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Maritime Traffic Models for Vessel-to-Vessel Distances

机译:船舶到船舶距离的海事流量模型

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摘要

The maritime traffic is significantly increasing in the recent decades due to its advantageous features related to costs, delivery rate and environmental compatibility. The Vessel Traffic System (VTS), mainly using radar and AIS (Automatic Identification System) data, provides ship's information (identity, location, intention and so on) but is not able to provide any direct information about the way in which ships are globally positioned, i.e. randomly distributed or grouped/organized in some way, e.g. following routes. This knowledge can be useful to estimate the mutual distances among ships and the mean number of surroundings vessels, that is the number of marine radars in visibility. The AIS data provided by the Italian Coast Guard show a Gamma-like distribution for the mutual distances whose parameters can be estimated through the Maximum-Likelihood method. The truncation of the Gamma model is a useful tool to take into account only ships in a relatively small region. The result is a simple one-parameter distribution able to provide indications about the traffic topology. The empirical study is confirmed by a theoretical distribution coming from the bi-dimensional Poisson process with ships being randomly distributed points on the sea surface.
机译:由于其与成本,运输率和环境兼容性相关的有利功能,近几十年来海上流量显着增加。船只交通系统(VTS),主要使用雷达和AIS(自动识别系统)数据,提供船舶的信息(身份,位置,意图等),但无法提供有关船舶在全球范围内的方式的任何直接信息定位,即以某种方式随机分布或分组/组织,例如遵循路线。这种知识可用于估计船舶之间的相互距离和周围血管的平均数量,即可见性的海洋雷达数量。意大利海岸警卫队提供的AIS数据显示了一种类似伽留的分布,用于通过最大似然方法估算参数的相互距离。伽马模型的截断是一个有用的工具,只考虑在相对较小的区域中的船只。结果是一种简单的一个参数分布,能够提供关于交通拓扑的指示。经验研究通过来自双维泊松过程的理论分布,船舶在海面上随机分布点。

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