Ship data obtained through the maritime sector will inevitably have missing values and outliers, which will adversely affect the subsequent study. Many existing methods for missing data imputation cannot meet the requirements of ship data quality, especially in cases of high missing rates. In this paper, a missing data imputation method based on generative adversarial networks (GANs) is proposed. The generative adversarial imputation network (GAIN) is improved using the Wasserstein distance and gradient penalty to handle missing values. Meanwhile, the data preprocessing process is optimized by combining knowledge from the ship domain, such as using isolation forests for anomaly detection. Statistical analysis of ship data is also conducted, including correlation analysis of ship design parameters, analysis of outliers, and analysis of missing data types. These analyses provide the basis for the proposed model. In a case study of 8167 bulk carriers, the proposed model outperformed the missing forest (MF) and polynomial fitting (PF) models, with an average error reduction of 2.4% and 6.3%, respectively. The proposed model also showed stable performance in cases of high missing rates. This study provides a new approach for estimating or imputing critical parameters of ships.

An active area of study under the dual carbon target, which is based on automatic identification systems (AIS), is the emission inventory of pollutants from ships. Data compression is required because there is currently so much data that it has become difficult to transmit, process, and store it. A trajectory simplification method considering the ship sailing state and acceleration rate of change is developed in this paper to assure the validity of the compressed data used in the emission inventory analysis. By carefully examining the integral relationship between acceleration and pollution emissions, the algorithm constructs an acceleration rate of change function for data compression and categorizes AIS data by ship navigation status. By dynamically altering the amount of acceleration change, the developed function can stabilize the pollutant emission calculation error and adaptively calculate the threshold value. The experimental results show that the emission calculation error of the proposed algorithm is only 0.185% when the compression rate is 90.28%.

Accurate traffic flow forecasting is pivotal for intelligent traffic control and guidance. Manually capturing the intricate dependencies between spatial and temporal dimensions in traffic data presents a significant challenge. Prior methods have primarily employed Recurrent Neural Networks or Graph Convolutional Networks, without fully accounting for the interdependency between spatial and temporal factors. To address this, we introduce a novel Hierarchical Spatial-Temporal Neural Networks with Attention Mechanism model (HSTAN). This model concurrently captures temporal correlations and spatial dependencies using a multi-headed self-attention mechanism in both temporal and spatial terms. It also integrates global spatial-temporal correlations through a hierarchical structure with residuals. Moreover, the analysis of attention weight matrices can depict complex spatial-temporal correlations, thereby enhancing our traffic forecasting capabilities. We conducted experiments on two publicly available traffic datasets, and the results demonstrated that the HSTAN model’s prediction accuracy surpassed that of several benchmark methods.