标题: 应用分群法于众源行车震动事件采探路面异常点
Road Surface Anomalies Discovery by Clustering Crowdsourced Bumping Events
作者: 黄小红
Ng, Siau Hong
易志伟
Yi,Chih-Wei
网路工程研究所
关键字: 智慧探侦车;群众资源;分群法;路面异常;Smartphone probe car;crowdsourcing;clustering;road surface anomaly
公开日期: 2015
摘要: 此论文中我们提出了一个可透过智慧手机探侦车 (SPC) 所记录的车辆震动事件群集找寻路面异常点的架构。我们设计了一个具有可适性的震动侦测和评估的演算法,可提供动态手机与车辆配对的能力,因此可大量布建并使用在众源资料收集,我们将相关技术实作成“Bumping Sensor”的行动感测APP,提供相关可靠的车辆震动事件侦测,用以评估车辆行经的路面异常点。由于来自群众所收集的震动事件资料可能包含定位的误差和错误的侦测等所造成的杂讯,因此我们运用Naive Grid-based Clustering (NGC), DENsitybased CLUstEring (DENCLUE), Adaptive Mesh Refinement (AMR) 等三个群聚演算法进行资料汇整并从群众车辆所回传的震动事件挖掘出道路表面的异常点。我们进行了两个震动侦测实验,分别在台湾交通大学校园和美国柏克莱加州大学里收集到2059和641个震动事件。由NGC, DENCLUE和AMR三种分群演算法所挖掘出之震动事件正确率分别为64%, 82%, 70%。实验结果显示DENCLUE分群演算法相较于NGC和AMR具有更好的路面异常点分辨能力。
In this thesis, we develop a framework to discover road surface anomalies from vehicle bumping events reported by the Smartphone Probe Cars (SPC) crowd. An adaptive bumping detection and assessment algorithm is implemented to develop a mobile sensing App, called “Bumping Sensor”, that provides relatively reliable detection and assessment of the run-over road surface anomalies. Since the crowdsourced data may contain false alerts and be contaminated by imprecise position information. Three clustering algorithms, including Naive Grid-based Clustering (NGC), DENsitybased CLUstEring (DENCLUE), and Adaptive Mesh Refinement (AMR), are implemented to mine road surface anomalies from crowdsourced bumping events. Two experiments were executed on NCTU campus, Taiwan and Richmond Field Station, Berkeley, USA. In the two experiments, 2059 and 641 bumping events were collected respectively. The accuracy results in identification of road anomalies by NGC, DENCLUE and AMR clustering algorithms are 64%, 82%, and 70%. The results show that DENCLUE clustering algorithm outperforms NGC and AMR in identification of road anomalies.
URI: http://140.113.39.130/cdrfb3/record/nctu/#GT070156549
http://hdl.handle.net/11536/125670
显示于类别:Thesis