A Multi-stage Data Mining Approach for Liquid Bulk Cargo Volume Analysis based on Bill of Lading Data

Abstract

Liquid bulk cargo (LBC) volume analysis has received considerably great attention recently since LBC is a valuable and high-demand cargo. Thus, it is important to establish an analysis system for LBC volume, as it can help inform strategies for port planning and management. Nevertheless, LBC volume analysis is a challenging task for researchers because trends in LBC volume are highly volatile and non-stationary. In this paper, a new framework for enabling informative LBC volume analysis based on bill of lading (BL) data is proposed, which consists of three parts: item segmentation, exploratory volume analysis, and volume prediction. Firstly, an innovative item segmentation system using item texts of BL data was developed, which can generate subcategory as well as category information of LBC items that existing system cannot provide. Next, exploratory volume analysis was performed to understand the volume characteristics of each categorized and subcategorized item in terms of geography and timeline. Lastly, manifold learning- and deep learning-based time series techniques were proposed to increase LBC volume prediction accuracy compared with existing statistical models. The experimental results for volume prediction show the accuracy increased by 34% and 18% in average at category and subcategory levels over baseline models. It is believed that our proposed method will be helpful for stakeholders in maritime logistics, giving them the insights that they need to make better decisions.