This study selected the Sino-US route data from the top 30 global container liner companies between December 1,2019,and December 29,2019,as the data source utilizing the complex network research methodology.It constru...This study selected the Sino-US route data from the top 30 global container liner companies between December 1,2019,and December 29,2019,as the data source utilizing the complex network research methodology.It constructs a Sino-US container shipping network through voyage weighting and analyzes the essential structural characteristics to explore the network’s complex structural fea-tures.The network’s evolution is examined from three perspectives,namely,time,space,and event influence,aiming to comprehens-ively explore the network’s evolution mechanism.The results revealed that:1)the weighted Sino-US container shipping network exhib-its small-world and scale-free properties.Key hub ports in the United States include NEW YORK NY,SAVANNAH GA,LOS ANGELES CA,and OAKLAND CA,whereas SHANGHAI serving as the hub port in China.The geographical distribution of these hub ports is uneven.2)Concerning the evolution of the weighted Sino-US container shipping network,from a temporal perspective,the evolution of the regional structure of the entire Sino-US region and the Inland United States is in a stage of radiative expansion and de-velopment,with a need for further enhancement in competitiveness and development speed.The evolution of the regional structure of southern China and Europe is transitioning from the stage of radiative expansion and development to an advanced equilibrium stage.The shipping development in Northern China,the Western and Eastern United States,and Asia is undergoing significant changes but faces challenges of fierce competition and imbalances.From a spatial perspective,the rationality and effectiveness of the improved weighted Barrat-Barthelemy-Vespignani(BBV)model are confirmed through theoretical derivation.The applicability of the improved evolution model is verified by simulating the evolution of the weighted Sino-US container shipping network.From an event impact per-spective,the Corona Virus Disease 2019(COVID-19)pandemic has not fundamentally affected the spatial pattern of the weighted Sino-US container shipping network but has significantly impacted the network’s connectivity.The network lacks sufficient resilience and stability in emergency situations.3)Based on the analysis of the structural characteristics and evolution of the weighted Sino-US con-tainer shipping network,recommendations for network development are proposed from three aspects:emphasizing the development of hub ports,focusing on the balanced development of the network,and optimizing the layout of Chinese ports.展开更多
为探究全服务航空公司航线网络演化影响因素及演化模型,以全球排名前10的全服务航空公司为研究对象,基于链路预测思想,对5种内生属性相似性指标和3种外生属性相似性指标及耦合相似性指标分别进行曲线下面积(Area Under Curve,AUC)计算,...为探究全服务航空公司航线网络演化影响因素及演化模型,以全球排名前10的全服务航空公司为研究对象,基于链路预测思想,对5种内生属性相似性指标和3种外生属性相似性指标及耦合相似性指标分别进行曲线下面积(Area Under Curve,AUC)计算,选取AUC值最高的相似性指标进行航线预测及验证,并构建基于链路预测的全服务航空公司航线网络改进BBV动态加权演化模型.研究结果表明:基于优先连接(Preferential Attachment,PA)指标和机场航班量指标的耦合相似性指标的AUC值最高,是影响全服务航空公司航线网络演化的关键指标;在基于该耦合相似性指标的链路预测算法中,平均航线命中率达45.79%,AUC值达95%以上;相比于传统BBV模型,改进BBV动态加权演化模型的航线数拟合准确率平均提升了4.43%,能够较为科学地拟合不同全服务航空公司航线网络的扩张与收缩演变过程.展开更多
基金Under the auspices of National Natural Science Foundation of China(No.41201473,41371975)。
文摘This study selected the Sino-US route data from the top 30 global container liner companies between December 1,2019,and December 29,2019,as the data source utilizing the complex network research methodology.It constructs a Sino-US container shipping network through voyage weighting and analyzes the essential structural characteristics to explore the network’s complex structural fea-tures.The network’s evolution is examined from three perspectives,namely,time,space,and event influence,aiming to comprehens-ively explore the network’s evolution mechanism.The results revealed that:1)the weighted Sino-US container shipping network exhib-its small-world and scale-free properties.Key hub ports in the United States include NEW YORK NY,SAVANNAH GA,LOS ANGELES CA,and OAKLAND CA,whereas SHANGHAI serving as the hub port in China.The geographical distribution of these hub ports is uneven.2)Concerning the evolution of the weighted Sino-US container shipping network,from a temporal perspective,the evolution of the regional structure of the entire Sino-US region and the Inland United States is in a stage of radiative expansion and de-velopment,with a need for further enhancement in competitiveness and development speed.The evolution of the regional structure of southern China and Europe is transitioning from the stage of radiative expansion and development to an advanced equilibrium stage.The shipping development in Northern China,the Western and Eastern United States,and Asia is undergoing significant changes but faces challenges of fierce competition and imbalances.From a spatial perspective,the rationality and effectiveness of the improved weighted Barrat-Barthelemy-Vespignani(BBV)model are confirmed through theoretical derivation.The applicability of the improved evolution model is verified by simulating the evolution of the weighted Sino-US container shipping network.From an event impact per-spective,the Corona Virus Disease 2019(COVID-19)pandemic has not fundamentally affected the spatial pattern of the weighted Sino-US container shipping network but has significantly impacted the network’s connectivity.The network lacks sufficient resilience and stability in emergency situations.3)Based on the analysis of the structural characteristics and evolution of the weighted Sino-US con-tainer shipping network,recommendations for network development are proposed from three aspects:emphasizing the development of hub ports,focusing on the balanced development of the network,and optimizing the layout of Chinese ports.
文摘为探究全服务航空公司航线网络演化影响因素及演化模型,以全球排名前10的全服务航空公司为研究对象,基于链路预测思想,对5种内生属性相似性指标和3种外生属性相似性指标及耦合相似性指标分别进行曲线下面积(Area Under Curve,AUC)计算,选取AUC值最高的相似性指标进行航线预测及验证,并构建基于链路预测的全服务航空公司航线网络改进BBV动态加权演化模型.研究结果表明:基于优先连接(Preferential Attachment,PA)指标和机场航班量指标的耦合相似性指标的AUC值最高,是影响全服务航空公司航线网络演化的关键指标;在基于该耦合相似性指标的链路预测算法中,平均航线命中率达45.79%,AUC值达95%以上;相比于传统BBV模型,改进BBV动态加权演化模型的航线数拟合准确率平均提升了4.43%,能够较为科学地拟合不同全服务航空公司航线网络的扩张与收缩演变过程.
