Characterizing the Evolution of the Container Traffic Share in the Mediterranean Sea Using Hierarchical Clustering

Abstract

This research investigates the traffic share evolution of the container throughput in the Mediterranean ports from 2000 to 2015 considering hierarchical clustering and concentration indexes. Compositional Data analysis techniques are used to illustrate periods with similar traffic share composition. Two different regions (East and West) in the Mediterranean Sea (Med) are selected in the function of the long haul services. The standard concentration indexes (i.e., concentration ratio, Gini coefficient, and Normalized Herfindahl-Hirschman) reveal a gentle decreasing of the concentration with relevant fluctuations mainly in the East region. This is due to the investment in port infrastructure in the area resulting from privatization initiatives in many Eastern Mediterranean countries. The periods obtained from the hierarchical clustering show a differentiated pattern in traffic share composition. For these periods, the shift-share results are consistent with traffic fluctuations and in line with the evolution of the concentration indexes. The combination of methods has allowed a good interpretation of the spatial and temporal evolution of the Med ports’ traffic being the methodology applicable elsewhere in the context of port system analysis.

Hybrid approaches for container traffic forecasting in the context of anomalous events: The case of the Yangtze River Delta region in the COVID-19 pandemic

“…Container transportation has become one of the most essential activities in the world's economic and logistics chain ( Onut et al, 2011 ; Balci et al, 2018 ) and container throughput has been widely recognized as the most important indicator of port activity ( Ziran et al, 2022 ; Gao et al, 2016 ; Grifoll et al, 2018 ). For this reason, accurate forecasting of container throughput plays a crucial role, regardless of the port development strategies ( Feng et al, 2019 ), infrastructure investments or maritime supply chain ( Ha et al, 2019 ).…”

Ship AIS Trajectory Clustering: An HDBSCAN-Based Approach

“…Cluster analysis is to group or cluster data according to the inherent similarity and characteristics between data to achieve the purpose of data mining [4]. In maritime traffic applications, the clustering results may show the usual route and traffic volume distribution and regularity of marine environmental change [5][6][7]. As a commonly used data mining method, ship trajectory clustering integrates the trajectory data of different ships into different categories or clusters.…”

Adriatic Sea Hub Ports Feeder Service Optimization Using Multi-Criteria Decision-Making Methods

“…However, there is no relevant research for the selection of hub ports or the feeder network in the Adriatic Sea. The Adriatic Sea has several routes that are being used, which indicates that there is no coherence of the network, and its feeder service remains relatively underdeveloped [27][28][29][30][31][32][33]. In accordance with all previously stated research, the main objective of this study is to find a model for selecting the hub port that can handle the feeder service in the Adriatic Sea, and to optimize the network of service seaports.…”