Predictive Analysis of Maritime Congestion Using Dynamic Big Data and Multiscale Feature Analysis

• Published in: Journal of electrical and computer engineering 2024-08, Vol.2024 (1)
• Main Author: Wu, Yalin
• Format: Article
• Language: English
• Subjects: Accuracy; Algorithms; Artificial neural networks; Autoregressive moving average; Big Data; Classification; Clustering; Data analysis; Deep learning; Efficiency; Global economy; Information management; Machine learning; Maritime industry; Methods; Neural networks; Noise prediction; Oceans; Performance prediction; Real time; Sea vessels; Support vector machines; Time series; Traffic congestion; Traffic flow; Traffic information; Trajectory analysis; Trends; Wavelet analysis; Wavelet transforms
• ISSN: 2090-0147; 2090-0155
• DOI: 10.1155/2024/5225558

The maritime industry is one of the most crucial sectors in the global economy, facilitating the transportation of goods and commodities across vast distances. However, maritime network congestion has become an increasingly critical challenge that significantly affects shipping efficiency and the overall operational performance of the industry. This study proposes an innovative congestion prediction approach using dynamic big data analysis of vessel trajectories and multiscale feature analysis. First, the dynamic analysis of vessel trajectories aims to extract valuable information from ships’ data as they navigate the oceans, enabling proactive traffic management and optimized routing. Second, the multiscale feature analysis provides a comprehensive understanding of maritime network congestion by examining it from different perspectives and scales, leading to more accurate predictions and effective congestion management strategies. Furthermore, this study introduces an enhanced Faster R‐CNN vessel detection model for real‐time tracking, integrating convolutional and SKNet networks. To improve short‐term traffic flow prediction accuracy, this study employs multiscale feature analysis through wavelet transformation. The foundational traffic data undergo wavelet decomposition for a detailed representation across frequencies. Gated recurrent unit (GRU) neural network and autoregressive moving average (ARMA) models are utilized to predict trend and noise components, respectively. Fusion of predictions demonstrates superior accuracy and is validated against real data. Empirical results showcase minimal errors and heightened prediction accuracy compared to actual data.