Some methods for addressing errors in static AIS data records

Abstract

The Automatic Identification System (AIS) provides essential services in support of maritime domain awareness. Accurate AIS values for hull dimension and type are often critical for safe and efficient management of ship traffic, and for development of new artificial intelligence maritime algorithms. AIS variables are subject to faults from multiple sources, ranging from bad weather to human error. New heuristic methods for correcting ship draft, beam, and class were developed and evaluated, using AIS data in the vicinity of large Florida ports as a test bed. Novel low order polynomials for 8 broad functional vessel classes yielded predicted values for draft and beam as functions of vessel length. The majority of relative differences between predicted and reported values were <0.1. A logistic regression (LR) multiclass classification scheme using the residuals from these polynomial predictions generally showed good agreement between estimated and reported vessel class. The LR scheme demonstrated skill in verifying AIS-transmitted classification, detecting incorrectly classified vessels, and flagging those with incorrect draft or operating near an extreme draft. A diagnostic of reports whose classification had very low and very high confidence suggested directions for further improvement of the algorithm. A new hierarchy for processed AIS data is proposed.