[1] 高强, 张凤荔, 王瑞锦, 等. 轨迹大数据:数据处理关键技术研究综述[J]. 软件学报, 2017, 28(4):959-992. Gao Qiang, Zhang Fengli, Wang Ruijin, et al. Trajectory big data:a review of key technologies in data processing[J]. Journal of Software, 2017, 28(4):959-992. [2] 许佳捷, 郑凯, 池明旻, 等. 轨迹大数据:数据、应用与技术现状[J]. 通信学报, 2015, 36(12):97-105. Xu Jiajie, Zheng Kai, Chi Mingmin, et al. Trajectory big data:data, applications and techniques[J]. Journal on Communications, 2015, 36(12):97-105. [3] Damiani M L, Hachem F. Segmentation techniques for the summarization of individual mobility data[J]. Wiley Interdisciplinary Reviews:Data Mining and Knowledge Discovery, 2017, 7(6):e1214. [4] Damiani M L, Hachem F, Issa H, et al. Cluster-based trajectory segmentation with local noise[J]. Data Mining and Knowledge Discovery, 2018, 32:1017-1055. [5] 张治华. 基于GPS轨迹的出行信息提取研究[D]. 上海:华东师范大学, 2010. [6] Du J, Aultman-Hall L. Increasing the accuracy of trip rate information from passive multi-day GPS travel datasets:automatic trip end identification issues[J]. Transportation Re-search, Part A(Policy and Practice), 2007, 41(3):220-232. [7] 张健钦, 仇培元, 徐志洁, 等. 一种基于手机定位数据的出行行程识别方法[J]. 武汉理工大学学报, 2013(5):934-938. Zhang Jianqin, Qiu Peiyuan, Xu Zhijie, et al. A method to identify trip based on the mobile phone positioning data[J]. Journal of Wuhan University of Technology, 2013(5):934-938. [8] Palma A T, Bogorny V, Kuijpers B, et al. A clustering-based approach for discovering interesting places in trajectories[C]//ACM Symposium on Applied Computing. Fortaleza:ACM, 2008:863-868. [9] Alvares L O, Bogorny V, Kuijpers B, et al. A model for enriching trajectories with semantic geographical information[J]. Proceedings of the 15th Annual ACM International Symposium on Advances in Geographic Information Systems, 2007, 22:1-8. [10] Hwang S, Vandemark C, Dhatt N, et al. Segmenting human trajectory data by movement states while addressing signal loss and signal noise[J]. International Journal of Geographical Information Science, 2018(7):1391-1412. [11] Hachem F, Damiani M L. Periodic stops discovery through density-based trajectory segmentation[C]//SIGSPATIAL' 18. New York:ACM Press, 2018:584-587. [12] 侯颖超, 王盼成, 刘兴权, 等. 基于速度的空间轨迹停留点提取算法[J]. 地理与地理信息科学, 2016(6):63-68. Hou Yingchao, Wang Pancheng, Liu Xingquan, et al. Algorithm study for stay points recognition of spatial trajectory based on velocity[J]. Geography and Geo-Information Science, 2016(6):63-68. [13] Soares Junior Amilcar, Moreno Neiva Moreno, Times Valéria Cesário, et al. GRASP-UTS:an algorithm for unsupervised trajectory segmentation[J]. International Journal of Geographical Information Science, 2015, 29(1):46-68. [14] Soares Junior Amilcar, Times Valéria Cesário, Chiara Renso, et al. A semi-supervised approach for the semantic segmentation of trajectories[C]//Proceedings of the 19th IEEE International Conference on Mobile Data Management. New York:IEEE, 2018(1):145-154. [15] Wu Ruizhi, Luo Guangchun, Shao Junming, et al. Location prediction on trajectory data:a review[J]. Big Data Mining and Analytics, 2018, 1(2):108-127. [16] 王京. 基于相关系数的轨迹停留点识别算法[D]. 武汉:华中师范大学, 2016. [17] Zheng Yu, Zhang Lizhu, Xie Xing, et al. Mining interesting locations and travel sequences from GPS trajectories[C]//Proceedings of the 18th International Conference on World Wide Web. New York:ACM Press, 2009:791-800. [18] Bao Jie, He Tianfu, Ruan Sijia, et al. Planning bike lanes based on sharing-bikes' trajectories[C]//Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York:ACM Press, 2017:1377-1386. |