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题名: 基于多尺度空间划分与路网建模的城市移动轨迹模式挖掘
其他题名: Mining Urban Moving Trajectory Patterns Based on Multi-scale Space Partition and Road Network Modeling
作者: 王亮; 胡琨元; 库涛; 吴俊伟
作者部门: 信息服务与智能控制技术研究室
关键词: 数据挖掘 ; 移动轨迹 ; 多尺度划分 ; 路网模型
刊名: 自动化学报
ISSN号: 0254-4156
出版日期: 2015
卷号: 41, 期号:1, 页码:47-58
收录类别: EI ; CSCD
产权排序: 1
摘要: 针对城市移动轨迹模式挖掘问题展开研究,提出移动全局模式与移动过程模式相结合的挖掘方法,即通过移动轨迹的起始位置点–终点位置点(Origin-destination,OD点)与移动过程序列分别进行移动全局模式与过程模式的发现.在移动全局模式发现中,提出了弹性多尺度空间划分方法,避免了硬性等尺度网格划分对密集区域边缘的破坏,同时增强了密集区域与稀疏区域的区分能力.在移动过程模式发现中,提出了基于移动轨迹的路网拓扑关系模型构建方法,通过路网关键位置点的探测抽取拓扑关系模型.最后基于空间划分集合与路网拓扑模型对原始移动轨迹数据进行序列数据转换与频繁模式挖掘.通过深圳市出租车历史GPS轨迹数据的实验结果...
英文摘要: In this paper, the problem of discovering moving trajectory patterns in urban environment is studied and the method of integration of moving global pattern and moving local pattern is proposed. Through moving trajectory origin-destination (OD) and moving sequence features, the global patterns and local patterns are mined. In the process of moving global pattern mining, a flexible multi-scale space partition is devised to avoid damage of the dense region edges by hard regular grid division and enhance the ability to distinguish the dense regions and sparse regions. In the process of moving local pattern mining, the modeling method of road network based on moving trajectory is devised to extract the feature of topological relation by key road network nodes. Finally, the raw moving trajectory dataset is converted by partitioned discrete regions and road network model, and the frequent moving trajectory patterns are discovered by a modified sequence pattern mining algorithm. A comprehensive experimental evaluation on Shenzhen taxicabs GPS trajectory dataset is presented, and the evaluation shows that the proposed method outperforms the existing methods in space division, data transform, and interpretability of mined patterns.
语种: 中文
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内容类型: 期刊论文
URI标识: http://ir.sia.cn/handle/173321/15727
Appears in Collections:信息服务与智能控制技术研究室_期刊论文

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