基于数据挖掘的水上交通肇事逃逸船舶自动追踪方法研究

项目名称： 基于数据挖掘的水上交通肇事逃逸船舶自动追踪方法研究

项目编号： No.51309044

项目类型： 青年科学基金项目

立项/批准年度： 2014

项目学科： 水利工程

项目作者： 朱飞祥

作者单位： 大连海事大学

项目金额： 25万元

中文摘要： 水上船舶肇事逃逸案件危害大，查获率低，其主要原因是海事管理机构缺少或不能及时获取目击证人提供的办案线索。本项目首次提出根据船舶行为知识自动快速识别肇事逃逸嫌疑船舶的研究思路，尝试从大量水上交通数据中构建肇事逃逸船舶行为特征模型，并探索该行为特征的高效检测算法。首先，提出建立水上交通数据综合预处理机制，构建数据质量评价模型，为项目实施提供高质量数据保障；其次，针对船舶行为分析过程中存在的"维数灾难"问题，提出利用粗糙集理论约简逃逸船舶行为属性的降维方法，揭示特征属性及其相互作用关系；再次，研制新的聚类算法对确定的特征属性集从单维或多维的角度分别建立正常和肇事逃逸船舶行为特征模型；最后，提出误用检测和异常检测相结合的、高效的分层次检测算法，克服海量数据计算开销大的问题。本课题可望对高维复杂结构数据的挖掘理论研究取得突破，为船舶异常行为检测研究提供新思路，对提高我国海事监管能力具有一定促进意义。

中文关键词： 船舶自动识别系统；船舶行为；航迹压缩；；

英文摘要： Poperty damage or personal injuries in a ship hit-and-run accident are very serious，however the case dection rate is always low. The most important reason to restrain dection rate is that Chinese maritime safety administration can not timely get the evidence provided by witness. This research project proposes an original idea that suspect ships can be quickly automatic identified according to ship behavior knowledge. This research project explores to mine the hit-and-run ship's behaviors feature model from massive waterborne traffic data sets and deeply analyzes the efficient detection algorithm for the model. First of all, the waterborne traffic data pre-processing mechanism is researched and data quality assessment model is proposed for data quality assurance; Second, in order to overcome the dimensionality curse problem in analysis of ship's behaviors, the data reduction algorithm based on Rough Set theory is designed and applied to reduce the dimension of ship's behaviors . The features of the hit-and-run ship's behaviors are discovered and the relationship of features is analysised; Third, a new clustering algorithm designed in this research project is applied to single-dimension or multi-dimensions feature data for building the normal and the hit-and-run ship's behaviors feature model. Finally, the efficie

英文关键词： Automatic Identification System；Ship Behavior；Trajectory Compression；；