{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,28]],"date-time":"2026-08-28T16:37:20Z","timestamp":1787935040164,"version":"build-2784847793"},"reference-count":60,"publisher":"Association for Computing Machinery (ACM)","issue":"4","content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["Proc. VLDB Endow."],"published-print":{"date-parts":[[2022,12]]},"abstract":"<jats:p>\n            Modern location-based systems have stimulated explosive growth of urban trajectory data and promoted many real-world applications,\n            <jats:italic>e.g.<\/jats:italic>\n            , trajectory prediction. However, heavy big data processing overhead and privacy concerns hinder trajectory acquisition and utilization. Inspired by regular trajectory distribution on transportation road networks, we propose to model trajectory data privately with a deep generative model and leverage the model to generate representative trajectories for downstream tasks or directly support these tasks (\n            <jats:italic>e.g.<\/jats:italic>\n            , popularity ranking), rather than acquiring and processing the original big trajectory data. Nevertheless, it is rather challenging to model high-dimensional trajectories with time-varying yet skewed distribution. To address this problem, we model and generate trajectory sequence with judiciously encoded spatio-temporal features over skewed distribution by leveraging an important factor neglected by the literature - the underlying road properties (\n            <jats:italic>e.g.<\/jats:italic>\n            , road types and directions), which are closely related to trajectory distribution. Specifically, we decompose trajectory into map-matched road sequence with temporal information and embed them to encode spatio-temporal features. Then, we enhance trajectory representation by encoding inherent route planning patterns from the underlying road properties. Later, we encode spatial correlations among edges and daily and weekly temporal periodicity information. Next, we employ a meta-learning module to generate trajectory sequence step by step by learning generalized trajectory distribution patterns from skewed trajectory data based on the well-encoded trajectory prefix. Last but not least, we preserve trajectory privacy by learning the model differential privately with clipping gradients. Experiments on real-world datasets show that our method significantly outperforms existing methods.\n          <\/jats:p>","DOI":"10.14778\/3574245.3574277","type":"journal-article","created":{"date-parts":[[2023,2,21]],"date-time":"2023-02-21T23:14:12Z","timestamp":1677021252000},"page":"973-985","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":26,"title":["A Deep Generative Model for Trajectory Modeling and Utilization"],"prefix":"10.14778","volume":"16","author":[{"given":"Yong","family":"Wang","sequence":"first","affiliation":[{"name":"Tsinghua University, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guoliang","family":"Li","sequence":"additional","affiliation":[{"name":"Tsinghua University, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kaiyu","family":"Li","sequence":"additional","affiliation":[{"name":"Tsinghua University, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haitao","family":"Yuan","sequence":"additional","affiliation":[{"name":"Tsinghua University, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2023,2,21]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"crossref","unstructured":"Martin Abadi Andy Chu Ian Goodfellow H Brendan McMahan Ilya Mironov Kunal Talwar and Li Zhang. 2016. Deep learning with differential privacy. In SIGSAC. 308--318.  Martin Abadi Andy Chu Ian Goodfellow H Brendan McMahan Ilya Mironov Kunal Talwar and Li Zhang. 2016. Deep learning with differential privacy. In SIGSAC. 308--318.","DOI":"10.1145\/2976749.2978318"},{"key":"e_1_2_1_2_1","doi-asserted-by":"crossref","unstructured":"Michael Balmer Marcel Rieser Konrad Meister David Charypar Nicolas Lefebvre and Kai Nagel. 2009. MATSim-T: Architecture and simulation times. In Multi-agent systems for traffic and transportation engineering. IGI Global 57--78.  Michael Balmer Marcel Rieser Konrad Meister David Charypar Nicolas Lefebvre and Kai Nagel. 2009. MATSim-T: Architecture and simulation times. In Multi-agent systems for traffic and transportation engineering. IGI Global 57--78.","DOI":"10.4018\/978-1-60566-226-8.ch003"},{"key":"e_1_2_1_3_1","volume-title":"Andrew K Skidmore, and Bert AKG Toxopeus.","author":"Baratchi Mitra","year":"2014","unstructured":"Mitra Baratchi , Nirvana Meratnia , Paul JM Havinga , Andrew K Skidmore, and Bert AKG Toxopeus. 2014 . A hierarchical hidden semi-Markov model for modeling mobility data. In UbiComp . 401--412. Mitra Baratchi, Nirvana Meratnia, Paul JM Havinga, Andrew K Skidmore, and Bert AKG Toxopeus. 2014. A hierarchical hidden semi-Markov model for modeling mobility data. In UbiComp. 401--412."},{"key":"e_1_2_1_4_1","volume-title":"Theory of Cryptography Conference. Springer, 437--454","author":"Beimel Amos","year":"2010","unstructured":"Amos Beimel , Shiva Prasad Kasiviswanathan , and Kobbi Nissim . 2010 . Bounds on the sample complexity for private learning and private data release . In Theory of Cryptography Conference. Springer, 437--454 . Amos Beimel, Shiva Prasad Kasiviswanathan, and Kobbi Nissim. 2010. Bounds on the sample complexity for private learning and private data release. In Theory of Cryptography Conference. Springer, 437--454."},{"key":"e_1_2_1_5_1","volume-title":"Green transportation logistics","author":"Bekta\u015f Tolga","unstructured":"Tolga Bekta\u015f , Emrah Demir , and Gilbert Laporte . 2016. Green vehicle routing . In Green transportation logistics . Springer , 243--265. Tolga Bekta\u015f, Emrah Demir, and Gilbert Laporte. 2016. Green vehicle routing. In Green transportation logistics. Springer, 243--265."},{"key":"e_1_2_1_6_1","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1023\/A:1015231126594","article-title":"A framework for generating network-based moving objects","volume":"6","author":"Brinkhoff Thomas","year":"2002","unstructured":"Thomas Brinkhoff . 2002 . A framework for generating network-based moving objects . GeoInformatica 6 , 2 (2002), 153 -- 180 . Thomas Brinkhoff. 2002. 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Discovering popular routes from trajectories. In ICDE. IEEE , 900--911. Zaiben Chen, Heng Tao Shen, and Xiaofang Zhou. 2011. Discovering popular routes from trajectories. In ICDE. IEEE, 900--911."},{"key":"e_1_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1007\/s41019-022-00180-w"},{"key":"e_1_2_1_10_1","first-page":"3","article-title":"The algorithmic foundations of differential privacy","volume":"9","author":"Dwork Cynthia","year":"2014","unstructured":"Cynthia Dwork , Aaron Roth , 2014 . The algorithmic foundations of differential privacy . Found. Trends Theor. Comput. Sci. 9 , 3 - 4 (2014), 211--407. Cynthia Dwork, Aaron Roth, et al. 2014. The algorithmic foundations of differential privacy. Found. Trends Theor. Comput. Sci. 9, 3-4 (2014), 211--407.","journal-title":"Found. Trends Theor. Comput. 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Nan Gao, Hao Xue, Wei Shao, Sichen Zhao, Kyle Kai Qin, Arian Prabowo, Mohammad Saiedur Rahaman, and Flora D Salim. 2020. Generative Adversarial Networks for Spatio-temporal Data: A Survey. arXiv preprint arXiv:2008.08903 (2020)."},{"key":"e_1_2_1_14_1","first-page":"1","article-title":"Generative adversarial networks for spatio-temporal data: A survey","volume":"13","author":"Gao Nan","year":"2022","unstructured":"Nan Gao , Hao Xue , Wei Shao , Sichen Zhao , Kyle Kai Qin , Arian Prabowo , Mohammad Saiedur Rahaman , and Flora D Salim . 2022 . Generative adversarial networks for spatio-temporal data: A survey . ACM Transactions on Intelligent Systems and Technology (TIST) 13 , 2 (2022), 1 -- 25 . Nan Gao, Hao Xue, Wei Shao, Sichen Zhao, Kyle Kai Qin, Arian Prabowo, Mohammad Saiedur Rahaman, and Flora D Salim. 2022. Generative adversarial networks for spatio-temporal data: A survey. 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Generating sequences with recurrent neural networks. arXiv preprint arXiv:1308.0850 (2013)."},{"key":"e_1_2_1_17_1","doi-asserted-by":"crossref","unstructured":"Aditya Grover and Jure Leskovec. 2016. node2vec: Scalable feature learning for networks. In SIGKDD. 855--864.  Aditya Grover and Jure Leskovec. 2016. node2vec: Scalable feature learning for networks. In SIGKDD. 855--864.","DOI":"10.1145\/2939672.2939754"},{"key":"e_1_2_1_18_1","first-page":"2315","article-title":"Differentially private and utility preserving publication of trajectory data","volume":"18","author":"Gursoy Mehmet Emre","year":"2018","unstructured":"Mehmet Emre Gursoy , Ling Liu , Stacey Truex , and Lei Yu . 2018 . Differentially private and utility preserving publication of trajectory data . TMC 18 , 10 (2018), 2315 -- 2329 . Mehmet Emre Gursoy, Ling Liu, Stacey Truex, and Lei Yu. 2018. Differentially private and utility preserving publication of trajectory data. TMC 18, 10 (2018), 2315--2329.","journal-title":"TMC"},{"key":"e_1_2_1_19_1","first-page":"1154","article-title":"DPT: differentially private trajectory synthesis using hierarchical reference systems","volume":"8","author":"He Xi","year":"2015","unstructured":"Xi He , Graham Cormode , and 2015 . DPT: differentially private trajectory synthesis using hierarchical reference systems . PVLDB 8 , 11 (2015), 1154 -- 1165 . Xi He, Graham Cormode, and et al. 2015. DPT: differentially private trajectory synthesis using hierarchical reference systems. PVLDB 8, 11 (2015), 1154--1165.","journal-title":"PVLDB"},{"key":"e_1_2_1_20_1","volume-title":"Long short-term memory. Neural computation 9, 8","author":"Hochreiter Sepp","year":"1997","unstructured":"Sepp Hochreiter and J\u00fcrgen Schmidhuber . 1997. Long short-term memory. Neural computation 9, 8 ( 1997 ), 1735--1780. Sepp Hochreiter and J\u00fcrgen Schmidhuber. 1997. Long short-term memory. Neural computation 9, 8 (1997), 1735--1780."},{"key":"e_1_2_1_21_1","volume-title":"Meta-learning in neural networks: A survey. arXiv preprint arXiv:2004.05439","author":"Hospedales Timothy","year":"2020","unstructured":"Timothy Hospedales , Antreas Antoniou , Paul Micaelli , and Amos Storkey . 2020. Meta-learning in neural networks: A survey. arXiv preprint arXiv:2004.05439 ( 2020 ). Timothy Hospedales, Antreas Antoniou, Paul Micaelli, and Amos Storkey. 2020. Meta-learning in neural networks: A survey. arXiv preprint arXiv:2004.05439 (2020)."},{"key":"e_1_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2015.2485213"},{"key":"e_1_2_1_23_1","volume-title":"Crime-GAN: A Context-based Sequence Generative Network for Crime Forecasting with Adversarial Loss","author":"Jin Guangyin","unstructured":"Guangyin Jin , Qi Wang , Xia Zhao , Yanghe Feng , Qing Cheng , and Jincai Huang . 2019. Crime-GAN: A Context-based Sequence Generative Network for Crime Forecasting with Adversarial Loss . In IEEE BigData. IEEE , 1460--1469. Guangyin Jin, Qi Wang, Xia Zhao, Yanghe Feng, Qing Cheng, and Jincai Huang. 2019. Crime-GAN: A Context-based Sequence Generative Network for Crime Forecasting with Adversarial Loss. In IEEE BigData. IEEE, 1460--1469."},{"key":"e_1_2_1_24_1","volume-title":"Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907","author":"Kipf Thomas N","year":"2016","unstructured":"Thomas N Kipf and Max Welling . 2016. Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907 ( 2016 ). Thomas N Kipf and Max Welling. 2016. Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907 (2016)."},{"key":"e_1_2_1_25_1","volume-title":"Social-bigat: Multimodal trajectory forecasting using bicycle-gan and graph attention networks. In NIPS. 137--146.","author":"Kosaraju Vineet","year":"2019","unstructured":"Vineet Kosaraju , Amir Sadeghian , Roberto Mart\u00edn-Mart\u00edn , Ian Reid , Hamid Rezatofighi , and Silvio Savarese . 2019 . Social-bigat: Multimodal trajectory forecasting using bicycle-gan and graph attention networks. In NIPS. 137--146. Vineet Kosaraju, Amir Sadeghian, Roberto Mart\u00edn-Mart\u00edn, Ian Reid, Hamid Rezatofighi, and Silvio Savarese. 2019. Social-bigat: Multimodal trajectory forecasting using bicycle-gan and graph attention networks. In NIPS. 137--146."},{"key":"e_1_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE.2012.93"},{"key":"e_1_2_1_27_1","volume-title":"Diffusion convolutional recurrent neural network: Data-driven traffic forecasting. arXiv preprint arXiv:1707.01926","author":"Li Yaguang","year":"2017","unstructured":"Yaguang Li , Rose Yu , Cyrus Shahabi , and Yan Liu . 2017. Diffusion convolutional recurrent neural network: Data-driven traffic forecasting. arXiv preprint arXiv:1707.01926 ( 2017 ). Yaguang Li, Rose Yu, Cyrus Shahabi, and Yan Liu. 2017. Diffusion convolutional recurrent neural network: Data-driven traffic forecasting. arXiv preprint arXiv:1707.01926 (2017)."},{"key":"e_1_2_1_28_1","doi-asserted-by":"crossref","unstructured":"Qiang Liu Shu Wu and etal 2016. Predicting the next location: A recurrent model with spatial and temporal contexts. In AAAI.  Qiang Liu Shu Wu and et al. 2016. Predicting the next location: A recurrent model with spatial and temporal contexts. In AAAI.","DOI":"10.1609\/aaai.v30i1.9971"},{"key":"e_1_2_1_29_1","volume-title":"Location Privacy and Security Workshop. 1--7.","author":"Liu Xi","year":"2018","unstructured":"Xi Liu , Hanzhou Chen , and Clio Andris . 2018 . trajGANs: Using generative adversarial networks for geo-privacy protection of trajectory data (Vision paper) . In Location Privacy and Security Workshop. 1--7. Xi Liu, Hanzhou Chen, and Clio Andris. 2018. trajGANs: Using generative adversarial networks for geo-privacy protection of trajectory data (Vision paper). In Location Privacy and Security Workshop. 1--7."},{"key":"e_1_2_1_30_1","first-page":"949","article-title":"Direction-preserving trajectory simplification","volume":"6","author":"Long Cheng","year":"2013","unstructured":"Cheng Long , Raymond Chi-Wing Wong , and HV Jagadish . 2013 . Direction-preserving trajectory simplification . PVLDB 6 , 10 (2013), 949 -- 960 . Cheng Long, Raymond Chi-Wing Wong, and HV Jagadish. 2013. Direction-preserving trajectory simplification. PVLDB 6, 10 (2013), 949--960.","journal-title":"PVLDB"},{"key":"e_1_2_1_31_1","volume-title":"2018 21st international conference on intelligent transportation systems (ITSC). IEEE, 2575--2582","author":"Lopez Pablo Alvarez","year":"2018","unstructured":"Pablo Alvarez Lopez , Michael Behrisch , Laura Bieker-Walz , Jakob Erdmann , Yun-Pang Fl\u00f6tter\u00f6d , Robert Hilbrich , Leonhard L\u00fccken , Johannes Rummel , Peter Wagner , and Evamarie Wie\u00dfner . 2018 . Microscopic traffic simulation using sumo . In 2018 21st international conference on intelligent transportation systems (ITSC). IEEE, 2575--2582 . Pablo Alvarez Lopez, Michael Behrisch, Laura Bieker-Walz, Jakob Erdmann, Yun-Pang Fl\u00f6tter\u00f6d, Robert Hilbrich, Leonhard L\u00fccken, Johannes Rummel, Peter Wagner, and Evamarie Wie\u00dfner. 2018. Microscopic traffic simulation using sumo. In 2018 21st international conference on intelligent transportation systems (ITSC). IEEE, 2575--2582."},{"key":"e_1_2_1_32_1","doi-asserted-by":"crossref","unstructured":"Yin Lou Chengyang Zhang Yu Zheng Xing Xie Wei Wang and Yan Huang. 2009. Map-matching for low-sampling-rate GPS trajectories. In SIGSPATIAL. 352--361.  Yin Lou Chengyang Zhang Yu Zheng Xing Xie Wei Wang and Yan Huang. 2009. Map-matching for low-sampling-rate GPS trajectories. In SIGSPATIAL. 352--361.","DOI":"10.1145\/1653771.1653820"},{"key":"e_1_2_1_33_1","unstructured":"Yonghong Luo Xiangrui Cai Ying Zhang Jun Xu etal 2018. Multivariate time series imputation with generative adversarial networks. In NIPS. 1596--1607.  Yonghong Luo Xiangrui Cai Ying Zhang Jun Xu et al. 2018. Multivariate time series imputation with generative adversarial networks. In NIPS. 1596--1607."},{"key":"e_1_2_1_34_1","volume-title":"R\u00e9nyi differential privacy","author":"Mironov Ilya","unstructured":"Ilya Mironov . 2017. R\u00e9nyi differential privacy . In CSF. IEEE , 263--275. Ilya Mironov. 2017. R\u00e9nyi differential privacy. In CSF. IEEE, 263--275."},{"key":"e_1_2_1_35_1","volume-title":"TRANSIMS traffic flow characteristics. arXiv preprint adap-org\/9710003","author":"Nagel Kai","year":"1997","unstructured":"Kai Nagel , Paula Stretz , Martin Pieck , Rick Donnelly , and Christopher L Barrett . 1997. TRANSIMS traffic flow characteristics. arXiv preprint adap-org\/9710003 ( 1997 ). Kai Nagel, Paula Stretz, Martin Pieck, Rick Donnelly, and Christopher L Barrett. 1997. TRANSIMS traffic flow characteristics. arXiv preprint adap-org\/9710003 (1997)."},{"key":"e_1_2_1_36_1","volume-title":"An overview of agent-based traffic simulators. Transportation research interdisciplinary perspectives 12","author":"Nguyen Johannes","year":"2021","unstructured":"Johannes Nguyen , Simon T Powers , Neil Urquhart , Thomas Farrenkopf , and Michael Guckert . 2021. An overview of agent-based traffic simulators. Transportation research interdisciplinary perspectives 12 ( 2021 ), 100486. Johannes Nguyen, Simon T Powers, Neil Urquhart, Thomas Farrenkopf, and Michael Guckert. 2021. An overview of agent-based traffic simulators. Transportation research interdisciplinary perspectives 12 (2021), 100486."},{"key":"e_1_2_1_37_1","volume-title":"Markov chains. Number 2","author":"Norris James R","unstructured":"James R Norris . 1998. Markov chains. Number 2 . Cambridge university press . James R Norris. 1998. Markov chains. Number 2. Cambridge university press."},{"key":"e_1_2_1_38_1","doi-asserted-by":"crossref","unstructured":"Kun Ouyang Reza Shokri David S Rosenblum and Wenzhuo Yang. 2018. A Non-Parametric Generative Model for Human Trajectories.. In IJCAI. 3812--3817.  Kun Ouyang Reza Shokri David S Rosenblum and Wenzhuo Yang. 2018. A Non-Parametric Generative Model for Human Trajectories.. In IJCAI. 3812--3817.","DOI":"10.24963\/ijcai.2018\/530"},{"key":"e_1_2_1_39_1","volume-title":"Proceedings of the Web Conference 2021. 1846","author":"Pan Zheyi","year":"2021","unstructured":"Zheyi Pan , Songyu Ke , Xiaodu Yang , Yuxuan Liang , Yong Yu , Junbo Zhang , and Yu Zheng . 2021 . AutoSTG: Neural Architecture Search for Predictions of Spatio-Temporal Graph . In Proceedings of the Web Conference 2021. 1846 --1855. Zheyi Pan, Songyu Ke, Xiaodu Yang, Yuxuan Liang, Yong Yu, Junbo Zhang, and Yu Zheng. 2021. AutoSTG: Neural Architecture Search for Predictions of Spatio-Temporal Graph. In Proceedings of the Web Conference 2021. 1846--1855."},{"key":"e_1_2_1_40_1","doi-asserted-by":"crossref","unstructured":"Zheyi Pan Yuxuan Liang Weifeng Wang Yong Yu Yu Zheng and Junbo Zhang. 2019. Urban traffic prediction from spatio-temporal data using deep meta learning. In SIGKDD. 1720--1730.  Zheyi Pan Yuxuan Liang Weifeng Wang Yong Yu Yu Zheng and Junbo Zhang. 2019. Urban traffic prediction from spatio-temporal data using deep meta learning. In SIGKDD. 1720--1730.","DOI":"10.1145\/3292500.3330884"},{"key":"e_1_2_1_41_1","volume-title":"Spatio-Temporal Meta Learning for Urban Traffic Prediction. TKDE","author":"Pan Zheyi","year":"2020","unstructured":"Zheyi Pan , Wentao Zhang , Yuxuan Liang , Weinan Zhang , Yong Yu , Junbo Zhang , and Yu Zheng . 2020. Spatio-Temporal Meta Learning for Urban Traffic Prediction. TKDE ( 2020 ). Zheyi Pan, Wentao Zhang, Yuxuan Liang, Weinan Zhang, Yong Yu, Junbo Zhang, and Yu Zheng. 2020. Spatio-Temporal Meta Learning for Urban Traffic Prediction. TKDE (2020)."},{"key":"e_1_2_1_42_1","unstructured":"Alexandre Papadopoulos Pierre Roy Jean-Charles R\u00e9gin and Fran\u00e7ois Pachet. 2015. Generating all possible palindromes from ngram corpora. In IJCAI.  Alexandre Papadopoulos Pierre Roy Jean-Charles R\u00e9gin and Fran\u00e7ois Pachet. 2015. Generating all possible palindromes from ngram corpora. In IJCAI."},{"key":"e_1_2_1_43_1","volume-title":"symposium on SP. IEEE, 247--262","author":"Shokri Reza","year":"2011","unstructured":"Reza Shokri , George Theodorakopoulos , Jean-Yves Le Boudec , and Jean-Pierre Hubaux . 2011 . Quantifying location privacy . In symposium on SP. IEEE, 247--262 . Reza Shokri, George Theodorakopoulos, Jean-Yves Le Boudec, and Jean-Pierre Hubaux. 2011. Quantifying location privacy. In symposium on SP. IEEE, 247--262."},{"key":"e_1_2_1_44_1","doi-asserted-by":"crossref","unstructured":"Mudhakar Srivatsa Raghu Ganti Jingjing Wang and Vinay Kolar. 2013. Map matching: Facts and myths. In SIGSPATIAL. 484--487.  Mudhakar Srivatsa Raghu Ganti Jingjing Wang and Vinay Kolar. 2013. Map matching: Facts and myths. In SIGSPATIAL. 484--487.","DOI":"10.1145\/2525314.2525466"},{"key":"e_1_2_1_45_1","first-page":"557","article-title":"k-anonymity: A model for protecting privacy","volume":"10","author":"Sweeney Latanya","year":"2002","unstructured":"Latanya Sweeney . 2002 . k-anonymity: A model for protecting privacy . IJUFKS 10 , 05 (2002), 557 -- 570 . Latanya Sweeney. 2002. k-anonymity: A model for protecting privacy. IJUFKS 10, 05 (2002), 557--570.","journal-title":"IJUFKS"},{"key":"e_1_2_1_46_1","volume-title":"Meta-learning: A survey. arXiv preprint arXiv:1810.03548","author":"Vanschoren Joaquin","year":"2018","unstructured":"Joaquin Vanschoren . 2018 . Meta-learning: A survey. arXiv preprint arXiv:1810.03548 (2018). Joaquin Vanschoren. 2018. Meta-learning: A survey. arXiv preprint arXiv:1810.03548 (2018)."},{"key":"e_1_2_1_47_1","volume-title":"A Survey on Trajectory Data Management, Analytics, and Learning. arXiv preprint arXiv:2003.11547","author":"Wang Sheng","year":"2020","unstructured":"Sheng Wang , Zhifeng Bao , J Shane Culpepper , and Gao Cong . 2020. A Survey on Trajectory Data Management, Analytics, and Learning. arXiv preprint arXiv:2003.11547 ( 2020 ). Sheng Wang, Zhifeng Bao, J Shane Culpepper, and Gao Cong. 2020. A Survey on Trajectory Data Management, Analytics, and Learning. arXiv preprint arXiv:2003.11547 (2020)."},{"key":"e_1_2_1_48_1","first-page":"1249","article-title":"Querying shortest paths on time dependent road networks","volume":"12","author":"Wang Yong","year":"2019","unstructured":"Yong Wang , Guoliang Li , and Nan Tang . 2019 . Querying shortest paths on time dependent road networks . PVLDB 12 , 11 (2019), 1249 -- 1261 . Yong Wang, Guoliang Li, and Nan Tang. 2019. Querying shortest paths on time dependent road networks. PVLDB 12, 11 (2019), 1249--1261.","journal-title":"PVLDB"},{"key":"e_1_2_1_49_1","first-page":"1","article-title":"Exploring pattern-aware travel routes for trajectory search","volume":"4","author":"Wei Ling-Yin","year":"2013","unstructured":"Ling-Yin Wei , Wen-Chih Peng , and Wang-Chien Lee . 2013 . Exploring pattern-aware travel routes for trajectory search . TIST 4 , 3 (2013), 1 -- 25 . Ling-Yin Wei, Wen-Chih Peng, and Wang-Chien Lee. 2013. Exploring pattern-aware travel routes for trajectory search. TIST 4, 3 (2013), 1--25.","journal-title":"TIST"},{"key":"e_1_2_1_50_1","doi-asserted-by":"crossref","unstructured":"Hao Wu Ziyang Chen Weiwei Sun Baihua Zheng and Wei Wang. 2017. Modeling trajectories with recurrent neural networks. IJCAI.  Hao Wu Ziyang Chen Weiwei Sun Baihua Zheng and Wei Wang. 2017. Modeling trajectories with recurrent neural networks. IJCAI.","DOI":"10.24963\/ijcai.2017\/430"},{"key":"e_1_2_1_51_1","doi-asserted-by":"crossref","first-page":"547","DOI":"10.1080\/13658816.2017.1400548","article-title":"Fast map matching, an algorithm integrating hidden Markov model with precomputation","volume":"32","author":"Yang Can","year":"2018","unstructured":"Can Yang and Gyozo Gidofalvi . 2018 . Fast map matching, an algorithm integrating hidden Markov model with precomputation . Int. J. Geogr. Inf. Syst. 32 , 3 (2018), 547 -- 570 . Can Yang and Gyozo Gidofalvi. 2018. Fast map matching, an algorithm integrating hidden Markov model with precomputation. Int. J. Geogr. Inf. Syst. 32, 3 (2018), 547--570.","journal-title":"Int. J. Geogr. Inf. Syst."},{"key":"e_1_2_1_52_1","volume-title":"Context-Aware Path Ranking in Road Networks. TKDE","author":"Yang Sean Bin","year":"2020","unstructured":"Sean Bin Yang , Chenjuan Guo , and Bin Yang . 2020. Context-Aware Path Ranking in Road Networks. TKDE ( 2020 ). Sean Bin Yang, Chenjuan Guo, and Bin Yang. 2020. Context-Aware Path Ranking in Road Networks. TKDE (2020)."},{"key":"e_1_2_1_53_1","first-page":"613","article-title":"A novel representation and compression for queries on trajectories in road networks","volume":"30","author":"Yang Xiaochun","year":"2017","unstructured":"Xiaochun Yang , Bin Wang , Kai Yang , Chengfei Liu , and Baihua Zheng . 2017 . A novel representation and compression for queries on trajectories in road networks . TKDE 30 , 4 (2017), 613 -- 629 . Xiaochun Yang, Bin Wang, Kai Yang, Chengfei Liu, and Baihua Zheng. 2017. A novel representation and compression for queries on trajectories in road networks. TKDE 30, 4 (2017), 613--629.","journal-title":"TKDE"},{"key":"e_1_2_1_54_1","unstructured":"Huaxiu Yao Yiding Liu Ying Wei Xianfeng Tang and Zhenhui Li. 2019. Learning from multiple cities: A meta-learning approach for spatial-temporal prediction. In WWW. 2181--2191.  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Data Sci","volume":"6","author":"Yuan Haitao","year":"2021","unstructured":"Haitao Yuan and Guoliang Li . 2021 . A Survey of Traffic Prediction: from Spatio-Temporal Data to Intelligent Transportation. Data Sci . Eng. 6 , 1 (2021), 63 -- 85 . Haitao Yuan and Guoliang Li. 2021. A Survey of Traffic Prediction: from Spatio-Temporal Data to Intelligent Transportation. Data Sci. Eng. 6, 1 (2021), 63--85.","journal-title":"Eng."},{"key":"e_1_2_1_57_1","doi-asserted-by":"crossref","unstructured":"Haitao Yuan Guoliang Li Zhifeng Bao and Ling Feng. 2020. Effective Travel Time Estimation: When Historical Trajectories over Road Networks Matter. In SIGMOD.  Haitao Yuan Guoliang Li Zhifeng Bao and Ling Feng. 2020. Effective Travel Time Estimation: When Historical Trajectories over Road Networks Matter. In SIGMOD.","DOI":"10.1145\/3318464.3389771"},{"key":"e_1_2_1_58_1","first-page":"934","article-title":"Trajectory simplification: an experimental study and quality analysis","volume":"11","author":"Zhang Dongxiang","year":"2018","unstructured":"Dongxiang Zhang , Mengting Ding , Dingyu Yang , Yi Liu , Ju Fan , and Heng Tao Shen . 2018 . Trajectory simplification: an experimental study and quality analysis . PVLDB 11 , 9 (2018), 934 -- 946 . Dongxiang Zhang, Mengting Ding, Dingyu Yang, Yi Liu, Ju Fan, and Heng Tao Shen. 2018. Trajectory simplification: an experimental study and quality analysis. PVLDB 11, 9 (2018), 934--946.","journal-title":"PVLDB"},{"key":"e_1_2_1_59_1","volume-title":"Proceedings of the AAAI Conference on Artificial Intelligence","volume":"34","author":"Zheng Chuanpan","year":"2020","unstructured":"Chuanpan Zheng , Xiaoliang Fan , Cheng Wang , and Jianzhong Qi . 2020 . Gman: A graph multi-attention network for traffic prediction . In Proceedings of the AAAI Conference on Artificial Intelligence , Vol. 34 . 1234--1241. Chuanpan Zheng, Xiaoliang Fan, Cheng Wang, and Jianzhong Qi. 2020. Gman: A graph multi-attention network for traffic prediction. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 34. 1234--1241."},{"key":"e_1_2_1_60_1","volume-title":"Proceedings of the AAAI Conference on Artificial Intelligence","volume":"27","author":"Zheng Jiangchuan","year":"2013","unstructured":"Jiangchuan Zheng and Lionel M Ni . 2013 . Time-dependent trajectory regression on road networks via multi-task learning . In Proceedings of the AAAI Conference on Artificial Intelligence , Vol. 27 . 1048--1055. Jiangchuan Zheng and Lionel M Ni. 2013. Time-dependent trajectory regression on road networks via multi-task learning. 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