Seaports play a crucial role in the global economy, and researchers have sought to understand their significance through various studies. In this paper, we aim to explore the common characteristics shared by important ports by analyzing the network of connections formed by vessel movement among them. To accomplish this task, we adopt a bottom-up network construction approach that combines three years' worth of AIS (Automatic Identification System) data from around the world, constructing a Ports Network that represents the connections between different ports. Through such representation, we use machine learning to measure the relative significance of different port features. Our model examined such features and revealed that geographical characteristics and the depth of the port are indicators of a port's significance to the Ports Network. Accordingly, this study employs a data-driven approach and utilizes machine learning to provide a comprehensive understanding of the factors contributing to ports' importance. The outcomes of our work are aimed to inform decision-making processes related to port development, resource allocation, and infrastructure planning in the industry.

翻译：海港在全球经济中扮演着关键角色，研究人员已通过多项研究试图理解其重要性。本文旨在通过分析港口间船舶移动所形成的连接网络，探究重要港口所共有的特征。为实现这一目标，我们采用自底向上的网络构建方法，整合了全球范围内三年的AIS（自动识别系统）数据，构建了一个表征不同港口间连接关系的港口网络。基于此表征，我们运用机器学习来衡量不同港口特征的相对重要性。我们的模型检验了这些特征，并揭示出地理特征与港口水深是衡量港口在港口网络中重要性的指标。因此，本研究采用数据驱动的方法，并利用机器学习来全面理解影响港口重要性的因素。我们的研究成果旨在为行业内的港口发展、资源分配和基础设施规划等相关决策过程提供参考依据。