通信学报 ›› 2020, Vol. 41 ›› Issue (7): 18-28.doi: 10.11959/j.issn.1000-436x.2020147
徐丰力,李勇
修回日期:
2020-06-25
出版日期:
2020-07-25
发布日期:
2020-08-01
作者简介:
徐丰力(1993- ),男,广东深圳人,清华大学博士生,主要研究方向为移动数据挖掘、城市计算、数据隐私|李勇(1985- ),男,湖南长沙人,博士,清华大学副教授、博士生导师,主要研究方向为数据挖掘、城市计算、移动计算
基金资助:
Fengli XU,Yong LI
Revised:
2020-06-25
Online:
2020-07-25
Published:
2020-08-01
Supported by:
摘要:
针对城市环境成为移动通信、交通调度、疾病防控等领域的典型场景,而用户的移动行为建模对这些关键领域有重要的应用与研究价值,梳理、总结城市环境下移动行为建模的研究进展与现状,为该领域的相关研究提供文献概述。首先讨论了城市环境移动行为建模问题面临的主要挑战及对应的核心科学问题,即移动行为数据增强算法、城市结构感知的移动行为模式识别、多时空尺度的移动行为预测模型和移动数据隐私保护机制问题。进一步地,围绕这些核心科学问题梳理总结了该领域近年来的发展脉络与最新研究成果,为未来的研究工作奠定了基础。
中图分类号:
徐丰力,李勇. 城市环境下的用户移动行为建模概述[J]. 通信学报, 2020, 41(7): 18-28.
Fengli XU,Yong LI. Survey on user’s mobility behavior modelling in urban environment[J]. Journal on Communications, 2020, 41(7): 18-28.
[1] | DEVILLE P , LINARD C , MARTIN S ,et al. Dynamic population mapping using mobile phone data[J]. Proceedings of the National Academy of Sciences, 2014,111(45): 15888-15893. |
[2] | 单留举, 王晓东, 马英运 . 基于大数据的用户学习偏好建模及应用[J]. 计算机应用与软件, 2016,33(1): 77-80. |
SHAN L J , WANG X D , MA Y Y . User learning preference and application based on big data[J]. Computer Applications and Software, 2016,33(1): 77-80. | |
[3] | ZHENG K , YANG Z , ZHANG K ,et al. Big data-driven optimization for mobile networks toward 5G[J]. IEEE Network, 2016,30(1): 44-51. |
[4] | 尹浩, 乔波 . 大数据驱动的网络信息平面[J]. 计算机学报, 2016,39(1): 126-139. |
YIN H , QIAO B . Big data-driven network information plane[J]. Chinese Journal of Computers, 2016,39(1): 126-139. | |
[5] | NAIMI A I , WESTREICH D J . Big data:a revolution that will transform how we live,work,and think[J]. Mathematics & Computer Education, 2013,47(17): 181-183. |
[6] | 李国杰, 程学旗 . 大数据研究:未来科技及经济社会发展的重大战略领域——大数据的研究现状与科学思考[J]. 中国科学院院刊, 2012,27(6): 647-657. |
LI G J , CHEN X Q . Big data research:a major strategic field of future technology and economic and social development-research status and scientific thinking of big data[J]. Bulletin of Chinese Academy of Sciences, 2012,27(6): 647-657. | |
[7] | BROCKMANN D , HUFNAGEL L , GEISEL T . The scaling laws of human travel[J]. Nature, 2006,439(7075):462. |
[8] | LEE J G , HAN J , WHANG K Y . Trajectory clustering:a partition-and-group framework[C]// ACM SIGMOD International Conference on Management of Data. New York:ACM Press, 2007: 593-604. |
[9] | JIANG S , YANG Y , GUPTA S ,et al. The TimeGeo modeling framework for urban motility without travel surveys[J]. Proceedings of the National Academy of Sciences, 2016,113(37):e5370. |
[10] | QIAO S , SHEN D , WANG X ,et al. A self-adaptive parameter selection trajectory prediction approach via hidden Markov models[J]. IEEE Transactions on Intelligent Transportation Systems, 2015,16(1): 284-296. |
[11] | CAMAGNI R , GIBELLI M C , RIGAMONTI P . Urban mobility and urban form:the social and environmental costs of different patterns of urban expansion[J]. Ecological Economics, 2002,40(2): 199-216. |
[12] | SCHRANK D , EISELE B , LOMAX T . TTI’s 2012 urban mobility report[J]. The Texas A&M University System, 2012,4(1): 1-64. |
[13] | GOODSPEED R , YAN X , HARDY J ,et al. Comparing the data quality of global positioning system devices and mobile phones for assessing relationships between place,mobility,and health:field study[J]. JMIR mHealth and uHealth, 2018,6(8):e168. |
[14] | LOPEZ A J , SEMANJSKI I , GAUTAMA S ,et al. Assessment of smartphone positioning data quality in the scope of citizen science contributions[J]. Mobile Information Systems, 2017,2017(1): 1-11. |
[15] | BASSOLAS A , BARBOSA-FILHO H , DICKINSON B ,et al. Hierarchical organization of urban mobility and its connection with city livability[J]. Nature Communications, 2019,10(1): 1-10. |
[16] | GIANNOTTI F , NANNI M , PINELLI F ,et al. Trajectory pattern mining[C]// ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York:ACM Press, 2007: 330-339. |
[17] | HASAN S , SCHNEIDER C M , UKKUSURI S V ,et al. Spatiotemporal patterns of urban human mobility[J]. Journal of Statistical Physics, 2013,151(1-2): 304-318. |
[18] | FAN C , LIU Y , HUANG J ,et al. Correlation between social proximity and mobility similarity[J]. Scientific Reports, 2017,7(1): 1-8. |
[19] | LI H , MA D , MEDJAHED B ,et al. Data privacy in the emerging connected mobility services:architecture,use cases,privacy risks,and countermeasures[J]. SAE International Journal of Transportation Cybersecurity and Privacy, 2019,2(1): 49-61. |
[20] | SONG C , QU Z , BLUMM N ,et al. Limits of predictability in human mobility[J]. Science, 2010,327(5968): 1018-1021. |
[21] | DE MONTJOYE Y A , RADAELLI L , SINGH V K . Unique in the shopping mall:on the reidentifiability of credit card metadata[J]. Science, 2015,347(6221): 536-539. |
[22] | YOU L , ZHAO F , CHEAH L ,et al. A generic future mobility sensing system for travel data collection,management,fusion,and visualization[J]. IEEE Transactions on Intelligent Transportation Systems, 2019,1(1): 1-12. |
[23] | FENG J , ZHANG M , WANG H ,et al. DPLink:user identity linkage via deep neural network from heterogeneous mobility data[C]// The World Wide Web Conference. New York:ACM Press, 2019: 459-469. |
[24] | HOTEIT S , CHEN G , VIANA A ,et al. Filling the gaps:on the completion of sparse call detail records for mobility analysis[C]// Proceedings of the Eleventh ACM Workshop on Challenged Networks. New York:ACM Press, 2016: 45-50. |
[25] | HOYT H . The structure and growth of residential neighborhoods in American cities[M]. Washington: US Government Printing OfficePress, 1939. |
[26] | WU F . Mining heterogeneous data for semantic understanding of mobility data[D]. Pennsylvania:Pennsylvania State University, 2018,1(1): 1-120. |
[27] | CHEN M , LIU Y , YU X . NLPMM:a next location predictor with Markov modeling[C]// Pacific-Asia Conference on Knowledge Discovery and Data Mining. Berlin:Springer, 2014: 186-197. |
[28] | CAO H , XU F , SANKARANARAYANAN J ,et al. Habit2vec:trajectory semantic embedding for living pattern recognition in population[J]. IEEE Transactions on Mobile Computing, 2019,19(5): 1096-1108. |
[29] | LIU W , SHOJI Y . DeepVM:RNN-based vehicle mobility prediction to support intelligent vehicle applications[J]. IEEE Transactions on Industrial Informatics, 2019,16(6): 3997-4006. |
[30] | FENG J , LI Y , ZHANG C ,et al. Deepmove:predicting human mobility with attentional recurrent networks[C]// Proceedings of the 2018 World Wide Web Conference. New York:ACM Press, 2018: 1459-1468. |
[31] | XU F , ZHANG P , LI Y . Context-aware real-time population estimation for metropolis[C]// Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing. New York:ACM Press, 2016: 1064-1075. |
[32] | ZONG Z , FENG J , LIU K ,et al. DeepDPM:dynamic population mapping via deep neural network[C]// Proceedings of the AAAI Conference on Artificial Intelligence. Palo Alto:AAAI Press, 2019: 1294-1301. |
[33] | GAO J , SUN L , CAI M . Quantifying privacy vulnerability of individ ual mobility traces:a case study of license plate recognition data[J]. Transportation Research Part C:Emerging Technologies, 2019,104: 78-94. |
[34] | YOUNG M , RODRIGUEZ L , KELLER E ,et al. closed:balancing individual privacy and public accountability in data sharing[C]// Proceedings of the Conference on Fairness,Accountability,and Transparency.[S.l.:s.n]. 2019: 191-200. |
[35] | GOGA O , LOISEAU P , SOMMER R ,et al. On the reliability of profile matching across large online social networks[C]// Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York:ACM Press, 2015: 1799-1808. |
[36] | NARAYANAN A , SHMATIKOV V . De-anonymizing social networks[C]// 2009 30th IEEE Symposium on Security and Privacy. Piscataway:IEEE Press, 2009: 1-19. |
[37] | DE MONTJOYE Y A , HIDALGO C A , VERLEYSEN M ,et al. Unique in the crowd:the privacy bounds of human mobility[J]. Scientific Reports, 2013,3:1376. |
[38] | WANG H , LI Y , WANG G ,et al. You are how you move:linking multiple user identities from massive mobility traces[C]// Proceedings of the 2018 SIAM International Conference on Data Mining.[S.l.:s.n]. 2018: 189-197. |
[39] | MAMOULIS N , CAO H , KOLLIOS G ,et al. Mining,indexing,and querying historical spatiotemporal data[C]// ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York:ACM Press, 2004: 236-245. |
[40] | ZHANG K , LIN Y R , PELECHRINIS K . Eigen Transitions with hypothesis testing:the anatomy of urban mobility[C]// International AAAI Conference on Web and Social Media. Palo Alto:AAAI Press, 2016: 486-495. |
[41] | YAO D , ZHANG C , ZHU Z ,et al. Trajectory clustering via deep representation learning[C]// International Joint Conference on Neural Networks. Piscataway:IEEE Press, 2017: 3880-3887. |
[42] | VLACHOS M , HADJIELEFTHERIOU M , GUNOPULOS D ,et al. Indexing multi-dimensional time-series with support for multiple distance measures[C]// ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York:ACM Press, 2003: 216-225. |
[43] | GAO Q , ZHOU F , ZHANG K ,et al. Identifying human mobility via trajectory embeddings[C]// International Joint Conference on Artificial Intelligence.[S.l.:s.n]. 2017: 1689-1695. |
[44] | XU F , XIA T , CAO H ,et al. Detecting popular temporal modes in population-scale unlabelled trajectory data[J]. Proceedings of the ACM on IMWUT, 2018,2(1):46. |
[45] | CHENG C , YANG H . Where you like to go next:successive point-of-interest recommendation[C]// International Joint Conference on Artificial Intelligence.[S.l.:s.n. ], 2013: 2605-2611. |
[46] | MATHEW W , RAPOSO R , MARTINS B . Predicting future locations with hidden Markov models[C]// ACM Conference on Ubiquitous Computing. New York:ACM Press, 2012: 911-918. |
[47] | DU N , DAI H , TRIVEDI R ,et al. Recurrent marked temporal point processes:embedding event history to vector[C]// ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York:ACM Press, 2016: 1555-1564. |
[48] | ZHANG C , ZHANG K , YUAN Q ,et al. GMove:group-level mobility modeling using geo-tagged social media[C]// ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York:ACM Press, 2016: 1305-1314. |
[49] | DU H , YU Z , YI F ,et al. Recognition of group mobility level and group structure with mobile devices[J]. IEEE Transactions on Mobile Computing, 2018,17(4): 884-897. |
[50] | YU Z , YI F , LV Q ,et al. Identifying on-site users for social events:mobility,content,and social relationship[J]. IEEE Transactions on Mobile Computing, 2018,17(9): 2055-2068. |
[51] | DONG C , CHEN C L , HE K ,et al. Image super-resolution using deep convolutional networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2016,38(2): 295-307. |
[52] | VHADURI S , POELLABAUER C . Hierarchical cooperative discovery of personal places from location traces[J]. IEEE Transactions on Mobile Computing, 2018,17(8): 1865-1878. |
[53] | ZHANG J , ZHENG Y , QI D . Deep spatio-temporal residual networks for citywide crowd flows prediction[C]// Thirty-First AAAI Conference on Artificial Intelligence. Palo Alto:AAAI Press, 2017: 1655-1661. |
[54] | VANDAL T , KODRA E , GANGULY S ,et al. DeepSD:generating high resolution climate change projections through single image super-resolution[C]// ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York:ACM Press, 2017: 1663-1672. |
[55] | SWEENEY L . k-anonymity:a model for protecting privacy[J]. International Journal of Uncertainty,Fuzziness and Knowledge-Based Systems, 2002,10(5): 557-570. |
[56] | DWORK C , . Differential privacy:a survey of results[C]// International Conference on Theory and Applications of Models of Computation. Berlin:Springer, 2008: 1-19. |
[57] | MACHANAVAJJHALA A , GEHRKE J , KIFER D ,et al. l-diversity:privacy beyond k-anonymity[C]// 22nd International Conference on Data Engineering. Piscataway:IEEE Press, 2006:24. |
[58] | LI N , LI T , VENKATASUBRAMANIAN S . t-closeness:privacy beyond k-anonymity and l-diversity[C]// 2007 IEEE 23rd International Conference on Data Engineering. Piscataway:IEEE Press, 2007: 106-115. |
[59] | RASTOGI V , NATH S . Differentially private aggregation of distributed time-series with transformation and encryption[C]// Proceedings of the 2010 ACM SIGMOD International Conference on Management of Data. New York:ACM Press, 2010: 735-746. |
[60] | GRAMAGLIA M , FIORE M . Hiding mobile traffic fingerprints with glove[C]// Proceedings of the 11th ACM Conference on Emerging Networking Experiments and Technologies. New York:ACM Press, 2015:26. |
[61] | XU F , TU Z , HUANG H ,et al. No more than what I post:preventing linkage attacks on check-in services[C]// The World Wide Web Conference. New York:ACM Press, 2019: 3405-3412. |
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