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Maritime ETA prediction system uses historical knowledge graph

By PulseAugur Editorial · [1 sources] ·

A new methodology uses Automatic Identification System (AIS) data to construct a historical maritime knowledge graph for predicting vessel estimated-time-of-arrival (ETA). This graph, comprising over 5,000 nodes and 12,000 edges, stores speed distributions stratified by vessel type, time, and direction. The system achieved a median RMSE of 22.75 minutes for segment-level predictions and 30.90 minutes for trajectory-level predictions on a test set, demonstrating its potential for optimizing port operations and reducing emissions through just-in-time arrival planning. AI

IMPACT Enables more accurate global travel-time predictions for maritime logistics and emissions reduction.

RANK_REASON The cluster contains an academic paper detailing a new methodology for maritime ETA prediction. [lever_c_demoted from research: ic=1 ai=0.7]

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Maritime ETA prediction system uses historical knowledge graph

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The cluster contains an academic paper detailing a new methodology for maritime ETA prediction. [lever_c_demoted from research: ic=1 ai=0.7]
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131 days old
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COVERAGE [1]

  1. arXiv cs.CV TIER_1 English(EN) · Neofytos Dimitriou ·

    Historical Knowledge Graphs for Global Maritime Estimated Time of Arrival

    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 gra…

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