大范围公路网交通态势估计驱动的非对称超大规模在线聚类技术研究

项目名称： 大范围公路网交通态势估计驱动的非对称超大规模在线聚类技术研究

项目编号： No.61202311

项目类型： 青年科学基金项目

立项/批准年度： 2013

项目学科： 计算机科学学科

项目作者： 钱鹏江

作者单位： 江南大学

项目金额： 23万元

中文摘要： 当前国内交通态势估计的研究与应用呈现出区域性之显著特点，聚类技术在其研究中占据着极其重要的作用。当面对大范围公路网交通态势估计研究时，现有的聚类方法会面临数据吞吐量弱、实时性和实用性差的困境。本课题拟针对此问题提出适合于海量交通态势流的超大规模在线聚类方法。课题组基于已有的快速图论松弛聚类算法，引入关联矩阵估计技术提出其在线图论松弛聚类版本，并在此基础上通过拟研究的基于一致集的快速聚类集成算法，提出本课题最终的基于关联矩阵估计和一致集快速聚类集成的超大规模在线图论松弛聚类方法。拟发展的此方法将具有非对称态势信息处理、超大规模数据吞吐量和在线自适应聚类等优点，并试图为解决大范围公路网交通态势的监测、估计和应急处理之科学问题提供新途径。本课题所要解决的问题来源于申请者及课题组的工程实践，但也具有普适意义，其成果对于计算智能、模式识别和交通管理等领域均具有重要的学术和应用意义。

中文关键词： 知识迁移学习；半监督学习；多视角学习；大规模数据处理；公路网交通流监测

英文摘要： Nowadays, most of researches and applications on traffic situation assessment focus on the regional road network study where clustering technologies play an extremely important role in it. However, when we face the problem of traffic situation assessment for large-scaled road network, these existing clustering methods have some heavy shortcomings, such as low data throughput, very poor on-line clustering capability. To cope with these problems, this project aims to propose a very-large-scaled on-line clustering method, which is especially suitable for huge amounts of traffic situation flows. Based on the Fast Graph-based Relaxed Clustering (FGRC) algorithm proposed by the applicant, and by using the proposed incidence matrix approximation method, the applicant and his team plan to first develop the on-line FGRC algorithm, and then propose the consistent-set-based fast clustering aggregation algorithm. Based on these two novel algorithms, the final incidence matrix approximation and consistence-set fast clustering aggregation based very-large-scaled on-line graph-based relaxed clustering method will be also available. This novel method will be good at asymmetric information processing, very-large-scaled data throughput and on-line self-adaptive clustering. Furthermore, this proposed clustering method will provide

英文关键词： Knowledge transfer learning；semi-supervised learning；multi-view learning；large-scale data processing；monitoring of traffic flow of road network