Mining Port Congestion Indicators from Big AIS Data

Abstract

In this paper, we introduce three maritime Port Congestion Indicators (PCIs) mined using Automatic Identification System (AIS) static and dynamic messages. The proposed indicators are spatial complexity, spatial density, and time criticality. To calculate the PCIs, we proposed three Big AIS Data mining algorithms to find the geohash area for certain precision, the convex hull area, and the average vessels proximity within the Port Area of Interest (AOI) and in the Period of Interest (POI). The indicators are calculated for the year of 2015 for three ports (Halifax, Hong Kong, and Singapore). The proposed PCIs capture the spatial complexity, spatial density, and time of service criticality. These indicators can be used by port authorities and other maritime stakeholders to alert for congestion levels that can be correlated to weather, high demand, or a sudden collapse in capacity due to strike, sabotage, or other disruptive events. We clustered the indicators for each port into three colour-coded (Green, Yellow, and Red) clusters corresponding to low, medium and high congestion levels. The centroids of these clusters can be used to predict future congestion levels of the port under consideration. To the best of our knowledge in published literature, this work is the first to introduce the application of AIS Big Data analytics to evaluate maritime port congestion levels.