[1] MARR B. Big data:using smart big data, analytics and metrics to make better decisions and improve performance[M]. London:John Wiley & Sons, 2015. [2] 裴韬, 刘亚溪, 郭思慧, 等. 地理大数据挖掘的本质[J]. 地理学报, 2019, 74(3):586-598. PEI Tao, LIU Yaxi, GUO Sihui, et al. Principle of big geodata mining[J]. Acta Geographica Sinica, 2019, 74(3):586-598. [3] 牟乃夏, 张恒才, 陈洁, 等. 轨迹数据挖掘城市应用研究综述[J]. 地球信息科学学报, 2015, 17(10):1136-1142. MOU Naixia, ZHANG Hengcai, CHEN Jie, et al. A review on the application research of trajectory data mining in urban cities[J]. Journal of Geo-Information Science, 2015, 17(10):1136-1142. [4] GUO Diansheng. Flow mapping and multivariate visualization of large spatial interaction data[J]. IEEE Transactions on Visualization and Computer Graphics, 2009, 15(6):1041-1048. [5] 陈占龙, 周路林, 禹文豪, 等. 顾及兴趣点潜在上下文关系的城市功能区识别[J]. 测绘学报, 2020, 49(7):907-920. DOI:10.11947/j.AGCS.2020.20190315. CHEN Zhanlong, ZHOU Lulin, YU Wenhao, et al. Identification of the urban functional regions considering the potential context of interest points[J]. Acta Geodaetica et Cartographica Sinica, 2020, 49(7):907-920. DOI:10.11947/j.AGCS.2020.20190315. [6] 姜晶莉, 郭黎, 李豪. 基于出租车轨迹数据的道路空驶率分析[J]. 兰州交通大学学报, 2019, 38(3):95-100. JIANG Jingli, GUO Li, LI Hao. Analysis of empty-run rate based on taxi trajectory data[J]. Journal of Lanzhou Jiaotong University, 2019, 38(3):95-100. [7] 姚尧, 张亚涛, 关庆锋, 等. 使用时序出租车轨迹识别多层次城市功能结构[J]. 武汉大学学报(信息科学版), 2019, 44(6):875-884. YAO Yao, ZHANG Yatao, GUAN Qingfeng, et al. Sensing multi-level urban functional structures by using time series taxi trajectory data[J]. Geomatics and Information Science of Wuhan University, 2019, 44(6):875-884. [8] 贾涛, 李琦, 马楚, 等. 武汉市出租车轨迹二氧化碳排放的时空模式分析[J]. 武汉大学学报(信息科学版), 2019, 44(8):1115-1123. JIA Tao, LI Qi, MA Chu, et al. Computing the CO2 emissions of taxi trajectories and exploring their spatiotemporal patterns in Wuhan city[J]. Geomatics and Information Science of Wuhan University, 2019, 44(8):1115-1123. [9] 赵夏君. 基于GPS轨迹数据的城市路段交通拥堵时序分析[J]. 湖南交通科技, 2018, 44(3):210-215. ZHAO Xiajun. Urban traffic congestion timing analysis based on GPS trajectory data[J]. Hunan Communication Science and Technology, 2018, 44(3):210-215. [10] 邬群勇, 张良盼, 吴祖飞. 利用出租车轨迹数据识别城市功能区[J]. 测绘科学技术学报, 2018, 35(4):413-417, 424. WU Qunyong, ZHANG Liangpan, WU Zufei. Identifying city functional area using taxi trajectory data[J]. Journal of Geomatics Science and Technology, 2018, 35(4):413-417, 424. [11] LAHA A K, PUTATUDA S. Real time location prediction with taxi-GPS data streams[J]. Transportation Research PartC:Emerging Technologies, 2018, 92:298-322. [12] ZHOU Zuojian, DOU Wanchun, JIA Guochao, et al. A method for real-time trajectory monitoring to improve taxi service using GPS big data[J]. Information & Management, 2016, 53(8):964-977. [13] GONG Shuhui, CARTLIDGE J, BAI Ruibin, et al. Activity modelling using journey pairing of taxi trajectory data[C]//Proceedings of the 4th IEEE International Conference on Big Data Analytics (ICBDA). Suzhou, China:IEEE, 2019:236-240. [14] 李明晓, 张恒才, 仇培元, 等. 一种基于模糊长短期神经网络的移动对象轨迹预测算法[J]. 测绘学报, 2018, 47(12):1660-1669. LI Mingxiao, ZHANG Hengcai, QIU Peiyuan, et al. Predicting future locations with deep fuzzy-LSTM network[J]. Acta Geodaetica et Cartographica Sinica, 2018, 47(12):1660-1669. [15] 许涛. 基于海量出租车轨迹数据的旅行时间预测[D]. 上海:华东师范大学, 2017. XU Tao. Travel time prediction based on massive taxi trajectory data[D]. Shanghai:East China Normal University, 2017. [16] 许震洲. 轨迹大数据下城市异常移动模式研究及可视化[D]. 西安:西北大学, 2019. XU Zhenzhou. Anomaly urban mobility detection and visualization design based on large trajectory data[D]. Xi'an:Northwest University, 2019. [17] IWAN L H, SAFAR M. Pattern mining from movement of mobile users[J]. Journal of Ambient Intelligence and Humanized Computing, 2010, 1(4):295-308. [18] GONG Li, LIU Xi, WU Lun, et al. Inferring trip purposes and uncovering travel patterns from taxi trajectory data[J]. Cartography and Geographic Information Science,2016, 43(2):103-114. [19] LI Aoyong, AXHAUSEN K W. Trip purpose imputation for taxi data[C]//Proceedings of the 18th Swiss Transport Research Conference (STRC 2018). Ascona, Switzerland:STRC, 2018. [20] XIE Kexin, DENG Ke, ZHOU Xiaofang. From trajectories to activities:a spatio-temporal join approach[C]//Proceedings of 2009 International Workshop on Location Based Social Networks. Seattle, Washington:ACM, 2009:25-32. [21] HUANG Lian, LI Qingquan, YUE Yang. Activity identification from GPS trajectories using spatial temporal POIs' attractiveness[C]//Proceedings of the 2nd ACM SIGSPATIAL International Workshop on Location based Social Networks. San Jose, California:ACM, 2010:27-30. [22] PHITHAKKITNUKOON S, HORANONTT, DI LORENZO G, et al. Activity-aware map:identifying human daily activity pattern using mobile phone data[C]//Proceedings of 2010 International Workshop on Human Behavior Understanding. Berlin:Springer, 2010:14-25. [23] FURLETTI B, CINTIA P, RENSO C, et al. Inferring human activities from GPS tracks[C]//Proceedings of the 2nd ACM SIGKDD International Workshop on Urban Computing. Chicago, Illinois:ACM, 2013:1-8. [24] XIAO XIANGYE, ZHENG Yu, LUO Qiong, et al. Inferring social ties between users with human location history[J]. Journal of Ambient Intelligence and Humanized Computing,2014, 5(1):3-19. [25] 吕明琪. 基于轨迹数据挖掘的语义化位置感知计算研究[D]. 杭州:浙江大学, 2012. LÜ Mingqi. Research on the semantic location-aware computing based on trajectory data mining[D]. Hangzhou:Zhejiang University, 2012. [26] 廖律超, 蒋新华, 邹复民, 等. 一种支持轨迹大数据潜在语义相关性挖掘的谱聚类方法[J]. 电子学报, 2015, 43(5):956-964. LIAO Lüchao, JIANG Xinhua, ZOU Fumin, et al. A spectral clustering method for big trajectory data mining with latent semantic correlation[J]. Acta Electronica Sinica, 2015, 43(5):956-964. [27] 普树芳. 基于移动计算平台的轨迹数据挖掘语义化感知技术研究[J]. 科技资讯, 2019, 17(24):23-24. PU Shufang. Research on semantic sensing technology of trajectory data mining based on mobile computing platform[J]. Science & Technology Information, 2019, 17(24):23-24. [28] 章静蕾, 石海龙, 崔莉. 基于出行方式及语义轨迹的位置预测模型[J]. 计算机研究与发展, 2019, 56(7):1357-1369. ZHANG Jinglei, Shi Hailong, CUI Li. Location prediction model based on transportation mode and semantic trajectory[J]. Journal of Computer Research and Development, 2019, 56(7):1357-1369. [29] 刘春, 周燕, 李鑫. 挖掘语义轨迹频繁模式及拼车应用研究[J]. 计算机工程与应用, 2019, 55(15):96-103. LIU Chun, ZHOU Yan, LI Xin. Mining semantic trajectory frequent pattern and car pooling application research[J]. Computer Engineering and Applications, 2019, 55(15):96-103. [30] 吴瑕, 唐祖锴, 祝园园. 近似到达时间约束下的语义轨迹频繁模式挖掘[J]. 软件学报, 2018, 29(10):3184-3204. WU Xia, TANG Zukai, ZHU Yuanyuan. Frequent pattern mining with approximate arrival-time in semantic trajectories[J]. Journal of Software, 2018, 29(10):3184-3204. [31] WANG Pengfei, LIU Guannan, FU Yanjie, et al. Spotting trip purposes from taxi trajectories:a general probabilistic model[J]. ACM Transactions on Intelligent Systems and Technology, 2017, 9(3):1-26. [32] ALSGER A, TAVASSOLI A, MESBAHM, et al. Public transport trip purpose inference using smart card fare data[J]. Transportation Research Part C:Emerging Technologies, 2018, 87:123-137. [33] CHEN Chao, LIAO Chengwu, XIE Xuefeng, et al. Trip2Vec:a deep embedding approach for clustering and profiling taxi trip purposes[J]. Personal and Ubiquitous Computing, 2019, 23(1):53-66. [34] MA Xiaolei, WU Y J, WANG Yinhai, et al. Mining smart card data for transit riders'travel patterns[J]. Transportation Research Part C:Emerging Technologies, 2013, 36:1-12. [35] GIANNOTTI F, NANNI M, PEDRESCHI D, et al. Unveiling the complexity of human mobility by querying and mining massive trajectory data[J]. The VLDB Journal, 2011, 20(5):695. [36] GAFFNRY S, SMYTH P. Trajectory clustering with mixtures of regression models[C]//Proceedings of the 5th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. San Diego, California, USA:ACM, 1999:63-72. [37] KALNIS P, MAMOULIS N, BAKIRAS S. On discovering moving clusters in spatio-temporal data[C]//Proceedings of 2005 International Symposium on Spatial and Temporal Databases. Berlin:Springer, 2005:364-381. [38] LEE J G, HAN Jiawei, WHANG K Y. Trajectory clustering:a partition-and-group framework[C]//Proceedings of 2007 ACM SIGMOD International Conference on Management of Data. Beijing, China:ACM, 2007:593-604. [39] 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. Madrid, Spain:ACM, 2009:791-800. [40] ZHAO Pengxiang, KWAN M P, QIN Kun. Uncovering the spatiotemporal patterns of CO2 emissions by taxis based on individuals' daily travel[J]. Journal of Transport Geography, 2017, 62:122-135. [41] DASZYKOWSKI M, WALCZAK B, MASSART D L. Looking for natural patterns in data:Part 1. Density-based approach[J]. Chemometrics and Intelligent Laboratory Systems, 2001, 56(2):83-92. [42] MOREIRA-MATIAS L, GAMA J, FERREIRA M, et al. A predictive model for the passenger demand on a taxi network[C]//Proceedings of the 15th International IEEE Conference on Intelligent Transportation Systems. Anchorage, AK, USA:IEEE, 2012:1014-1019. |