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近年来，我国城市轨道交通系统中延误、破坏事件及运营故障等事故频发，事故站点大多客流量大且交通繁忙，因此识别城市轨道交通网络中的重要节点对于保证网络的正常运营具有重要意义。为更真实地反映现实世界城市轨道交通网络的实际情况，本文首先基于复杂网络理论构建城市轨道交通网络模型；其次在网络拓扑指标的基础上，考虑车站自身属性及其周边区域环境对节点重要度的影响，构建城市轨道交通网络指标评价体系；然后基于混沌时间序列与混沌系统指标序列的共性，采用Kolmogorov熵权法计算指标权重，结合城市轨道交通网络指标数据得到城市轨道交通网络节点重要度排序；最后，通过成都地铁网络的实例研究结果表明：排序前10的关键车站均位于城市中心城区且皆为换乘站点。与信息熵权法结果进行对比发现，本文方法有8个共同的关键站点，且排序结果分布更集中、极端值更少、得分差距更小。研究成果有助于更好地理解城市轨道交通网络的结构特征，为预防事故、优化运营提供了重要理论依据。
Abstract：In recent years,the frequency of accidents, such as delays, sabotage events, and operational failures, in China's urban rail transit system has escalated. Most of these incidents occur at locations with high passenger flow and heavy traffic, emphasizing the critical need to identify important nodes in urban rail transit networks for ensuring their normal operation. To realistically model the realworld complexities of such networks, this study initially develops a model based on complex network theory. Subsequently, we incorporate the influence of station-specific attributes and surrounding regional environment on node importance, constructing an index evaluation system for urban rail transit networks. Then, leveraging the commonalities between chaotic time series and chaotic system index series, we introduce the Kolmogorov entropy weight method to compute index weights. The importance ranking of urban rail transit network nodes is then derived by integrating the index data.Case-study results from the Chengdu metro network demonstrate that the top-10 key stations are concentrated in the central urban area and serve as transfer stations. Comparative analysis with the information entropy weight method reveals that the proposed approach shares eight common key sites,displaying a more concentrated distribution of ranking results with fewer extreme values and a smaller score gap. These research findings enhance our understanding of the structural characteristics of urban rail transit networks, providing a crucial theoretical foundation for accident prevention and operational optimization.
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基本信息:
DOI：10.19961/j.cnki.1672-4747.2023.07.012
中图分类号:U239.5
引用信息:
[1]杨振珑,黄文成,法慧妍,等.基于Kolmogorov熵权的城市轨道交通网络节点重要度识别算法[J].交通运输工程与信息学报,2024,22(01):139-149.DOI:10.19961/j.cnki.1672-4747.2023.07.012.
基金信息:
国家自然科学基金项目（72001179,72171198）;; 四川省科技厅国际科技创新合作项目（2021YFH0106）;; 中央高校基本科研项目（2682021CX052）
2023-12-08
2023-12-08
2023-12-08