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
Mining Urban Moving Trajectory Patterns Based on Multi-scale Space Partition and Road Network Modeling
查看参考文献32篇
|
文摘
|
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表明,该方法与现有方法相比在区域划分、数据转换等方面具有更好的性能,同时挖掘结果语义更为丰富,可解释性更强. |
|
其他语种文摘
|
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. |
|
来源
|
自动化学报
,2015,41(1):47-58 【核心库】
|
|
DOI
|
10.16383/j.aas.2015.c130804
|
|
关键词
|
数据挖掘
;
移动轨迹
;
多尺度划分
;
路网模型
|
|
地址
|
中国科学院沈阳自动化研究所信息服务与智能控制研究室, 沈阳, 110016
|
|
语种
|
中文 |
|
文献类型
|
研究性论文 |
|
ISSN
|
0254-4156 |
|
学科
|
自动化技术、计算机技术 |
|
基金
|
国家自然科学基金
|
|
文献收藏号
|
CSCD:5337401
|
参考文献 共
32
共2页
|
|
1.
刘瑜. 基于位置感知设备的人类移动研究综述.
地理与地理信息科学,2011,27(4):8-13
|
被引
45
次
|
|
|
|
|
2.
王明生. 探索城市公交客流移动模式.
电子科技大学学报,2012,41(1):2-7
|
被引
8
次
|
|
|
|
|
3.
邹永贵. 基于路网的LBSN用户移动轨迹聚类挖掘方法.
计算机应用研究,2013,30(8):2410-2414
|
被引
4
次
|
|
|
|
|
4.
Castro P S. Urban traffic modelling and prediction using large scale taxi GPS traces.
Proceedings of the 2012 Pervasive Computing Lecture Notes in Computer Science,2012:57-72
|
被引
5
次
|
|
|
|
|
5.
Gong H M. A GPS/ GIS method for travel mode detection in New York city.
Computers, Environment, and Urban Systems,2012,36(2):131-139
|
被引
3
次
|
|
|
|
|
6.
Yue Y. Exploratory calibration of a spatial interaction model using taxi GPS trajectories.
Computers, Environment, and Urban Systems,2012,36(2):140-153
|
被引
10
次
|
|
|
|
|
7.
Zhan X Y. Urban link travel time estimation using large-scale taxi data with partial information.
Transportation Research Part C: Emerging Technologies,2013,33:37-49
|
被引
4
次
|
|
|
|
|
8.
Brouwers N. Dwelling in the canyons: dwelling detection in urban environments using GPS, Wi-Fi, and geolocation.
Pervasive and Mobile Computing,2013,9(5):665-680
|
被引
1
次
|
|
|
|
|
9.
Yue Y. Mining time-dependent attractive areas and movement patterns from taxi trajectory data.
Proceedings of the 17th International Conference on Geoinformatics,2009:1-6
|
被引
3
次
|
|
|
|
|
10.
Zhang W S. Mining the semantics of origin-destination flows using taxi traces.
Proceedings of the 2012 ACM Conference on Ubiquitous Computing,2012:943-949
|
被引
3
次
|
|
|
|
|
11.
Guo D S. Discovering spatial patterns in origin-destination mobility data.
Transactions in GIS,2012,16(3):411-429
|
被引
11
次
|
|
|
|
|
12.
Pan G. Land-use classification using taxi GPS traces.
IEEE Transactions on Intelligent Transportation Systems,2013,14(1):113-123
|
被引
50
次
|
|
|
|
|
13.
Veloso M. Urban mobility study using taxi traces.
Proceedings of the 2011 International Workshop on Trajectory Data Mining and Analysis,2011:23-30
|
被引
6
次
|
|
|
|
|
14.
Veloso M. Sensing urban mobility with taxi flow.
Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Location-Based Social Networks,2011:41-44
|
被引
3
次
|
|
|
|
|
15.
黄健斌. 融合语义特征的移动对象轨迹预测方法.
计算机研究与发展,2014,51(1):76-87
|
被引
4
次
|
|
|
|
|
16.
赵越. 基于模式挖掘与匹配的移动轨迹预测方法.
吉林大学学报(工学版),2008,38(5):1125-1130
|
被引
5
次
|
|
|
|
|
17.
Gidofalvi G. Mining long, sharable patterns in trajectories of moving objects.
Geolnformatica,2009,13(1):27-55
|
被引
7
次
|
|
|
|
|
18.
Sohn K. Dynamic origin-destination flow estimation using cellular communication system.
IEEE Transactions on Vehicular Technology,2008,57(5):2703-2713
|
被引
3
次
|
|
|
|
|
19.
Caceres N. Deriving origin destination data from a mobile phone network.
IET Intelligent Transport Systems,2007,1(1):15-26
|
被引
9
次
|
|
|
|
|
20.
Won J I. Trajectory clustering in road network environment.
Proceedings of the 2009 Computational Intelligence and Data Mining,2009:299-305
|
被引
1
次
|
|
|
|
|
了解气象卫星更多信息，请访问