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.

由于 LBC 是一种有价值且需求量大的货物，因此液体散装货物 (LBC) 的体积分析最近受到了极大的关注。因此，建立 LBC 量分析系统非常重要，因为它可以帮助为港口规划和管理策略提供信息。尽管如此，LBC 体积分析对研究人员来说是一项具有挑战性的任务，因为 LBC 体积的趋势高度波动且不稳定。在本文中，提出了一种基于提单 (BL) 数据进行信息性 LBC 体积分析的新框架，该框架由三部分组成：项目分割、探索性体积分析和体积预测。首先，开发了一种创新的利用BL数据的项目文本的项目分割系统，该系统可以生成现有系统无法提供的LBC项目的子类别和类别信息。下一个，进行探索性体积分析以了解每个分类和子分类项目在地理和时间线方面的体积特征。最后，与现有的统计模型相比，提出了基于流形学习和深度学习的时间序列技术来提高 LBC 体积预测精度。体积预测的实验结果表明，与基线模型相比，类别和子类别级别的准确率平均提高了 34% 和 18%。相信我们提出的方法将有助于海运物流的利益相关者，为他们提供做出更好决策所需的洞察力。与现有的统计模型相比，提出了基于流形学习和深度学习的时间序列技术来提高 LBC 体积预测精度。体积预测的实验结果表明，与基线模型相比，类别和子类别级别的准确率平均提高了 34% 和 18%。相信我们提出的方法将有助于海运物流的利益相关者，为他们提供做出更好决策所需的洞察力。与现有的统计模型相比，提出了基于流形学习和深度学习的时间序列技术来提高 LBC 体积预测精度。体积预测的实验结果表明，与基线模型相比，类别和子类别级别的准确率平均提高了 34% 和 18%。相信我们提出的方法将有助于海运物流的利益相关者，为他们提供做出更好决策所需的洞察力。