Li, H, Zhang, Y, Li, Y, Lam, JSL, Matthews, C and Yang, Z (2025) Deep multi-view information-powered vessel traffic flow prediction for intelligent transportation management. Transportation Research Part E: Logistics and Transportation Review, 197. ISSN 1366-5545

Preview

Text 1-s2.0-S1366554525001139-main.pdf - Published Version Available under License Creative Commons Attribution. Download (17MB) | Preview

Abstract

Vessel traffic flow (VTF) prediction, essential for intelligent transportation management, is derived from the statistical analysis of longitude and latitude information from Automatic Identification System (AIS) data. Traditional deep learning approaches have struggled to effectively capture the intricate and dynamic characteristics inherent in VTF data. To address these challenges, this paper proposes a new prediction model called a Multi-view Periodic-Temporal Network with Semantic Representation (i.e., MPTNSR), which leverages three perspectives: periodic, temporal, and semantic. VTF typically conceals the periodic and temporal characteristics during its evolution. A Convolutional Neural Network and Bidirectional Long Short-Term Memory (CNN-BiLSTM) model, constructed from periodic and temporal views, effectively captures this information. However, real-world scenarios frequently involve predicting VTF for multiple target regions simultaneously, where correlations between VTF changes in different areas are significant. The semantic view seeks to extract relationships across different channels based on the similarity of VTF data fluctuations and geographical distribution across regions, utilising a Graph Convolutional Network (GCN). The final prediction result is generated by fusing the information from these three views. Additionally, an optimised loss function is developed in the MPTNSR model that integrates local and global measurement information. In summary, the proposed model combines the strengths of a multi-view learning network and an optimised loss function. Quantitative comparative experiments demonstrate that the MPTNSR model outperforms eighteen state-of-the-art methods in VTF prediction tasks. To enhance the model’s scalability, Graphics Processing Unit (GPU)-accelerated computation is introduced, significantly improving its efficiency and reducing its running time. The model enables accurate and robust prediction, effectively assisting in port planning and waterway management, thereby enhancing the safety and sustainability of maritime transportation.

Item Type:	Article
Uncontrolled Keywords:	0102 Applied Mathematics; 0103 Numerical and Computational Mathematics; 1507 Transportation and Freight Services; Logistics & Transportation; 3509 Transportation, logistics and supply chains
Subjects:	T Technology > TA Engineering (General). Civil engineering (General) T Technology > TJ Mechanical engineering and machinery V Naval Science > VM Naval architecture. Shipbuilding. Marine engineering
Divisions:	Engineering
Publisher:	Elsevier
SWORD Depositor:	A Symplectic
Date Deposited:	21 Mar 2025 12:39
Last Modified:	21 Mar 2025 12:45
DOI or ID number:	10.1016/j.tre.2025.104072
URI:	https://researchonline.ljmu.ac.uk/id/eprint/25950

View Item