Scalable Maritime Traffic Map Inference and Real-time Prediction of Vessels' Future Locations on Apache Spark

Abstract

In this paper, we propose scalable algorithms allowing primo to infer a map of vessels' trajectories and secundo to predict future locations of a vessel on sea. Our system is based on Apache Spark -a fast and scalable engine for large-scale data processing. The training dataset is event-based. Each event depicts the GPS position of the vessel at a timestamp. We propose and implement a workflow computing trips' patterns, with GPS locations of each trip summarized using GeoHashing. The latter is an efficient encoding of a geographic location into a short string of letters and digits. In order to perform prediction queries efficiently, we propose (i) a geohash positional index which maps each geohash to a list of pairs (trip-pattern-identifier, offset of the geohash in the geohash sequence of the trip-pattern), (ii) a departure-port index which maps each departure port to a list of trip-patterns' identifiers, as well as (iii) a pairwise geohash sequence alignment allowing to score the similarity of two geohash-sequences using queen-spatial neighborhood.