RESUMEN
Intelligent maritime transportation is one of the most promising enabling technologies for promoting trade efficiency and releasing the physical labor force. The trajectory prediction method is the foundation to guarantee collision avoidance and route optimization for ship transportation. This article proposes a bidirectional data-driven trajectory prediction method based on Automatic Identification System (AIS) spatio-temporal data to improve the accuracy of ship trajectory prediction and reduce the risk of accidents. Our study constructs an encoder-decoder network driven by a forward and reverse comprehensive historical trajectory and then fuses the characteristics of the sub-network to predict the ship trajectory. The AIS historical trajectory data of US West Coast ships are employed to investigate the feasibility of the proposed method. Compared with the current methods, the proposed approach lessens the prediction error by studying the comprehensive historical trajectory, and 60.28% has reduced the average prediction error. The ocean and port trajectory data are analyzed in maritime transportation before and after COVID-19. The prediction error in the port area is reduced by 95.17% than the data before the epidemic. Our work helps the prediction of maritime ship trajectory, provides valuable services for maritime safety, and performs detailed insights for the analysis of trade conditions in different sea areas before and after the epidemic.
RESUMEN
Intelligent maritime transportation is one of the most promising enabling technologies for promoting trade efficiency and releasing the physical labor force. The trajectory prediction method is the foundation to guarantee collision avoidance and route optimization for ship transportation. This article proposes a bidirectional data-driven trajectory prediction method based on Automatic Identification System (AIS) spatio-temporal data to improve the accuracy of ship trajectory prediction and reduce the risk of accidents. Our study constructs an encoder-decoder network driven by a forward and reverse comprehensive historical trajectory and then fuses the characteristics of the sub-network to predict the ship trajectory. The AIS historical trajectory data of US West Coast ships are employed to investigate the feasibility of the proposed method. Compared with the current methods, the proposed approach lessens the prediction error by studying the comprehensive historical trajectory, and 60.28% has reduced the average prediction error. The ocean and port trajectory data are analyzed in maritime transportation before and after COVID-19. The prediction error in the port area is reduced by 95.17% than the data before the epidemic. Our work helps the prediction of maritime ship trajectory, provides valuable services for maritime safety, and performs detailed insights for the analysis of trade conditions in different sea areas before and after the epidemic.
RESUMEN
Wet wipes have been widely used in consumers' daily lives, especially confronted with the COVID-19 pandemic. Most wet wipes contain plastic components, which cause salient plastic consumption and environmental pollution after their usage. In the current study, the discarding stage of wet wipes in China is investigated systematically. The waste flow of wet wipes are described in detail for the first time, based on the results of domestic waste composition field survey and 2461 consumer questionnaires. The decision-making mechanism for consumers’ wet wipes discarding behavior has been established, and the impacts of various factors have been quantified and analyzed by using structural equation modeling. Results show that waste wet wipes account for 0.2% of all domestic waste in China. Consumer behavior, which is determined by 12 influencing factors, will affect the flow of waste wet wipes substantially. All factors are divided into three categories, including “consumption habit”, “cost” and “incentive”. Among all factors, “time cost” is the most crucial one which indicates matching the convenient solid waste treatment infrastructure is an effective way to ameliorate environmental leakage. Popularizing the knowledge that wet wipes contain plastic through various ways such as printing the composition on packaging bags is a feasible approach. In order to mitigate the environmental impacts, a systematic solution integrating production, consumption and disposal processes of wet wipes is necessary.