Abstract:

This study developed a complex network model for the China Export Containerized Freight Index (CCFI) fluctuation analysis, based on the symbolic dynamic method. The CCFI fluctuation patterns were analyzed by the network dynamic topological properties, such as mode strength, strength distribution, weighted clustering coefficient, average shortest path length, and mode betweenness. The results indicate that the container freight fluctuation network has the small- world and scale- free characteristics. The CCFI fluctuation is characterized by clustering, periodicity, continuity, and graduality. The conversion cycle between linkage modes within the clustering subgroups is less than 2 months, and the average conversion cycle of linkage modes between clustering subgroups lasts 3.8 months. The CCFI clustering fluctuation carries out conversion by the modes with low strength and high betweenness. The CCFI fluctuation pattern study based on complex network theory provides a new perspective for governments and shipping enterprises to understand fluctuation characteristics and reduce risks.

Key words: waterway transportation, fluctuation patterns, complex network, freight index, container shipping

摘要：

借助符号动力学方法构建中国出口集装箱运价指数(CCFI)波动复杂网络模型，通过分析网络的模态强度及强度分布、加权聚集系数、平均最短路径长度、模态介数等动力学拓扑性质，研究集装箱运价波动的一般规律. 研究发现，集装箱运价波动网络具有小世界网络和无标度网络的特性，集装箱运价波动表现出群簇性、周期性、持续性和渐进性的规律. 波动子群内模态转换周期不超过2 个月，但子群外模态转换平均周期为3.8 个月，集装箱运价群簇性波动通过点强度低介数高的模态进行转换. 基于复杂网络理论的集装箱运价波动规律研究，为政府部门及航运企业加强集装箱运价波动风险的认知与防范提供了一种新视角.

关键词: 水路运输, 波动规律, 复杂网络, 运价指数, 集装箱海运