面向车辆轨迹数据的违法取证分析系统

发布时间：2018-05-01 02:30

本文选题：大数据 + 数据挖掘　；参考：《计算机工程》2017年05期

【摘要】：针对现有公安违法取证分析系统存在效率低下、功能单一的问题,提出基于云平台和大数据的违法取证分析系统。利用Mongo DB存储原始海量的车辆轨迹数据,采用MapReduce分布式计算对轨迹数据进行挖掘与筛选,通过大数据平台上的系统调度器对系统各层资源进行有效调度并监控系统运行状态。基于提取的离散轨迹数据对违反规定时间行驶、违法规定路线行驶、疲劳驾驶3种交通违法行为进行取证分析,从而找出违法车辆。实验结果表明,当车辆轨迹数据规模较大时,该系统仍具有较高的运行效率及稳定性。
[Abstract]:In view of the problems of low efficiency and single function in the existing public security illegal forensics analysis system, a criminal forensics analysis system based on cloud platform and big data is put forward. Mongo DB is used to store the original massive vehicle trajectory data, MapReduce distributed computing is used to mine and filter the track data, and the system scheduler on big data platform is used to effectively schedule the system resources and monitor the system running state. Based on the collected discrete track data, three kinds of illegal traffic behaviors, such as breaking the prescribed time, illegal route and fatigue driving, are analyzed to find out the illegal vehicles. The experimental results show that the system still has high efficiency and stability when the vehicle track data scale is large.
【作者单位】：武汉大学计算机学院;公安部交通管理科学研究所;武汉大学国家卫星定位系统工程技术研究中心;
【基金】：国家“863”计划项目(2013AA12A206) 道路交通安全公安部重点实验室开放课题(2016ZDSYSKFKT04) 中国工程院重大咨询项目
【分类号】：D631.2;TP311.13

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