AIS Data Compression for Vessel Traffic Pattern Recognition

Solution Overview

Problem

Conventional data mining techniques require significant time and computational resources to identify vessel traffic patterns from large-scale AIS data, and the raw AIS data often contains anomalies and storage challenges due to increasing data volumes.

Innovation Solution

A method for vessel traffic pattern recognition that involves data quality control and data compression, including sorting and segmenting AIS data points, repairing missing segments using cubic spline interpolation, and compressing trajectories with the Douglas-Peucker algorithm, followed by clustering using the Quick Bundles algorithm to efficiently extract traffic patterns.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If conventional data mining techniques are used to identify vessel traffic patterns from large-scale AIS data, then the accuracy of traffic pattern recognition is improved, but the time cost and computational cost increase significantly

Engineering Contradiction:

Improvetraffic pattern recognition accuracyVSAvoiddata processing time

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The patent applies preliminary data preprocessing actions including quality control, anomaly detection, and compression before the main traffic pattern recognition process. By pre-processing the AIS data to remove anomalies and compress the trajectory information, the system reduces the computational burden on subsequent data mining operations, thereby decreasing processing time while preserving the accuracy needed for reliable pattern identification

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent extracts and removes anomaly data points from the AIS dataset through quality control mechanisms. By identifying and eliminating erroneous trajectory points, the system separates the useful data from harmful anomalies, allowing the data mining process to operate on cleaner, more efficient data without sacrificing recognition accuracy

Inventive Principle:

Principle #2Taking out (Extraction)

2Measurement precision

If conventional data mining techniques are used to identify vessel traffic patterns from large-scale AIS data, then the accuracy of traffic pattern recognition is improved, but the computational cost increases significantly

Engineering Contradiction:

Improvetraffic pattern recognition accuracyVSAvoidcomputational resource consumption

Core Design Contradiction:

Measurement precisionVSUse of energy by moving object

Solution Approach 1:

The patent extracts and removes anomaly data points from the AIS dataset through quality control mechanisms. By identifying and eliminating erroneous trajectory points, the system separates the useful data from harmful anomalies, allowing the data mining process to operate on cleaner, more efficient data without sacrificing recognition accuracy

Inventive Principle:

Principle #2Taking out (Extraction)

Solution Approach 2:

The patent applies preliminary data preprocessing actions including quality control, anomaly detection, and compression before the main traffic pattern recognition process. By pre-processing the AIS data to remove anomalies and compress the trajectory information, the system reduces the computational burden on subsequent data mining operations, thereby decreasing processing time while preserving the accuracy needed for reliable pattern identification

Inventive Principle:

Principle #10Preliminary action

3Loss of information

If raw AIS data is stored without compression to maintain data quality, then the data completeness is improved, but the storage space requirement increases

Engineering Contradiction:

Improvedata completenessVSAvoiddata storage space

Core Design Contradiction:

Loss of informationVSVolume of stationary object

Solution Approach 1:

The patent changes the parameter representation of trajectory data by compressing coordinate sequences and utilizing spatial-temporal correlations in vessel movement patterns. This parameter transformation reduces the storage volume of AIS data while maintaining the essential information needed for traffic pattern recognition, effectively balancing data completeness with storage efficiency

Inventive Principle:

Principle #35Parameter changes

Data Source

PatentUS12057019B2Method for vessel traffic pattern recognition via data quality control and data compression

Publication Date: 2024.08.06 SHANGHAI SHIP & SHIPPING RES INST CO LTD

AI summary

The present invention provides a vessel traffic pattern identification method via data quality control and data compression, and includes the steps of assorting a collection of Automatic Identification System (AIS) data points according to Maritime Mobile Service Identity (MMSI) code; sorting each collection result by time ascending order; deleting duplicated vessel AIS data points considering time stamp, latitude, longitude and vessel speed over ground; segmenting vessel trajectories; obtaining high-quality AIS data with an AIS data anomaly detection; repairing and compressing each vessel trajectory with the Douglas-Peucker algorithm; clustering vessel trajectories with the Quick Bundles algorithm; and identifying a maritime traffic pattern. The invention can efficiently identify vessel traffic patterns and help maritime traffic management departments to accurately identify a traffic situation.