{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,28]],"date-time":"2026-02-28T05:19:31Z","timestamp":1772255971377,"version":"3.50.1"},"reference-count":71,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2019,11,12]],"date-time":"2019-11-12T00:00:00Z","timestamp":1573516800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2019,11,12]],"date-time":"2019-11-12T00:00:00Z","timestamp":1573516800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100001665","name":"Agence Nationale de la Recherche","doi-asserted-by":"publisher","award":["ANR-13-TDMO-07"],"award-info":[{"award-number":["ANR-13-TDMO-07"]}],"id":[{"id":"10.13039\/501100001665","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Data Min Knowl Disc"],"published-print":{"date-parts":[[2020,1]]},"DOI":"10.1007\/s10618-019-00662-y","type":"journal-article","created":{"date-parts":[[2019,11,12]],"date-time":"2019-11-12T06:03:03Z","timestamp":1573538583000},"page":"201-230","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Deep multi-task learning for individuals origin\u2013destination matrices estimation from census data"],"prefix":"10.1007","volume":"34","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1233-9808","authenticated-orcid":false,"given":"Mehdi","family":"Katranji","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sami","family":"Kraiem","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Laurent","family":"Moalic","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guilhem","family":"Sanmarty","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ghazaleh","family":"Khodabandelou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Alexandre","family":"Caminada","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fouad","family":"Hadj Selem","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2019,11,12]]},"reference":[{"issue":"11","key":"662_CR1","doi-asserted-by":"publisher","first-page":"1949","DOI":"10.1109\/TMM.2015.2477680","volume":"17","author":"AH Abdulnabi","year":"2015","unstructured":"Abdulnabi AH, Wang G, Lu J, Jia K (2015) Multi-task CNN model for attribute prediction multi-task cnn model for attribute prediction. IEEE Trans Multimed 17(11):1949\u20131959","journal-title":"IEEE Trans Multimed"},{"issue":"2","key":"662_CR2","doi-asserted-by":"publisher","first-page":"192","DOI":"10.1016\/0885-064X(90)90006-Y","volume":"6","author":"YS Abu-Mostafa","year":"1990","unstructured":"Abu-Mostafa YS (1990) Learning from hints in neural networks. J Complex 6(2):192\u2013198","journal-title":"J Complex"},{"key":"662_CR3","unstructured":"ADISP (2018) Archives de donn\u00e9es issues de la statistique publique. https:\/\/www.cmh.ens.fr\/ADISP"},{"key":"662_CR4","doi-asserted-by":"crossref","unstructured":"Ahmed A, Aly M, Das A, Smola AJ, Anastasakos T (2012) Web-scale multi-task feature selection for behavioral targeting. In: Proceedings of the 21st ACM international conference on Information and knowledge management, pp 1737\u20131741","DOI":"10.1145\/2396761.2398508"},{"key":"662_CR5","unstructured":"Alamgir M, Grosse-Wentrup M, Altun Y (2010) Multitask learning for brain-computer interfaces. In: Proceedings of the thirteenth international conference on artificial intelligence and statistics, pp 17\u201324"},{"key":"662_CR6","unstructured":"Arai A, Shibasaki R (2013) Estimation of human mobility patterns and attributes analyzing anonymized mobile phone CDR: developing real-time census from crowds of greater dhaka. In: Agile PhD school"},{"key":"662_CR7","doi-asserted-by":"crossref","unstructured":"Arai A, Witayangkurn A, Kanasugi H, Horanont T, Shao X, Shibasaki R (2014) Understanding user attributes from calling behavior: exploring call detail records through field observations. In: Proceedings of the 12th international conference on advances in mobile computing and multimedia, pp 95\u2013104","DOI":"10.1145\/2684103.2684107"},{"key":"662_CR8","doi-asserted-by":"crossref","unstructured":"Bachir D, Gauthier V, El\u00a0Yacoubi M, Khodabandelou G (2017) Using mobile phone data analysis for the estimation of daily urban dynamics. In: 2017 IEEE 20th international conference on intelligent transportation systems (ITSC), pp 626\u2013632","DOI":"10.1109\/ITSC.2017.8317956"},{"key":"662_CR9","doi-asserted-by":"publisher","first-page":"569","DOI":"10.1007\/978-3-030-10997-4_35","volume-title":"Machine Learning and Knowledge Discovery in Databases","author":"Danya Bachir","year":"2019","unstructured":"Bachir D, Khodabandelou G, Gauthier V, El\u00a0Yacoubi M, Vachon E (2018) Combining bayesian inference and clustering for transport mode detection from sparse and noisy geolocation data. In: Joint European conference on machine learning and knowledge discovery in databases, pp 569\u2013584"},{"key":"662_CR10","doi-asserted-by":"publisher","first-page":"254","DOI":"10.1016\/j.trc.2019.02.013","volume":"101","author":"D Bachir","year":"2019","unstructured":"Bachir D, Khodabandelou G, Gauthier V, El Yacoubi M, Puchinger J (2019) Inferring dynamic origin\u2013destination flows by transport mode using mobile phone data. Trans Res Part C Emerg Technol 101:254\u2013275","journal-title":"Trans Res Part C Emerg Technol"},{"key":"662_CR11","doi-asserted-by":"publisher","first-page":"149","DOI":"10.1613\/jair.731","volume":"12","author":"J Baxter","year":"2000","unstructured":"Baxter J (2000) A model of inductive bias learning. J Artif Intell Res 12:149\u2013198","journal-title":"J Artif Intell Res"},{"issue":"1","key":"662_CR12","doi-asserted-by":"publisher","first-page":"41","DOI":"10.1023\/A:1007379606734","volume":"28","author":"R Caruana","year":"1997","unstructured":"Caruana R (1997) Multitask learning. Mach Learn 28(1):41\u201375","journal-title":"Mach Learn"},{"issue":"3","key":"662_CR13","doi-asserted-by":"publisher","first-page":"956","DOI":"10.1109\/TII.2016.2604758","volume":"13","author":"M Ceci","year":"2017","unstructured":"Ceci M, Corizzo R, Fumarola F, Malerba D, Rashkovska A (2017) Predictive modeling of pv energy production: how to set up the learning task for a better prediction? IEEE Trans Ind Inform 13(3):956\u2013966","journal-title":"IEEE Trans Ind Inform"},{"key":"662_CR14","unstructured":"CEREMA (2012) Urban mobility in france. Main lessons learnt from the years 2000\u20132010. https:\/\/www.cerema.fr\/fr\/centre-ressources\/boutique\/urban-mobility-france-main-lessons-learnt-years-2000-2010"},{"key":"662_CR15","doi-asserted-by":"crossref","unstructured":"Cirstea, R-G, Micu D-V, Muresan G-M, Guo C, Yang B (2018) Correlated time series forecasting using multi-task deep neural networks. In: Proceedings of the 27th acm international conference on information and knowledge management, pp 1527\u20131530","DOI":"10.1145\/3269206.3269310"},{"key":"662_CR16","doi-asserted-by":"crossref","unstructured":"Collobert R, Weston J (2008) A unified architecture for natural language processing: Deep neural networks with multitask learning. In: Proceedings of the 25th international conference on machine learning, pp 160\u2013167","DOI":"10.1145\/1390156.1390177"},{"issue":"7","key":"662_CR17","doi-asserted-by":"publisher","first-page":"1265","DOI":"10.1162\/NECO_a_00848","volume":"28","author":"M-A C\u00f4t\u00e9","year":"2016","unstructured":"C\u00f4t\u00e9 M-A, Larochelle H (2016) An infinite restricted boltzmann machine. Neural Comput 28(7):1265\u20131288","journal-title":"Neural Comput"},{"key":"662_CR18","doi-asserted-by":"crossref","unstructured":"Ding S, Jia W, Su C, Zhang L, Shi Z (2008) Neural network research progress and applications in forecast. In: International symposium on neural networks, pp 783\u2013793","DOI":"10.1007\/978-3-540-87734-9_89"},{"key":"662_CR19","doi-asserted-by":"publisher","first-page":"207","DOI":"10.1007\/978-3-642-01818-3_25","volume-title":"Advances in Artificial Intelligence","author":"Lisa Gaudette","year":"2009","unstructured":"Gaudette L, Japkowicz N (2009) Evaluation methods for ordinal classification. In: Canadian conference on artificial intelligence, pp 207\u2013210"},{"key":"662_CR20","unstructured":"Ghosn J, Bengio Y (1997) Multi-task learning for stock selection. In: Advances in neural information processing systems, pp 946\u2013952"},{"issue":"5","key":"662_CR21","doi-asserted-by":"publisher","first-page":"695","DOI":"10.1007\/s00778-011-0244-8","volume":"20","author":"F Giannotti","year":"2011","unstructured":"Giannotti F, Nanni M, Pedreschi D, Pinelli F, Renso C, Rinzivillo S, Trasarti R (2011) Unveiling the complexity of human mobility by querying and mining massive trajectory data. VLDB J Int J Very Large Data Bases 20(5):695\u2013719","journal-title":"VLDB J Int J Very Large Data Bases"},{"key":"662_CR22","unstructured":"Glorot X, Bengio Y (2010) Understanding the difficulty of training deep feedforward neural networks. In: Proceedings of the thirteenth international conference on artificial intelligence and statistics, pp 249\u2013256"},{"key":"662_CR23","unstructured":"Glorot X, Bordes A, Bengio Y (2011) Deep sparse rectifier neural networks. In: Proceedings of the fourteenth international conference on artificial intelligence and statistics, pp 315\u2013323"},{"issue":"7196","key":"662_CR24","doi-asserted-by":"publisher","first-page":"779","DOI":"10.1038\/nature06958","volume":"453","author":"MC Gonzalez","year":"2008","unstructured":"Gonzalez MC, Hidalgo CA, Barabasi A-L (2008) Understanding individual human mobility patterns. Nature 453(7196):779","journal-title":"Nature"},{"key":"662_CR25","unstructured":"GraphHopper (2018) Graphhopper directions api with route optimization. https:\/\/www.graphhopper.com"},{"key":"662_CR26","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2015) Delving deep into rectifiers: Surpassing human-level performance on imagenet classification. In: Proceedings of the IEEE international conference on computer vision, pp 1026\u20131034","DOI":"10.1109\/ICCV.2015.123"},{"issue":"5786","key":"662_CR27","doi-asserted-by":"publisher","first-page":"504","DOI":"10.1126\/science.1127647","volume":"313","author":"GE Hinton","year":"2006","unstructured":"Hinton GE, Salakhutdinov RR (2006) Reducing the dimensionality of data with neural networks. Science 313(5786):504\u2013507","journal-title":"Science"},{"key":"662_CR28","unstructured":"Hinton GE, Srivastava N, Krizhevsky A, Sutskever I, Salakhutdinov RR (2012) Improving neural networks by preventing co-adaptation of feature detectors. arXiv preprint arXiv:1207.0580"},{"key":"662_CR29","unstructured":"Hopfield JJ (1987) Neural networks and physical systems with emergent collective computational abilities. In: Spin glass theory and beyond: an introduction to the replica method and its applications. World Scientific, pp 411\u2013415"},{"issue":"2","key":"662_CR30","doi-asserted-by":"publisher","first-page":"251","DOI":"10.1016\/0893-6080(91)90009-T","volume":"4","author":"K Hornik","year":"1991","unstructured":"Hornik K (1991) Approximation capabilities of multilayer feedforward networks. Neural Netw 4(2):251\u2013257","journal-title":"Neural Netw"},{"key":"662_CR31","doi-asserted-by":"crossref","unstructured":"Hu Q, Wu Z, Richmond K, Yamagishi J, Stylianou Y, Maia R (2015) Fusion of multiple parameterisations for dnn-based sinusoidal speech synthesis with multi-task learning. In: Sixteenth annual conference of the international speech communication association","DOI":"10.21437\/Interspeech.2015-265"},{"key":"662_CR32","unstructured":"INSEE (2018) National institute of statistics and economic studies. https:\/\/www.insee.fr"},{"key":"662_CR33","doi-asserted-by":"crossref","unstructured":"Katranji M, Thuillier E, Kraiem S, Moalic L, Selem FH (2016) Mobility data disaggregation: A transfer learning approach. In: 2016 IEEE 19th international conference on intelligent transportation systems (ITSC), pp 1672\u20131677","DOI":"10.1109\/ITSC.2016.7795783"},{"key":"662_CR34","unstructured":"Katranji M, Moalic L, Sanmarty G, Kraiem S, Caminada A, Hadj\u00a0Selem F (2018) Mixed-variate restricted boltzmann machines for the inference of origin\u2013destination matrices. In: TRB (transportation research board) annual meeting (2018)"},{"key":"662_CR35","doi-asserted-by":"crossref","unstructured":"Katranji M, Sanmarty G, Moalic L, Kraiem S, Caminada A, Selem FH (2018) Rnn encoder-decoder for the inference of regular human mobility patterns. In: 2018 international joint conference on neural networks (IJCNN), pp 1\u20139","DOI":"10.1109\/IJCNN.2018.8489639"},{"key":"662_CR36","unstructured":"Kendall A, Gal Y, Cipolla R (2018) Multi-task learning using uncertainty to weigh losses for scene geometry and semantics. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 7482\u20137491"},{"key":"662_CR37","doi-asserted-by":"crossref","unstructured":"Khodabandelou G, Gauthier V, El-Yacoubi M, Fiore M (2016) Population estimation from mobile network traffic metadata. In: 2016 IEEE 17th international symposium on a world of wireless, mobile and multimedia networks (WOWMOM), pp 1\u20139","DOI":"10.1109\/WoWMoM.2016.7523554"},{"key":"662_CR38","doi-asserted-by":"publisher","first-page":"2034","DOI":"10.1109\/TMC.2018.2871156","volume":"18","author":"G Khodabandelou","year":"2018","unstructured":"Khodabandelou G, Gauthier V, Fiore M, El Yacoubi MA (2018) Estimation of static and dynamic urban populations with mobile network metadata. IEEE Trans Mob Comput 18:2034\u20132047","journal-title":"IEEE Trans Mob Comput"},{"key":"662_CR39","unstructured":"Kingma DP, Welling M (2013) Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114"},{"key":"662_CR40","unstructured":"Kingma D, Ba J (2014) Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980"},{"issue":"8","key":"662_CR41","doi-asserted-by":"publisher","first-page":"e105184","DOI":"10.1371\/journal.pone.0105184","volume":"9","author":"M Lenormand","year":"2014","unstructured":"Lenormand M, Picornell M, Cant\u00fa-Ros OG, Tugores A, Louail T, Herranz R, Ramasco JJ (2014) Cross-checking different sources of mobility information. PLoS ONE 9(8):e105184","journal-title":"PLoS ONE"},{"key":"662_CR42","doi-asserted-by":"publisher","first-page":"10075","DOI":"10.1038\/srep10075","volume":"5","author":"M Lenormand","year":"2015","unstructured":"Lenormand M, Louail T, Cant\u00fa-Ros OG, Picornell M, Herranz R, Arias JM, Ramasco JJ (2015) Influence of sociodemographic characteristics on human mobility. Sci Rep 5:10075","journal-title":"Sci Rep"},{"key":"662_CR43","unstructured":"Long M, Wang J (2015) Learning multiple tasks with deep relationship networks. arXiv preprint arXiv:1506.02117"},{"key":"662_CR44","doi-asserted-by":"publisher","first-page":"5561","DOI":"10.1038\/srep05561","volume":"4","author":"R Louf","year":"2014","unstructured":"Louf R, Barthelemy M (2014) How congestion shapes cities: from mobility patterns to scaling. Sci Rep 4:5561","journal-title":"Sci Rep"},{"issue":"7","key":"662_CR45","doi-asserted-by":"publisher","first-page":"e102007","DOI":"10.1371\/journal.pone.0102007","volume":"9","author":"R Louf","year":"2014","unstructured":"Louf R, Roth C, Barthelemy M (2014) Scaling in transportation networks. PLoS ONE 9(7):e102007","journal-title":"PLoS ONE"},{"key":"662_CR46","unstructured":"Navitia (2018) The open api for building cool stuff with transport data. https:\/\/www.navitia.io"},{"key":"662_CR47","unstructured":"Orange (2018). Flux vision. https:\/\/www.orange-business.com\/en\/products\/flux-vision"},{"issue":"10","key":"662_CR48","doi-asserted-by":"publisher","first-page":"1345","DOI":"10.1109\/TKDE.2009.191","volume":"22","author":"SJ Pan","year":"2010","unstructured":"Pan SJ, Yang Q (2010) A survey on transfer learning. IEEE Trans Knowl Data Eng 22(10):1345\u20131359","journal-title":"IEEE Trans Knowl Data Eng"},{"issue":"Oct","key":"662_CR49","first-page":"2825","volume":"12","author":"F Pedregosa","year":"2011","unstructured":"Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O (2011) Scikit-learn: Machine learning in python. J Mach Learn Res 12(Oct):2825\u20132830","journal-title":"J Mach Learn Res"},{"key":"662_CR50","first-page":"53","volume-title":"Neural networks: tricks of the trade","author":"L Prechelt","year":"1998","unstructured":"Prechelt L (1998) Early stopping\u2014but when? In: Montavon G, Orr GB, M\u00fcller K-R (eds) Neural networks: tricks of the trade. Springer, Berlin, pp 53\u201367"},{"issue":"12","key":"662_CR51","doi-asserted-by":"publisher","first-page":"i208","DOI":"10.1093\/bioinformatics\/btq191","volume":"26","author":"K Puniyani","year":"2010","unstructured":"Puniyani K, Kim S, Xing EP (2010) Multi-population GWA mapping via multi-task regularized regression. Bioinformatics 26(12):i208\u2013i216","journal-title":"Bioinformatics"},{"key":"662_CR52","unstructured":"Ruder S (2017) An overview of multi-task learning in deep neural networks. arXiv preprint arXiv:1706.05098"},{"issue":"3","key":"662_CR53","first-page":"1","volume":"5","author":"DE Rumelhart","year":"1988","unstructured":"Rumelhart DE, Hinton GE, Williams RJ et al (1988) Learning representations by back-propagating errors. Cogn Model 5(3):1","journal-title":"Cogn Model"},{"key":"662_CR54","doi-asserted-by":"publisher","first-page":"745","DOI":"10.1108\/9781781902882-041","volume":"41","author":"Z Smoreda","year":"2013","unstructured":"Smoreda Z, Olteanu-Raimond A-M, Couronn\u00e9 T et al (2013) Spatiotemporal data from mobile phones for personal mobility assessment. Trans Surv Methods Best Pract Decis Mak 41:745\u2013767","journal-title":"Trans Surv Methods Best Pract Decis Mak"},{"key":"662_CR55","unstructured":"Song X, Kanasugi H, Shibasaki R (2016) Deeptransport: prediction and simulation of human mobility and transportation mode at a citywide level. In: IJCAI, vol 16, pp 2618\u20132624"},{"key":"662_CR56","doi-asserted-by":"crossref","unstructured":"Toqu\u00e9 F, C\u00f4me E, El\u00a0Mahrsi MK, Oukhellou L (2016) Forecasting dynamic public transport origin\u2013destination matrices with long-short term memory recurrent neural networks. In: 2016 IEEE 19th international conference on intelligent transportation systems (ITSC), pp 1071\u20131076","DOI":"10.1109\/ITSC.2016.7795689"},{"key":"662_CR57","unstructured":"Tran T, Phung D, Venkatesh S (2011) Mixed-variate restricted boltzmann machines. In: Asian conference on machine learning, pp 213\u2013229"},{"issue":"11","key":"662_CR58","doi-asserted-by":"publisher","first-page":"1849","DOI":"10.1080\/13658816.2011.561209","volume":"25","author":"M Wachowicz","year":"2011","unstructured":"Wachowicz M, Ong R, Renso C, Nanni M (2011) Finding moving flock patterns among pedestrians through collective coherence. Int J Geogr Inf Sci 25(11):1849\u20131864","journal-title":"Int J Geogr Inf Sci"},{"key":"662_CR59","unstructured":"Willumsen LG (1978) Estimation of an O\u2013D matrix from traffic counts? a review. Institute of Transport Studies, University of Leeds. http:\/\/eprints.whiterose.ac.uk\/2415\/"},{"key":"662_CR60","doi-asserted-by":"crossref","unstructured":"Wu Z, Valentini-Botinhao C, Watts O, King S (2015) Deep neural networks employing multi-task learning and stacked bottleneck features for speech synthesis. In: 2015 IEEE international conference on acoustics, speech and signal processing (ICASSP), pp 4460\u20134464","DOI":"10.1109\/ICASSP.2015.7178814"},{"issue":"Jan","key":"662_CR61","first-page":"35","volume":"8","author":"Y Xue","year":"2007","unstructured":"Xue Y, Liao X, Carin L, Krishnapuram B (2007) Multi-task learning for classification with dirichlet process priors. J Mach Learn Res 8(Jan):35\u201363","journal-title":"J Mach Learn Res"},{"key":"662_CR62","unstructured":"Yang Y, Hospedales T (2016) Deep multi-task representation learning: a tensor factorisation approach. arXiv preprint arXiv:1605.06391"},{"key":"662_CR63","unstructured":"Yim J, Jung H, Yoo B, Choi C, Park D, Kim J (2015) Rotating your face using multi-task deep neural network. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 676\u2013684"},{"key":"662_CR64","unstructured":"Zhang Y, Yeung D-Y (2012) A convex formulation for learning task relationships in multi-task learning. arXiv preprint arXiv:1203.3536"},{"issue":"3","key":"662_CR65","first-page":"12","volume":"8","author":"Y Zhang","year":"2014","unstructured":"Zhang Y, Yeung D-Y (2014) A regularization approach to learning task relationships in multitask learning. ACM Trans Knowl Discov Data (TKDD) 8(3):12","journal-title":"ACM Trans Knowl Discov Data (TKDD)"},{"key":"662_CR66","unstructured":"Zhang Y, Yang Q (2017) A survey on multi-task learning. arXiv preprint arXiv:1707.08114"},{"issue":"12","key":"662_CR67","doi-asserted-by":"publisher","first-page":"i97","DOI":"10.1093\/bioinformatics\/btq181","volume":"26","author":"K Zhang","year":"2010","unstructured":"Zhang K, Gray JW, Parvin B (2010) Sparse multitask regression for identifying common mechanism of response to therapeutic targets. Bioinformatics 26(12):i97\u2013i105","journal-title":"Bioinformatics"},{"key":"662_CR68","doi-asserted-by":"crossref","unstructured":"Zhang D, Huang J, Li Y, Zhang F, Xu C, He T (2014) Exploring human mobility with multi-source data at extremely large metropolitan scales. In: Proceedings of the 20th annual international conference on mobile computing and networking, pp 201\u2013212","DOI":"10.1145\/2639108.2639116"},{"key":"662_CR69","doi-asserted-by":"crossref","unstructured":"Zhang D, Zhao J, Zhang F, He T (2015) comobile: real-time human mobility modeling at urban scale using multi-view learning. In: Proceedings of the 23rd sigspatial international conference on advances in geographic information systems, p 40","DOI":"10.1145\/2820783.2820821"},{"issue":"3","key":"662_CR70","first-page":"38","volume":"5","author":"Y Zheng","year":"2014","unstructured":"Zheng Y, Capra L, Wolfson O, Yang H (2014) Urban computing: concepts, methodologies, and applications. ACM Trans Intell Syst Technol (TIST) 5(3):38","journal-title":"ACM Trans Intell Syst Technol (TIST)"},{"key":"662_CR71","unstructured":"Zhou J, Chen J, Ye J (2012) Multi-task learning: theory, algorithms, and applications. SDM tutorials"}],"container-title":["Data Mining and Knowledge Discovery"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10618-019-00662-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s10618-019-00662-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10618-019-00662-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,10,4]],"date-time":"2022-10-04T10:42:03Z","timestamp":1664880123000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s10618-019-00662-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,11,12]]},"references-count":71,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2020,1]]}},"alternative-id":["662"],"URL":"https:\/\/doi.org\/10.1007\/s10618-019-00662-y","relation":{},"ISSN":["1384-5810","1573-756X"],"issn-type":[{"value":"1384-5810","type":"print"},{"value":"1573-756X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,11,12]]},"assertion":[{"value":"18 June 2018","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 October 2019","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 November 2019","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}