[1]陆剑锋,郭茂祖,张昱,等.基于时空约束密度聚类的停留点识别方法[J].智能系统学报,2020,15(1):59-66.[doi:10.11992/tis.201910026]
 LU Jianfeng,GUO Maozu,ZHANG Yu,et al.Stay point recognition method based on spatio-temporal constraint density clustering[J].CAAI Transactions on Intelligent Systems,2020,15(1):59-66.[doi:10.11992/tis.201910026]
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基于时空约束密度聚类的停留点识别方法(/HTML)
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《智能系统学报》[ISSN:1673-4785/CN:23-1538/TP]

卷:
第15卷
期数:
2020年1期
页码:
59-66
栏目:
学术论文—智能系统
出版日期:
2020-01-01

文章信息/Info

Title:
Stay point recognition method based on spatio-temporal constraint density clustering
作者:
陆剑锋12 郭茂祖12 张昱13 赵玲玲4
1. 北京建筑大学 电气与信息工程学院, 北京 100044;
2. 北京建筑大学 建筑大数据智能处理方法研究北京市重点实验室, 北京 100044;
3. 北京建筑大学 深部岩土力学与地下工程国家重点实验室, 北京 100083;
4. 哈尔滨工业大学 计算机科学与技术学院, 黑龙江 哈尔滨 150001
Author(s):
LU Jianfeng12 GUO Maozu12 ZHANG Yu13 ZHAO Lingling4
1. School of Electrical and Information Engineering, Beijing University of Civil Engineering and Architecture, Beijing 100044, China;
2. Beijing Key Laboratory of Intelligent Processing for Building Big Data, Beijing University of Civil Engineering and
关键词:
停留点识别密度聚类时空约束间接时空特征时空相似性聚合过程统一细粒度
Keywords:
stay point identificationdensity clusteringspace-time constraintindirect spatio-temporal featurespatio-temporal similailyaggregatiedprocess uniformityfine-grained
分类号:
TP301
DOI:
10.11992/tis.201910026
摘要:
轨迹停留点的识别是轨迹分析、出行活动语义挖掘的关键。针对基于密度聚类的停留点识别方法对时空信息的表达缺陷,提出新的时空约束停留点识别方法,在密度聚类中引入轨迹的间接时空特征表示,将具有时空相似性的轨迹点进行聚合;采用与聚类过程相统一的时空特征约束对轨迹簇进行细粒度识别。算法在进行约束的时候再次利用到聚类时候所用的输入数据特征,特征的充分利用提高了识别的准确率。实验结果验证了本文方法的有效性。
Abstract:
The recognition of the track stay point is the key to the trajectory analysis and the semantic mining of travel activities. Aiming at the defect of spatio-temporal information based on density clustering, the new method of space-time constrained stay point recognition is proposed. In the density clustering, the indirect spatio-temporal feature representation of the trajectory is introduced, and the trajectory points with spatio-temporal similarity are aggregated. The spatio-temporal feature constraint unified with the clustering process is used to fine grain the trajectory cluster. Therefore, when the constraints are used, the input data features used in the clustering are reused, and the full utilization of the features improves accuracy of the recognition. The experimental results verify effectiveness of the proposed method.

参考文献/References:

[1] 吕志娟. 基于Lifelog数据的个人轨迹模式挖掘算法的研究与应用[D]. 沈阳: 东北大学, 2015: 20–30.
LYU Zhijuan. Research and application of personal trajectory pattern mining algorithm based on Lifelog data[D]. Shenyang: Northeastern University, 2015: 20–30.
[2] JIANG Renhe, ZHAO Jing, DONG Tingting, et al. A density-based approach for mining movement patterns from semantic trajectories[C]//TENCON 2015-2015 IEEE Region 10 Conference. Macao, China, 2015.
[3] 张文元, 谈国新, 朱相舟. 停留点空间聚类在景区热点分析中的应用[J]. 计算机工程与应用, 2018, 54(4): 263–270
ZHANG Wenyuan, TAN Guoxin, ZHU Xiangzhou. Application of stay points spatial clustering in hot scenic spots analysis[J]. Computer engineering and applications, 2018, 54(4): 263–270
[4] 杨震, 王红军. 基于Adaboost-Markov模型的移动用户位置预测方法[J]. 计算机应用, 2019, 39(3): 675–680
YANG Zhen, WANG Hongjun. Location prediction method of mobile user based on Adaboost-Markov model[J]. Journal of computer applications, 2019, 39(3): 675–680
[5] 石陆魁, 张延茹, 张欣. 基于时空模式的轨迹数据聚类算法[J]. 计算机应用, 2017, 37(3): 854–859, 895
SHI Lukui, ZHANG Yanru, ZHANG Xin. Trajectory data clustering algorithm based on spatiotemporal pattern[J]. Journal of computer applications, 2017, 37(3): 854–859, 895
[6] FU Zhongliang, TIAN Zongshun, XU Yanqing, et al. A two-step clustering approach to extract locations from individual GPS trajectory data[J]. ISPRS international journal of geo-information, 2016, 5(10): 166.
[7] XIANG Longgang, GAO Meng, WU Tao. Extracting stops from noisy trajectories: a sequence oriented clustering approach[J]. ISPRS international journal of geo-information, 2016, 5(3): 29.
[8] GONG Lei, SATO H, YAMAMOTO T, et al. Identification of activity stop locations in GPS trajectories by density-based clustering method combined with support vector machines[J]. Journal of modern transportation, 2015, 23(3): 202–213.
[9] GONG Lei, YAMAMOTO T, MORIKAWA T. Identification of activity stop locations in GPS trajectories by DBSCAN-TE method combined with support vector machines[J]. Transportation research procedia, 2018, 32: 146–154.
[10] GINGERICH K, MAOH H, ANDERSON W. Classifying the purpose of stopped truck events: an application of entropy to GPS data[J]. Transportation research part C: emerging technologies, 2016, 64: 17–27.
[11] 张鹏. 基于蜂窝网络数据的用户移动性分析和兴趣区挖掘[D]. 北京: 北京邮电大学, 2018: 31–52.
ZHANG Peng. User mobility research and interest region mining based on cellular networks traffic[D]. Beijing: Beijing University of Posts and Telecommunications, 2018: 31–52.
[12] 向隆刚, 邵晓天. 载体轨迹停留信息提取的核密度法及其可视化[J]. 测绘学报, 2016, 45(9): 1122–1131
XIANG Longgang, SHAO Xiaotian. Visualization and extraction of trajectory stops based on kernel-density[J]. Acta geodaetica et cartographica sinica, 2016, 45(9): 1122–1131
[13] LIAO Lin, FOX D, KAUTZ H. Extracting places and activities from GPS traces using hierarchical conditional random fields[J]. International journal of robotics research, 2007, 26(1): 119–134.
[14] 李毓瑞, 陈红梅, 王丽珍, 等. 基于密度的停留点识别方法[J]. 大数据, 2018, 4(5): 80–93
LI Yurui, CHEN Hongmei, WANG Lizhen, et al. Stay point identification based on density[J]. Big data research, 2018, 4(5): 80–93
[15] 杜润强. 基于手机轨迹数据的用户出行及停驻点识别系统研究[D]. 北京: 北京工业大学, 2014: 35–45.
DU Runqiang. The research on users’ movements and stay point identification based on mobile data[D]. Beijing: Beijing University of Technology, 2014: 35–45.
[16] HERDER E, SIEHNDEL P, KAWASE R. Predicting user locations and trajectories[M]//DIMITROVA V, KUFLIK T, CHIN D, et al. User Modeling, Adaptation, and Personalization. Cham: Springer, 2014: 86–97.
[17] DAMIANI M L, GüTING R H. Semantic trajectories and beyond[C]//Proceedings of 2014 IEEE 15th International Conference on Mobile Data Management. Brisbane, Australia, 2014.
[18] KHOSHAHVAL S, FARNAGHI M, TALEAI M. Spatio-temporal pattern mining on trajectory data using ARM[J]. International archives of the photogrammetry, remote sensing and spatial information sciences, 2017, XLⅡ-4/W4: 395–399.
[19] Sussex-Huawei locomotion dataset[EB/OL]. http://www.shl-dataset.org/download/.
[20] ZHENG Yu. Trajectory data mining: an overview[J]. ACM transactions on intelligent systems and technology, 2015, 6(3): 29–36.

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 ZHOU Zhiping,WANG Jiefeng,ZHU Shuwei,et al.An improved adaptive and fast AF-DBSCAN clustering algorithm[J].CAAI Transactions on Intelligent Systems,2016,11(1):93.[doi:10.11992/tis.201410021]

备注/Memo

备注/Memo:
收稿日期:2019-10-31。
基金项目:国家自然科学基金项目(61871020,61502117,61305013);北京市教委科技计划重点项目(KZ201810016019);北京市属高校高水平创新团队建设计划项目(IDHT20190506);国家重点研发计划项目(2016YFC0600901)
作者简介:陆剑锋,硕士研究生,主要研究方向为机器学习、智慧城市;郭茂祖,教授,博士生导师,主要研究方向为机器学习、智慧城市、生物信息学。主持和参与国家自然科学基金面上项目、北京市属高校高水平创新团队建设计划项目和北京市教委科技计划重点项目等。曾获得教育部高等学校科学研究优秀成果自然科学二等奖、省科技进步二等奖等。发表学术论文200余篇;赵玲玲,讲师,博士,主要研究方向为城市计算、生物信息学。主持和参与多项国家自然科学基金项目。发展学术论文50余篇。
通讯作者:赵玲玲.E-mail:zhaoll@hit.edu.cn
更新日期/Last Update: 1900-01-01