拥堵情境下国际铁路货运网络关键节点识别

Key Node Identification of International Railway Freight Network under Congestion Situation

为有效识别拥堵情景下国际铁路货运网络中的拥堵、连接影响关键节点,利用赋权PageRank方法,建立了拥堵赋权影响下的货运网络模型,设计了基于拥堵的关键节点识别算法,并根据各节点对拥堵变化情况的反馈差异,获取了拥堵、连接影响正负反馈关键节点。以中欧班列2020年9-12月货运网络为算例对算法进行了验证,结果表明该算法兼顾运输网络拓扑结构特征及拥堵在网络中的传播影响,可有效识别网络中的拥堵影响、连接影响正负反馈关键节点,可为国际铁路货运网络在运输过程中的运作及运营把控提供理论依据。

In order to effectively identify the congestion and connection-affected key nodes in the international railway freight network under the congestion situation, the weighted PageRank method was used to establish an freight network model under the influence of congestion weighting, and the key node identification algorithm based on congestion was designed, and based on the feedback difference of each node on the change of congestion is obtained, and the key nodes of positive and negative feedback of congestion and connection influence are obtained. The algorithm was verified by taking the freight network of China Railway Express from September to December 2020 as an example. The results show that the algorithm takes into account the characteristics of the transportation network topology and the spread of congestion in the network, and can effectively identify the impact of congestion in the network. Connecting key nodes that affect positive and negative feedback can provide a theoretical basis for the operation and control of the international railway freight network during transportation.