A multi-stage data mining approach for liquid bulk cargo volume analysis based on bill of lading data

作者：

Highlights：

• Liquid bulk cargo (LBC) is a valuable and competitive cargo in maritime logistics.

• A multi-stage data mining framework for LBC volume analysis is proposed.

• The new item segmentation system enables accurate aggregation of LBC volume.

• The exploratory volume analysis provides spatial and temporal effects on LBC volume.

• The proposed prediction models increased the prediction accuracy of LBC volume.

摘要

•Liquid bulk cargo (LBC) is a valuable and competitive cargo in maritime logistics.•A multi-stage data mining framework for LBC volume analysis is proposed.•The new item segmentation system enables accurate aggregation of LBC volume.•The exploratory volume analysis provides spatial and temporal effects on LBC volume.•The proposed prediction models increased the prediction accuracy of LBC volume.

论文关键词：Maritime logistics,Liquid bulk cargo volume,Data mining,Item segmentation,Exploratory volume analysis,Volume prediction

论文评审过程：Received 15 August 2020, Revised 27 November 2020, Accepted 27 May 2021, Available online 7 June 2021, Version of Record 22 June 2021.

论文官网地址：https://doi.org/10.1016/j.eswa.2021.115304