地球信息科学学报 ›› 2015, Vol. 17 ›› Issue (10): 1152-1161.doi: 10.3724/SP.J.1047.2015.01152

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浮动车轨迹数据聚类的有向密度方法

廖律超1,2(), 蒋新华1,2*(), 邹复民2, 李璐明1, 赖宏图2   

  1. 1. 中南大学信息科学与工程学院,长沙 410075
    2. 福建工程学院 福建省汽车电子与电驱动技术重点实验室,福州 350108
  • 收稿日期:2015-04-20 修回日期:2015-05-18 出版日期:2015-10-10 发布日期:2015-10-10
  • 作者简介:

    作者简介:廖律超(1980-),男,福建长汀人,博士生,高级工程师,研究方向为时空大数据处理、交通轨迹数据挖掘及交通行为模式识别。E-mail: achao@fjut.edu.cn

  • 基金资助:
    国家自然科学基金项目(61304199、41471333);福建省高校杰出青年科研人才计划(JA14209);福建省自然科学基金项目(2013J01214);福建省科技重大专项专题项目(2013HZ0002-1);福建省科技计划重点项目(2011I0002);福建省交通科技计划项目(201318)

A Fast Method of FCD Trajectory Data Clustering Based on the Directed Density

LIAO Lvchao1,2(), JIANG Xinhua1,2,*(), ZOU Fumin2, LI Luming1, LAI Hongtu2   

  1. 1. School of Information Science and Engineering, Central-South University, Changsha 410075, China
    2. Fujian Key Laboratory for Automotive Electronics and Electric Drive , Fujian University of Technology, Fuzhou 350108, China
  • Received:2015-04-20 Revised:2015-05-18 Online:2015-10-10 Published:2015-10-10
  • Contact: JIANG Xinhua E-mail:achao@fjut.edu.cn;xhjiang@fjut.edu.cn
  • About author:

    *The author: CHEN Nan, E-mail:fjcn99@163.com

摘要:

为了充分挖掘浮动车轨迹数据的潜在特性,本文在OPTICS空间密度聚类算法基础上,提出了一种有向密度的快速聚类方法(D-OPTICS)。该方法通过扇形空间邻域计算其有向密度信息,并基于方向信息约束其密度可连通性,通过有向可达距离曲线生成数据基本簇,最后,通过空间网格及类簇聚合等优化方法,实现其大规模浮动车轨迹数据的快速聚类处理。通过有向时空数据的聚类分析,发现浮动车轨迹的时空分布特性,以提取复杂路网的结构信息。本文以福州市大规模浮动车轨迹数据,对D-OPTICS进行了系统实验,分析表明,该算法可实现浮动车轨迹数据的快速有向密度聚类分析,有助于挖掘发现时空轨迹数据的分布规律,且基于聚类结果提取了福州市区复杂路网的有向拓扑结构图。同时,与DBSCAN及OPTICS等传统的密度聚类算法进行性能对比,实验表明,D-OPTICS算法能更好地支持大规模浮动车轨迹数据的处理要求。

关键词: 浮动车轨迹数据, 时空数据挖掘, 密度聚类, 有向密度聚类, 浮动车数据

Abstract:

Floating car data (FCD), which is the trajectories of vehicles, are automatically collected by huge quantities of commercial vehicles which are equipped with GPS devices. Exploring and exploiting such data is essential to understand the dynamic aggregation patterns of trajectory data. However, the existing methods of spatial density clustering mainly focus on undirected data, and it is difficult to effectively find the characteristics of trajectory data. We contribute to the literatures on FCD trajectory data mining by presenting a novel method called directed density clustering method (D-OPTICS), which is formulated based on the spatial density clustering algorithm (OPTICS). In our method, the directed density is computed by a fan-shaped neighborhood region, and the density connectivity is restrained by its direction information. Then, the base clusters are generated using the curve analysis of reachable distance. Finally, the D-OPTICS cluster method is formed by the optimization method of spatial grid and cluster polymerization. This method can be naturally applied to FCD trajectory data mining, and it is also appropriate for handling other directed spatial data. It can be employed to discover the spatio-temporal distribution characteristic of traffic trajectory, and then be adopted to extract the structure information of complex road network. The experiments, with massive floating car data of Fuzhou city, show that the D-OPTICS can cluster directed spatial data effectively, and is useful to uncover the inherent distribution characteristic of the massive trajectory data. Based on its clustering result, the topology information of road network can be extracted. In this work, we extracted the topology graph for the complex road network of Fuzhou city. The experiment results also show that the algorithm can automatically determine the number of clusters, and it is found that the algorithm is not limited to globular cluster data and is capable to deal with clusters of arbitrary shapes. The key contribution of this method is that it takes the direction information into account and it can also be effective in reducing the problems caused by traditional clustering algorithms which may incorrectly merge or decompose thus naturally produce large clusters and noise data. Meanwhile, the result of performance experiments shows that, compared with DBSCAN and OPTICS, the proposed method is more suitable for large-scale data processing.

Key words: FCD trajectory data, spatial-temporal data mining, density clustering, directed density clustering, floating car data