帮助 关于我们

返回检索结果

时空轨迹分类研究进展
Research Progress of Spatial-temporal Trajectory Classification

查看参考文献36篇

文摘 时空轨迹分类旨在为一条轨迹预测类别。时空轨迹分类在城市规划、个性化用户推荐等方面具有重要应用价值,其过程主要包括轨迹数据预处理、特征提取、建立分类器3个阶段。本文综述了近年来时空轨迹分类的研究进展,首先对时空轨迹分类的过程进行概述;然后将时空轨迹分类算法按特征提取的方式分为基于运动特征的轨迹分类算法、基于分类规则的轨迹分类算法和基于图像信号分析的轨迹分类算法3类,分别论述了这些算法的基本思想和优缺点;之后对现有的轨迹分类算法从数据来源、分类器、特征提取方式等方面进行对比分析;最后讨论现有的时空轨迹分类算法面临的挑战。
其他语种文摘 Spatial-temporal trajectory classification aims at predicting the category of a spatial-temporal trajectory. The classification of spatial-temporal trajectories plays an important role in urban planning, personalized user recommendation and so on. The process of trajectory classification includes three stages: trajectory preprocessing, feature extraction and classification. This paper reviews the recent research progress on trajectory classification. Firstly, we introduce the process of trajectory classification. Then, the trajectory classification algorithms are classified into three categories according to the method of feature extraction, including the trajectory classification algorithm based on motion feature, the trajectory classification algorithm based on classification rule and the trajectory classification algorithm based on image signal analysis. We also discuss the basic ideas, advantages and disadvantages of these algorithms. Thirdly, we compare the existing classification algorithms according to the sensors, feature extraction and classifiers used in these algorithms. Finally, we introduce the challenges of the existing trajectory classification algorithms.
来源 地球信息科学学报 ,2017,19(3):289-297 【核心库】
DOI 10.3724/SP.J.1047.2017.00289
关键词 时空轨迹分类 ; 时空轨迹挖掘 ; 时空数据挖掘
地址

南京师范大学计算机科学与技术学院, 南京, 210023

语种 中文
文献类型 综述型
ISSN 1560-8999
学科 测绘学
基金 国家自然科学基金项目
文献收藏号 CSCD:5942939

参考文献 共 36 共2页

1.  Biljecki F. Transportation mode-base segmentation and classification of movement trajectories. International Journal of Geographical Information Science,2013,27(2):385-407 CSCD被引 11 次    
2.  Mountain D. Modelling human spatio-temporal behaviour: A challenge for location-based services. Proceedings of 6th International Conference on Geocomputation,2001:24-26 CSCD被引 1 次    
3.  Zheng Y. Understanding transportation modes based on GPS data for web applications. Acm Transactions on the Web,2010,4(1):495-507 CSCD被引 23 次    
4.  Lee J G. TraClass: trajectory classification using hierarchical region-based and trajectory-based clustering. Proceedings of the Vldb Endowment,2008,1(1):1081-1094 CSCD被引 11 次    
5.  Patel D. Incorporating duration and region association information in trajectory classification. Journal of Location Based Services,2012,7(4):246-271 CSCD被引 2 次    
6.  Zhu Y. Inferring taxi status using GPS trajectories. Tech.Rep.MSR-TR-2011-144,2011 CSCD被引 1 次    
7.  Lee J G. Trajectory outlier detection: Apartition and detect framework. Proceedings of the 24th International Conference on Data Engineering,2008:140-149 CSCD被引 7 次    
8.  Krumm J. LOCADIO: Inferring motion and location from Wi-Fi signal strengths. International Conference on Mobile and Ubiquitous Systems: NETWORKING and Services,2004:4-13 CSCD被引 1 次    
9.  Yin J. High-level goal recognition in a wireless LAN. Nineteenth National Conference on Artificial Intelligence, Sixteenth Conference on Innovative Applications of Artificial Intelligence,2004:578-584 CSCD被引 1 次    
10.  Zheng Y. Trajectory data mining: An overview. Acm Transactions on Intelligent Systems & Technology,2015,6(3):1-41 CSCD被引 115 次    
11.  Gonzalez P A. Automating mode detection using neural networks and assisted GPS data collected using GPS-enabled mobile phones. 15th World Congress on Intelligent Transport Systems and ITS America's 2008 Annual Meeting,2008:30267-30279 CSCD被引 1 次    
12.  肖艳丽. 移动数据的交通出行方式识别方法. 智能系统学报,2014,9(5):536-543 CSCD被引 3 次    
13.  Assemi B. Developing and validating a statistical model for travel mode identification on smartphones. IEEE Transactions on Intelligent Transportation Systems,2016,17(7):1920-1931 CSCD被引 3 次    
14.  Wang H. Transportation mode inference from anonymized and aggregated mobile phone call detail records. 13th International IEEE Conference on Intelligent Transportation Systems (ITSC),2010:318-323 CSCD被引 1 次    
15.  Zhu W. Applying semi-supervised learning method for cellphone-based travel mode classification. Smart Cities Conference (ISC2),2015:1-6 CSCD被引 1 次    
16.  Su X. Online travel mode identification using smartphones with battery saving considerations. IEEE Transactions on Intelligent Transportation Systems,2016,99(1):1-14 CSCD被引 1 次    
17.  Lin M. Detecting modes of transport from unlabeled positioning sensor data. Journal of Location Based Services,2013,7(4):272-290 CSCD被引 2 次    
18.  Zheng Y. Understanding mobility based on GPS data. Ubiquitous Computing, International Conference,2008:312-321 CSCD被引 1 次    
19.  Jahangiri A. Applying machine learning techniques to transportation mode recognition using mobile phone sensor data. IEEE transactions on intelligent transportation systems,2015,16(5):2406-2417 CSCD被引 8 次    
20.  Shafique M A. A Comparison among various classification algorithms for travel mode detection using sensors' data collected by smartphones. International Conference on Computers in Urban Planning and Urban Management,2015 CSCD被引 1 次    
引证文献 8 篇

1 郑海林 上海外高桥港区停泊船聚类分析与异常检测 地球信息科学学报,2018,20(5):640-646
CSCD被引 3 次

2 冯慧芳 基于轨迹大数据的城市交通感知和路网关键节点识别 交通运输系统工程与信息,2018,18(3):42-47,54
CSCD被引 6 次

显示所有8篇文献

论文科学数据集
PlumX Metrics
相关文献

 作者相关
 关键词相关
 参考文献相关

版权所有 ©2008 中国科学院文献情报中心 制作维护：中国科学院文献情报中心
地址：北京中关村北四环西路33号 邮政编码：100190 联系电话：(010)82627496 E-mail:cscd@mail.las.ac.cn 京ICP备05002861号-4 | 京公网安备11010802043238号