urban road network in real life.

Key words: urban traffic, risk propagation, complex network, traffic congestion factors, SIR model, immune control

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