Work

A hybrid container throughput forecasting approach using bi-directional hinterland data of port

Year: 2024

Type: article

Abstract: Accurate forecasting of port container throughput plays a crucial role in optimising port operations, resource allocation, supply chain management, etc. However, existing studies only focus on the impact of port hinterland economic development on container throughput, ignoring the impact of port foreland. This study proposed a container throughput forecasting model based on deep learning, which considers the impact of port hinterland and foreland on container throughput. Real-world experimental results showed that the proposed model with multiple data sources outperformed other forecasting methods, achieving significantly higher accuracy. The implications of this study are significant for port authorities, logistics companies, and policymakers.

Source: Scientific Reports

DOI: 10.1038/s41598-024-77376-9

Authors: Fangli Zeng, Shuojiang Xu

Institutions: Zhejiang Wanli University, Guilin University of Electronic Technology

Language: English

Topic: Maritime Ports and Logistics

Subfield: Industrial and Manufacturing Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $2,590