Grand Challenge: Predicting Destinations by Nearest Neighbor Search on Training Vessel Routes

被引：1

作者：

Rosca, Valentin ^{[1
]}

Onica, Emanuel ^{[1
]}

Diac, Paul ^{[1
]}

Amariei, Ciprian ^{[1
]}

机构：

[1] Alexandru Ioan Cuza Univ, Iasi, Romania

来源：

DEBS'18: PROCEEDINGS OF THE 12TH ACM INTERNATIONAL CONFERENCE ON DISTRIBUTED AND EVENT-BASED SYSTEMS | 2018年

基金：

欧盟地平线“2020”;

关键词：

machine learning; ball trees; genetic algorithm; space partitioning; nearest neighbor;

D O I：

10.1145/3210284.3220509

中图分类号：

TP31 [计算机软件];

学科分类号：

081202 ; 0835 ;

摘要：

The DEBS Grand Challenge 2018 is set in the context of maritime route prediction. Vessel routes are modeled as streams of Automatic Identification System (AIS) data points selected from real-world tracking data. The challenge requires to correctly estimate the destination ports and arrival times of vessel trips, as early as possible. Our proposed solution partitions the training vessel routes by reported destination port and uses a nearest neighbor search to find the training routes that are closer to the query AIS point. Particular improvements have been included as well, such as a way to avoid changing the predicted ports frequently within one query route and automating the parameters tuning by the use of a genetic algorithm. This leads to significant improvements on the final score.

引用

页码：224 / 225

页数：2