训练船航线最近邻搜索预测目的地

Valentin Rosca, Emanuel Onica, Paul Diac, Ciprian Amariei
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引用次数: 2

摘要

2018年DEBS挑战赛以海上航线预测为背景。船舶航线建模为从真实世界跟踪数据中选择的自动识别系统(AIS)数据点流。这一挑战要求尽可能早地正确估计目的港口和船舶到达时间。我们提出的解决方案按照报告的目的港划分训练船路线,并使用最近邻搜索来查找离查询AIS点更近的训练船路线。还包括一些特殊的改进,例如避免在一个查询路由中频繁更改预测端口的方法,以及通过使用遗传算法自动调整参数。这将导致最终分数的显著提高。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Predicting Destinations by Nearest Neighbor Search on Training Vessel Routes
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.
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