Historical Knowledge Graphs for Global Maritime Estimated Time of Arrival

2026-05-18

AI Summary

This study addresses the challenge of limited low-cost contextual data for accurate vessel arrival time prediction, which hinders port operational efficiency and decarbonization efforts. Leveraging only Automatic Identification System (AIS) data, the authors construct a queryable global maritime knowledge graph by innovatively integrating Gaussian Mixture Models for trajectory segmentation, geohash-3 encoded geographic nodes, and hierarchical historical speed distributions stratified by vessel type, heading, and sailing duration. A hierarchical fallback querying mechanism is introduced to enhance robustness. Evaluated on time-preserving and external test sets, the approach achieves median segment-level and trajectory-level RMSEs of 22.75 and 30.90 minutes, respectively, with 69.1% of predictions falling within 20% of actual arrival times, demonstrating high accuracy and strong generalization capability.

Abstract

Accurate vessel estimated-time-of-arrival forecasts are critical for port operations and decarbonization, yet global-scale travel-time prediction remains difficult without costly contextual data. Herein, I present a methodology for constructing a historical maritime knowledge graph using only Automatic Identification System (AIS) data. First, segmented trajectories are extracted from noisy AIS data using a Gaussian-mixture-model-based preprocessing pipeline. The graph is then constructed by iteratively processing the trajectories and storing speed distributions stratified by vessel type, time of travel, and direction of travel; the resulting global graph comprises 5,433 geohash-3 nodes and 12,334 edges. The graph can be queried to retrieve travel-time predictions between any two location via a hierarchical, priority-based system that uses historical statistics with principled fallback. On a temporally held-out test set, median RMSE is 22.75 min (segment-level) and 30.90 min (trajectory-level), with 69.1% of trajectories within 20% of actual arrival time. On a second external test set, median RMSE is 27.36 min (segment-level) and 37.46 min (trajectory-level), with 62.1% of trajectories within 20%. These results corroborate the promise of our method, enabling global travel-time prediction and providing a strong foundation for just-in-time arrival planning and emissions reduction.

Problem

• estimated time of arrival
• maritime travel-time prediction
• global-scale prediction
• port operations
• decarbonization

Innovation

• maritime knowledge graph
• estimated time of arrival
• AIS data
• travel-time prediction
• Gaussian mixture model