[1] ZHENG Yu, XIE Xing. Learning travel recommendations from user-generated GPS traces[J]. ACM Transactions on Intelligent Systems and Technology, 2011, 2(1): 1-29. [2] 张国明, 王俊淑, 江南, 等. 关注点推荐算法的霍克斯过程法[J]. 测绘学报, 2018, 47(9): 1261-1269.DOI: 10.11947/j.AGCS.2018.20170552. ZHANG Guoming, WANG Junshu, JIANG Nan, et al. A point-of-interest recommendation method based on hawkes process[J]. Acta Geodaetica et Cartographica Sinica, 2018, 47(9): 1261-1269.DOI: 10.11947/j.AGCS.2018.20170552. [3] 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.DOI: 10.11947/j.AGCS.2018.20170268. 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.DOI: 10.11947/j.AGCS.2018.20170268. [4] 张博, 庞基敏, 章文嵩, 等. 互联网大数据技术在智慧交通发展中的应用[J]. 科技导报, 2020, 38(9): 47-54. ZHANG Bo, PANG Jimin, ZHANG Wensong, et al. Application of big data technology in the development of intelligent transportation[J]. Science & Technology Review, 2020, 38(9): 47-54. [5] 吴华意, 黄蕊, 游兰, 等. 出租车轨迹数据挖掘进展[J]. 测绘学报, 2019, 48(11): 1341-1356. DOI: 10.11947/j.AGCS.2019.20190210. WU Huayi, HUANG Rui, YOU Lan, et al. Recent progress in taxi trajectory data mining[J]. Acta Geodaetica et Cartographica Sinica, 2019, 48(11): 1341-1356.DOI: 10.11947/j.AGCS.2019.20190210. [6] 王喜娜, 黄华兵, 班亚, 等. 利用GIS空间分析进行台风相似路径筛选及预测[J]. 测绘通报, 2014(5): 115-118. WANG Xina, HUANG Huabing, BAN Ya, et al. Analogy typhoon tracks screening and forecasting based on GIS spatial analysis[J]. Bulletin of Surveying and Mapping, 2014(5): 115-118. [7] SRIVASTAVA S, NG K K, DELP E J. Coordinate mapping and analysis of vehicle trajectory for anomaly detection[C]//Proceedings of 2011 IEEE International Conference on Multimedia and Expo. Barcelona, Spain: IEEE, 2011: 1-6. [8] GUI Z, SUN Y, YANG L. LSI-LSTM: an attention-aware LSTM for real-time driving destination prediction by considering location semantics and location importance of trajectory points[J]. Neurocomputing, 2021, 440: 72-88. [9] 江婧, 张怀峰, 皮德常. 基于卷积神经网络的移动对象目的地预测[J]. 小型微型计算机系统, 2019, 40(12): 2519-2525. JIANG Jing, ZHANG Huaifeng, PI Dechang. Destination prediction of moving objects based on convolutional neural networks[J]. Journal of Chinese Computer Systems, 2019, 40(12): 2519-2525. [10] JEUNG H, YIU M L, ZHOU Xiaofang, et al. Discovery of convoys in trajectory databases[J]. Proceedings of the VLDB Endowment, 2008, 1(1): 1068-1080. [11] LI X, CEIKUTE V, JENSEN C S, et al. Effective online group discovery in trajectory databases[J]//IEEE Transactions on Knowledge and Data Engineering. 2012, 25(12): 2752-2766. [12] SU Han, ZHENG Kai, WANG Haozhou, et al. Calibrating trajectory data for similarity-based analysis[C]//Proceedings of 2013 ACM SIGMOD International Conference on Management of Data. New York, NY, USA: ACM Press, 2013: 833-844. [13] NASCIMENTO J C, FIGUEIREDO M A T, MARQUES J S. Trajectory classification using switched dynamical hidden Markov models[J]. IEEE Transactions on Image Processing, 2010, 19(5): 1338-1348. [14] LI Huanhuan, LIU Jingxian, YANG Zaili, et al. Adaptively constrained dynamic time warping for time series classification and clustering[J]. Information Sciences, 2020, 534: 97-116. [15] BENGIO Y, DUCHARME R, VINCENT P, et al. A neural probabilistic language model[J]. Journal of Machine Learning Research, 2003, 3: 1137-1155. [16] MIKOLOV T, CHEN K, CORRADO G, et al. Efficient estimation of word representations in vector space[C]//Proceedings of the 2nd International Conference for Learning Representations .[S.l.]:ICLR,2014. [17] SUTSKEVER I, VINYALS O, LE Q V. Sequence to sequence learning with neural networks[J]. Advances in neural information processing systems, 2014,10:3104-3112. [18] LI Xiucheng, ZHAO Kaiqi, CONG Gao, et al. Deep representation learning for trajectory similarity computation[C]//Proceedings of the 34th International Conference on Data Engineering (ICDE). Paris, France: IEEE, 2018: 617-628. [19] ZHANG Yifan, LIU An, LIU Guanfeng, et al. Deep representation learning of activity trajectory similarity computation[C]//Proceedings of 2019 IEEE International Conference on Web Services (ICWS).Milan, Italy: IEEE, 2019: 312-319. [20] DI Y, CHAO Z, ZHU Z, et al. Trajectory clustering via deep representation learning[C]//Proceedings of 2017 International Joint Conference on Neural Networks. Anchorage, AK,USA:IEEE, 2017: 3880-3887. [21] FU T, LEE W C. Trembr: exploring road networks for trajectory representation learning[J]. ACM Transactions on Intelligent Systems and Technology, 2020, 11(1): 1-25. [22] 曹翰林, 唐海娜, 王飞, 等. 轨迹表示学习技术研究进展[J]. 软件学报, 2021, 32(5): 1461-1479. CAO Hanlin, TANG Haina, WANG Fei, et al. Survey on trajectory representation learning techniques[J]. Journal of Software, 2021, 32(5): 1461-1479. [23] LIAN Defu, WU Yongji, GE Yong, et al. Geography-aware sequential location recommendation[C]//Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining.Virtual Event, CA, USA: ACM Press, 2020: 2009-2019. [24] CHUNG J, GULCEHRE C, CHO K, et al. Empirical evaluation of gated recurrent neural networks on sequence modeling[EB/OL].[2021-08-21]. https://arxiv.org/abs/1412.3555. [25] HOCHREITER S, SCHMIDHUBER J. Long short-term memory[J]. Neural Computation, 1997, 9(8): 1735-1780. [26] VASWANI A, SHAZEER N, PARMAR N, et al. Attention is all you need[C]//Proceedings of the 31st International Conference on Neural Information Processing Systems. Long Beach, CA, USA: ACM Press, 2017: 6000-6010. [27] DEVLIN J, CHANG M W, LEE K, et al. BERT: pre-training of deep bidirectional transformers for language understanding[C]//Proceedings of 2019 Conference of the North American Chapter of the Association for Computational Linguistics.Minneapolis,MN,USA:IEEE,2019. [28] KINGMA D, BA J. Adam: a method for stochastic optimization [C]//Proceedings of the 3rd International Conference for Learning Representations.San Diego,CA, USA:ICLR,2015. [29] YI B K, JAGADISH H V, FALOUTSOS C. Efficient retrieval of similar time sequences under time warping[C]//Proceedings of the 14th International Conference on Data Engineering. Orlando, FL, USA: IEEE, 2002: 201-208. [30] CHEN L, OZSU M T, ORIA V. Robust and fast similarity search for moving object trajectories[C]//Proceedings of 2005 ACM SIGMOD International Conference on Management of Data. [S.l.]: ACM Press, 2005. [31] VLACHOS M, KOLLIOS G, GUNOPULOS D. Discovering similar multidimensional trajectories[C]//Proceedings of the 18th International Conference on Data Engineering. San Jose, CA, USA: IEEE, 2002: 673-684. [32] REIMERS N, GUREVYCH I. Sentence-BERT: sentence embeddings using siamese BERT-Networks [C]//Proceedings of 2019 Conference on Empirical Methods in Natural Language. Hong Kong, China: IEEE, 2019. [33] LI Xiang, HUA Yixin, LIU Wenbing. A method of road data aided inertial navigation by using learning to rank and ICCP algorithm[J]. Journal of Geodesy and Geoinformation Science, 2021, 4(4): 84-96. |