Ports serve as essential nodes in global trade and economic development, offering valuable insights into both historical transformations and contemporary advancements. This study develops a port generational model using the latitude average clustering algorithm to systematically examine the evolution of investment ports along the Belt and Road Initiative (BRI) from 2013 to 2022. Key findings include: (1) A general improvement in the generational levels of ports along the BRI, with rapid development expanding from East Asia and Southeast Asia in 2013 to encompass regions such as the Persian Gulf, Eastern Europe, and West Africa by 2022. (2) The generational distribution of ports along the BRI shows a random spatial pattern rather than significant geographical clustering. BRI investments strategically manage regional risks by acquiring resources, technology, and market opportunities across different areas, aligning with China's domestic industrial and market needs, and supporting global trade objectives. (3) Ports with robust development are primarily located in East Asia, Southeast Asia, and parts of Europe, while those in decline are mainly spread across Africa, South Asia, and Eastern Europe. (4) In terms of investment approaches, contracted ports demonstrate stronger generational advancement in the first and second generations, whereas operated ports excel in the third generation and above. (5) A comparative analysis of BRI ports and surrounding ports shows that BRI investments not only elevate the development levels of the ports themselves but also positively influence the progress of nearby ports, without fostering competitive tensions.

Safety analysis according to the spatial distribution characteristics of maritime traffic accidents is critical to maritime traffic safety management. An accident analysis framework based on the geographic information system (GIS) is proposed to characterize the spatial distribution of maritime traffic accidents occurring in the Fujian sea area in 2007–2020 by employing kernel density estimation and spatial autocorrelation techniques. The sea area is divided into various grids, and in each grid, the mapping relationships between the number and severity of the traffic accidents and the traffic characteristics are established. Machine learning (ML) technology is used to assess whether a grid area is an accident-prone area and to predict accident severity in each grid. The accident prediction of different ML models, including random forest (RF) model, Adaboost model, gradient boosting decision tree (GBDT) model, and Stacking combined model, were compared. The optimality of the Stacking combined model was verified by comparing the experimental results of this model with those of classical prediction models, convolutional neural network (CNN), long short term memory (LSTM), and support vector machine (SVM). According to the results, the maritime accident data set of the entire Fujian sea area shows typical clustering characteristics and positive spatial correlation. That is, the kernel density estimation indicates that subareas, including the Ningde sea area, Fuzhou sea area, and Xiamen sea area, generally have high densities of maritime accidents and the highest risk value within the whole Fujian sea area. High-high accident clustering, that is high cluster areas neighbored by other areas of high cluster, is mainly seen in the Ningde and Fuzhou sea areas, while the Xiamen, Putian, and Zhangzhou subareas show low-low clustering, which are low clusters neighbored by low clusters. Among the ML models, the Stacking combined model shows high accuracy, precision, recall, and F1-score values of 0.912, 0.910, 0.912, and 0.904 in predicting whether a grid area is an accident-prone area and 0.750, 0.745, 0.750, and 0.746 in predicting the accident severity in the grid, indicating its superior maritime traffic accident prediction performance. Based on our analysis of the distribution characteristics and geospatial data, our proposed method demonstrates effective and reliable risk prediction.