Adaptive Risk Model for Inspected Small Passenger Vessels
Description
A lack of a comprehensive data-driven tool for informing marine inspection policy has left policymakers to make decisions based on qualitative and anecdotal information, resulting in less-than-optimal allocation of limited marine inspection resources.
Detailed example
Numerical score that compares vessels predicted safety risk relative to each other.
AI / analytics pattern
Classical/Predictive Machine Learning: Models trained on data to make predictions or classifications based on identified patterns or relationships.
Automation level / stage
c) Deployed – The use case is being actively authorized or utilized to support the functions or mission of an agency.
Expected benefit
The Small Passenger Vessels Safety Task Force uses machine learning and expert input to build a flexible analysis tool that identifies the main causes of marine casualties and calculates a risk score for each vessel in the largest segment of the U.S.-inspected fleet. By using a logistic regression–based model with basic machine learning, this effort improves how inspectors are allocated, sharpens the focus on higher-risk vessels, and strengthens oversight to improve passenger safety.
Controls / human review
ATO: No; PIA: Not published
Data needed
Commercial vessel profiles including: engineering, life saving, propulsion, fire protection, manning, operating routes, plan review, and USCG inspection activity details.