Can AIS data improve the short-term forecast of weekly dry bulk cargo port throughput? - a machine-learning approach

This study examines the development of a machine-learning model to forecast weekly throughputs of dry bulk cargo in the short term based on automatic identification system (AIS) data. Specifically, the weekly amounts of iron ore exported from several major ports in Australia and Brazil in the latter half of 2019 are forecasted three weeks in advance using a long short-term memory model. We examine many variables extracted from AIS data, including the vessel position, speed, draught, and destination, as the input features of the model. Consequently, we develop a highly accurate forecasting model that uses four influential variables derived from AIS data, namely, vessel traffic around the target port and in the region, vessel traffic at major partner import ports, and vessel traffic at the target port during the past year. Finally, by forecasting the weekly port cargo throughputs in the first half of 2020, which was affected by the COVID-19 pandemic, the applicability of the model is confirmed, even for ports where the throughput fluctuates significantly. In particular, this study demonstrates that AIS data are beneficial not only as a real-time traffic database but also as a database containing various related explanatory variables, including historical vessel traffic. [ FROM AUTHOR] Copyright of Maritime Policy & Management is the property of Routledge and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Editorial: Logistics connectivity in East Asia: practices and challenges

In Tomoya Kawasaki, Takuma Matsuda, Yui-yip Lau and Xiaowen Fu (The durability of economic indicators in container shipping demand: a case study of East Asia–US container transport), the durability of economic indicators on container movements from East Asia to the USA are identified by a vector autoregression model using monthly-based time-series data. Takuma Matsuda, Enna Hirata and Tomoya Kawasaki aim to contribute to the empirical literature on the container shipping industry market structure (Monopoly in the container shipping market: an econometric approach). Phong Nha Nguyen and Hwayoung Kim aim to identify the characteristics of the maritime shipping network in Northeast Asia as well as compare the level of port connectivity among these container ports in the region (Analyzing the international connectivity of the major container ports in Northeast Asia).