Geographical spatial analysis and risk prediction based on machine learning for maritime traffic accidents: A case study of Fujian sea area

作者：Yang Yang, Zheping Shao, Yu Hu, Qiang Mei, Jiacai Pan, Rongxin Song, Peng Wang · 发表于：Ocean Engineering · 年份：2022 · DOI：10.1016/j.oceaneng.2022.113106 · 被引用次数：70 · 研究领域：Maritime Navigation and Safety、Traffic and Road Safety、Maritime Ports and Logistics

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...