, Word 2: travels during the week-end

, diffuse travel habits from 8 a.m to 4 p.m Mondays to Fridays, vol.3

, Word 4: travels at 7a.m on weekdays

, Word 6: diffuse habits from 9 a.m to 5 p.m with highest probability at 1 p.m Mondays to Saturdays

, Cluster 1: diffuse habits from 9 a.m to 5 p.m with highest probability at 1 p.m Mondays to Saturdays

, Cluster 2: travels at 6 or 7 a.m and at 4 or 5 p.m during the week

, Cluster 5: travels at 7 or 8 a.m diffuse habits during the afternoon

, Cluster 9: travels during the week-end. 10. Cluster 10: travels at

A. Bibliography-martin-abadi, I. Chu, B. Goodfellow, I. Mcmahan, K. Mironov et al., Deep learning with differential privacy, Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security, pp.308-318, 2016.

A. Milad-leyli-abadi, L. Samé, N. Oukhellou, P. Cheifetz, C. Mandel et al., Predictive classification of water consumption time series using non-homogeneous markov models, Data Science and Advanced Analytics (DSAA), 2017 IEEE International Conference on, pp.323-331, 2017.

F. Abramovich and T. Lahav, Sparse additive regression on a regular lattice, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol.77, issue.2, pp.443-459, 2015.

H. Ahvenniemi, A. Huovila, I. Pinto-seppä, and M. Airaksinen, What are the differences between sustainable and smart cities?, Cities, vol.60, pp.234-245, 2017.

W. Alonso, Toward a general theory of land rent, 1964.

P. Alquier and B. Guedj, An oracle inequality for quasiBayesian non-negative matrix factorization, Mathematical Methods of Statistics, vol.26, issue.1, pp.55-67, 2017.
DOI : 10.3103/s1066530717010045
URL : https://hal.archives-ouvertes.fr/hal-01251878

P. Alquier and X. Li, Prediction of quantiles by statistical learning and application to gdp forecasting, International Conference on Discovery Science, pp.22-36, 2012.
URL : https://hal.archives-ouvertes.fr/hal-00777482

. Naomi-s-altman, An introduction to kernel and nearest-neighbor nonparametric regression, The American Statistician, vol.46, issue.3, pp.175-185, 1992.

P. Simon, A. Anderson, and . Palma, Parking in the city, Papers in Regional Science, vol.86, issue.4, pp.621-632, 2007.

S. Arlot and R. Genuer, Analysis of purely random forests bias, 2014.
URL : https://hal.archives-ouvertes.fr/hal-01023596

S. Arlot and P. Massart, Data-driven calibration of penalties for least-squares regression, Journal of Machine Learning Research, vol.10, pp.245-279, 2009.
URL : https://hal.archives-ouvertes.fr/inria-00287631

R. Arnott, A. D. Palma, and R. Lindsey, A structural model of peak-period congestion: A traffic bottleneck with elastic demand, The American Economic Review, pp.161-179, 1993.

F. Atefeh and W. Khreich, A survey of techniques for event detection in twitter, Computational Intelligence, vol.31, issue.1, pp.132-164, 2015.

M. Batty, Big data, smart cities and city planning, Dialogues in Human Geography, vol.3, issue.3, pp.274-279, 2013.
DOI : 10.1177/2043820613513390
URL : https://journals.sagepub.com/doi/pdf/10.1177/2043820613513390

J. Baudry, C. Maugis, and B. Michel, Slope heuristics: overview and implementation, Statistics and Computing, vol.22, issue.2, pp.455-470, 2012.
DOI : 10.1007/s11222-011-9236-1
URL : https://hal.archives-ouvertes.fr/hal-00666838

T. Benaglia, D. Chauveau, D. Hunter, and D. Young, mixtools: An R package for analyzing finite mixture models, Journal of Statistical Software, vol.32, issue.6, pp.1-29, 2009.
DOI : 10.18637/jss.v032.i06
URL : https://hal.archives-ouvertes.fr/hal-00384896

J. Benedetti, On the nonparametric estimation of regression functions, Journal of the Royal Statistical Society. Series B (Methodological, pp.248-253, 1977.

G. Biau and E. Scornet, A random forest guided tour, Test, vol.25, issue.2, pp.197-227, 2016.
DOI : 10.1007/s11749-016-0481-7
URL : https://hal.archives-ouvertes.fr/hal-01221748

J. Bibby, J. Kent, and K. Mardia, Multivariate analysis, 1979.

C. Biernacki, G. Celeux, and G. Govaert, An improvement of the NEC criterion for assessing the number of clusters in a mixture model, Pattern Recognition Letters, vol.20, issue.3, pp.267-272, 1999.

M. Christopher and . Bishop, Pattern recognition and machine learning (information science and statistics), 2007.

D. Black and V. Henderson, A theory of urban growth, Journal of Political Economy, vol.107, issue.2, 1999.

M. David, . Blei, Y. Andrew, and M. Ng, Latent Dirichlet allocation, Journal of Machine Learning Research, vol.3, pp.993-1022, 2003.

C. Bothorel and N. Lathia, Romain Picot-Clemente, and Anastasios Noulas. Location recommendation with social media data, Social Information Access, pp.624-653, 2018.

C. Bouveyron and C. Brunet-saumard, Model-based clustering of high-dimensional data: a review, Computational Statistics and Data Analysis, vol.71, pp.52-78, 2014.
URL : https://hal.archives-ouvertes.fr/hal-00750909

C. Bouveyron, E. Côme, and J. Jacques, The discriminative functional mixture model for a comparative analysis of bike sharing systems, The Annals of Applied Statistics, vol.9, issue.4, pp.1726-1760, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01024186

S. Boyd, N. Parikh, E. Chu, B. Peleato, and J. Eckstein, Distributed optimization and statistical learning via the alternating direction method of multipliers. Foundations and Trends in Machine Learning, vol.3, pp.1-122, 2011.

L. Breiman, Random forests. Machine learning, vol.45, pp.5-32, 2001.

A. Sarah-briand, E. Come, M. Trepanier, and L. Oukhellou, Smart card clustering to extract typical temporal passenger habits in transit network. two case studies: Rennes in france and gatineau in canada, 3rd International Workshop and Symposium, 2017.

L. Carel and P. Alquier, Non-negative matrix factorization as a pre-processing tool for travelers temporal profiles clustering, Proceedings of the 25th European Symposium on Artificial Neural Networks, pp.417-422, 2017.

L. Carel and P. Alquier, Prévision de la fréquentation d'un réseau de transport à l'aide de modèles additifs généralisés, Proceedings of the 50th "Journées des Statistiques, 2018.

P. Samuel-castro, D. Zhang, C. Chen, S. Li, and G. Pan, From taxi gps traces to social and community dynamics: A survey, ACM Computing Surveys (CSUR), vol.46, issue.2, p.17, 2013.

G. Celeux, S. Frühwirth-schnatter, and C. P. Robert, Handbook of Mixture Analysis, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01928103

G. Celeux, C. Maugis-rabusseau, and M. Sedki, Variable selection in model-based clustering and discriminant analysis with a regularization approach, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01053784

P. Badr-eddine-chérief-abdellatif and . Alquier, Consistency of variational bayes inference for estimation and model selection in mixtures, Electronic Journal of Statistics, vol.12, issue.2, pp.2995-3035, 2018.

E. Côme and L. Oukhellou, Model-based count series clustering for bike sharing system usage mining: a case study with the vélib' system of paris, ACM Transactions on Intelligent Systems and Technology (TIST), vol.5, issue.3, p.39, 2014.

M. Cornec, Constructing a conditional gdp fan chart with an application to french business survey data, OECD Journal: Journal of Business Cycle Measurement and Analysis, vol.2013, issue.2, pp.109-127, 2014.

R. Crescenzi and A. Rodriguez-pose, Infrastructure and regional growth in the european union, Papers in Regional Science, vol.3, issue.91, pp.487-513, 2012.

R. , P. Dameri, and A. Cocchia, Smart city and digital city: twenty years of terminology evolution, X Conference of the Italian Chapter, pp.1-8, 2013.

D. Eleonora, F. Andrea, and . Marcelloni, Detection of traffic congestion and incidents from gps trace analysis, Expert Systems with Applications, vol.73, pp.43-56, 2017.

A. Palma and S. Dantan, Big data et politiques publiques dans les transports, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01787512

M. André-de-palma, S. Kilani, and . Proost, Discomfort in mass transit and its implication for scheduling and pricing, Transportation Research Part B: Methodological, vol.71, pp.1-18, 2015.

R. André-de-palma, G. Lindsey, and . Monchambert, The economics of crowding in public transport, Journal of Urban Economics, vol.101, pp.106-122, 2017.

R. Deswarte, Régression linéaire et apprentissage: contributions aux méthodes de régularisation et d'agrégation, 2018.

L. Devroye, The uniform convergence of nearest neighbor regression function estimators and their application in optimization, IEEE Transactions on Information Theory, vol.24, issue.2, pp.142-151, 1978.

Y. Inderjit-s-dhillon, B. Guan, and . Kulis, Kernel k-means: spectral clustering and normalized cuts, Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining, pp.551-556, 2004.

R. D. Clemente, M. Luengo-oroz, M. Travizano, S. Xu, B. Vaitla et al., Sequences of purchases in credit card data reveal lifestyles in urban populations, Nature communications, vol.9, 2018.

C. Ding, X. He, and H. Simon, On the equivalence of nonnegative matrix factorization and spectral clustering, Proceedings of the 2005 SIAM International Conference on Data Mining, pp.606-610, 2005.

K. Do, P. Müller, and F. Tang, A bayesian mixture model for differential gene expression, Journal of the Royal Statistical Society: Series C (Applied Statistics), vol.54, issue.3, pp.627-644, 2005.

B. Du, C. Liu, W. Zhou, Z. Hou, and H. Xiong, Detecting pickpocket suspects from large-scale public transit records, IEEE Transactions on Knowledge and Data Engineering, 2018.

G. Duranton and D. Puga, The growth of cities, Handbook of economic growth, vol.2, pp.781-853, 2014.

G. Duranton, . Matthew, and . Turner, Urban growth and transportation, Review of Economic Studies, vol.79, issue.4, pp.1407-1440, 2012.

I. Angel-m-dzhambov, . Markevych, G. Boris, . Tilov, and . Dimitrova, Residential greenspace might modify the effect of road traffic noise exposure on general mental health in students, Urban Forestry & Urban Greening, vol.34, pp.233-239, 2018.

M. Mahrsi, E. Côme, J. Baro, and L. Oukhellou, Understanding passenger patterns in public transit through smart card and socioeconomic data: a case study in Rennes, France, ACM SIGKDD Workshop on Urban Computing, 2014.
URL : https://hal.archives-ouvertes.fr/hal-01053794

M. Mahrsi, E. Côme, J. Baro, and L. Oukhellou, Understanding passenger patterns in public transit through smart card and socioeconomic data, 2014.
URL : https://hal.archives-ouvertes.fr/hal-01053794

M. Mahrsi, E. Côme, L. Oukhellou, and M. Verleysen, Clustering smart card data for urban mobility analysis, IEEE Transactions on Intelligent Transportation Systems, vol.18, issue.3, pp.712-728, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01467221

C. Févotte, N. Bertin, and J. Durrieu, Nonnegative matrix factorization with the Itakura-Saito divergence: with application to music analysis, Neural computation, vol.21, issue.3, pp.793-830, 2009.

M. Filippone, F. Camastra, F. Masulli, and S. Rovetta, A survey of kernel and spectral methods for clustering, Pattern recognition, vol.41, issue.1, pp.176-190, 2008.

M. Fop and T. Murphy, Variable selection methods for model based clustering, 2017.

C. Fraley and A. E. Raftery, Model-based clustering, discriminant analysis, and density estimation, Journal of the American statistical Association, vol.97, issue.458, pp.611-631, 2002.

J. Friedman, T. Hastie, and R. Tibshirani, The elements of statistical learning, Springer series in statistics, vol.1, 2001.

K. Gadouche and N. Picard, L'accès aux données très détaillées pour la recherche scientifique, 2017.

N. Gendron-carrier, M. Gonzalez-navarro, S. Polloni, and M. Turner, Subways and urban air pollution, 2018.

R. Genuer, Variance reduction in purely random forests, Journal of Nonparametric Statistics, vol.24, issue.3, pp.543-562, 2012.
URL : https://hal.archives-ouvertes.fr/hal-01590513

R. Genuer, J. Poggi, and C. Tuleau, Random forests: some methodological insights, 2008.
URL : https://hal.archives-ouvertes.fr/inria-00340725

Z. Ghahramani and G. E. Hinton, The EM algorithm for mixtures of factor analyzers, 1996.

S. Ghosal, K. Jayanta, A. Ghosh, . Van-der, and . Vaart, Convergence rates of posterior distributions, Annals of Statistics, vol.28, issue.2, pp.500-531, 2000.

F. Edward, Y. Gonzalez, and . Zhang, Accelerating the Lee-Seung algorithm for non-negative matrix factorization, Dept. Comput. & Appl. Math, 2005.

B. Grün, Model-based clustering, Handbook of Mixture Analysis, pp.155-188, 2018.

M. Guarnieri and J. Balmes, Outdoor air pollution and asthma, The Lancet, vol.383, issue.9928, pp.1581-1592, 2014.

B. Guedj and P. Alquier, Pac-bayesian estimation and prediction in sparse additive models, Electronic Journal of Statistics, vol.7, pp.264-291, 2013.
URL : https://hal.archives-ouvertes.fr/hal-00722969

R. Hamon and P. Borgnat, Cédric Févotte, Patrick Flandrin, and Céline Robardet. Factorisation de réseaux temporels: étude des rythmes hebdomadaires du système Vélo'v, Colloque GRETSI 2015, 2015.

M. Hardt, E. Price, and N. Srebro, Equality of opportunity in supervised learning, Advances in neural information processing systems, pp.3315-3323, 2016.

C. Harrison, B. Eckman, R. Hamilton, P. Hartswick, J. Kalagnanam et al., Foundations for smarter cities, IBM Journal of Research and Development, vol.54, issue.4, pp.1-16, 2010.

T. Hastie and R. Tibshirani, Generalized additive models, Statistical Science, vol.1, issue.3, pp.297-318, 1986.

E. Arthur, R. Hoerl, and . Kennard, Ridge regression: Biased estimation for nonorthogonal problems, Technometrics, vol.12, issue.1, pp.55-67, 1970.

J. Earl-b-hunt, P. Marin, and . Stone, Experiments in induction, 1966.

R. Ihaka and R. Gentleman, R: a language for data analysis and graphics, Journal of Computational and Graphical Statistics, vol.5, pp.299-314, 1996.

Z. Ji, C. Zachary-c-lipton, and . Elkan, Differential privacy and machine learning: a survey and review, 2014.

S. Jiang, J. Ferreira, and M. C. Gonzalez, Activity-based human mobility patterns inferred from mobile phone data: A case study of singapore, IEEE Transactions on Big Data, vol.3, issue.2, pp.208-219, 2017.

. Stephen-c-johnson, Hierarchical clustering schemes, Psychometrika, vol.32, issue.3, pp.241-254, 1967.

J. Kim and H. Park, Sparse nonnegative matrix factorization for clustering, 2008.

Y. Koren, R. Bell, and C. Volinsky, Matrix factorization techniques for recommender systems, Computer, vol.42, issue.8, pp.30-37, 2009.

P. Laharotte, R. Billot, E. Come, L. Oukhellou, A. Nantes et al., Spatiotemporal analysis of bluetooth data: Application to a large urban network, IEEE Transactions on Intelligent Transportation Systems, vol.16, issue.3, pp.1439-1448, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01213936

N. Lartillot and H. Philippe, A bayesian mixture model for acrosssite heterogeneities in the amino-acid replacement process, Molecular biology and evolution, vol.21, issue.6, pp.1095-1109, 2004.
URL : https://hal.archives-ouvertes.fr/lirmm-00108585

L. Saskia, J. Cessie, and . Houwelingen, Ridge estimators in logistic regression, Applied statistics, pp.191-201, 1992.

D. Daniel, H. Lee, and . Seung, Learning the parts of objects by non-negative matrix factorization, Nature, vol.401, issue.6755, pp.788-791, 1999.

D. Daniel, H. Lee, and . Seung, Algorithms for non-negative matrix factorization, Advances in neural information processing systems, pp.556-562, 2001.

M. G. Jung-hoon-lee, M. Hancock, and . Hu, Towards an effective framework for building smart cities: Lessons from seoul and san francisco, Technological Forecasting & Social Change, vol.89, pp.80-99, 2014.

R. Lee and K. Sumiya, Measuring geographical regularities of crowd behaviors for twitter-based geo-social event detection, Proceedings of the 2nd ACM SIGSPATIAL international workshop on location based social networks, pp.1-10, 2010.

C. Lin, Projected Gradient Methods for Non-negative Matrix Factorization, Neural computation, vol.19, issue.10, pp.2756-2779, 2007.

T. Litman, Rail transit in america: a comprehensive evaluation of benefits, 2015.

T. Litman, Evaluating public transportation health benefits. Victoria Transport Policy Institute, 2016.

T. Louail, M. Lenormand, J. M. Arias, and . Ramasco, Crowdsourcing the robin hood effect in cities, Applied Network Science, vol.2, issue.1, p.11, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01535480

X. Luo, M. Zhou, Y. Xia, and Q. Zhu, An efficient nonnegative matrix-factorization-based approach to collaborative filtering for recommender systems, IEEE Transactions on Industrial Informatics, vol.10, issue.2, pp.1273-1284, 2014.

A. Mackiewicz and W. Ratajczak, Principal components analysis (pca), Computers and Geosciences, vol.19, pp.303-342, 1993.

J. Macqueen, Some methods for classification and analysis of multivariate observations, Proceedings of the fifth Berkeley symposium on mathematical statistics and probability, vol.1, pp.281-297, 1967.

W. Donald, R. Marquardt, and . Snee, Ridge regression in practice, The American Statistician, vol.29, issue.1, pp.3-20, 1975.

C. Maugis, G. Celeux, and M. Martin-magniette, Variable selection for clustering with gaussian mixture models, Biometrics, vol.65, issue.3, pp.701-709, 2009.
URL : https://hal.archives-ouvertes.fr/inria-00153057

C. Maugis, G. Celeux, and M. Martin-magniette, Variable selection in model-based clustering: a general variable role modeling, Computational Statistics & Data Analysis, vol.53, issue.11, pp.3872-3882, 2009.
URL : https://hal.archives-ouvertes.fr/inria-00342108

J. Geoffrey, D. Mclachlan, and . Peel, Finite mixture models, 2004.

J. Geoffrey, D. Mclachlan, R. W. Peel, and . Bean, Modelling highdimensional data by mixtures of factor analyzers, Computational Statistics & Data Analysis, vol.41, issue.3-4, pp.379-388, 2003.

. Paul-d-mcnicholas, Model-based clustering, Journal of Classification, vol.33, issue.3, pp.331-373, 2016.

. Paul-d-mcnicholas, Mixture model-based classification, 2016.

D. Paul, T. Mcnicholas, and . Murphy, Parsimonious gaussian mixture models, Statistics and Computing, vol.18, issue.3, pp.285-296, 2008.

M. Medvedovic, K. Y. Yeung, and R. E. Bumgarner, Bayesian mixture model based clustering of replicated microarray data, Bioinformatics, vol.20, issue.8, pp.1222-1232, 2004.

J. Mei, Y. D. Castro, Y. Goude, and G. Hébrail, Recovering multiple nonnegative time series from a few temporal aggregates, 34th International Conference on Machine Learning (ICML), 2017.
URL : https://hal.archives-ouvertes.fr/hal-01686437

A. Fateh-nassim-melzi and . Same, Mohamed Haykel Zayani, and Latifa Oukhellou. A dedicated mixture model for clustering smart meter data: identification and analysis of electricity consumption behaviors, Energies, vol.10, issue.10, p.1446, 2017.

. Benoit-meyronin, Marketing territorial: enjeux et pratiques. Vuibert, 2015.

A. Montanari and C. Viroli, Heteroscedastic factor mixture analysis, Statistical Modelling, vol.10, issue.4, pp.441-460, 2010.

C. Morency, M. Trepanier, and B. Agard, Measuring transit use variability with smart-card data, Transport Policy, vol.14, issue.3, pp.193-203, 2007.

K. Murphy, C. Gormley, and C. Viroli, Infinite mixtures of infinite factor analysers: nonparametric model-based clustering via latent gaussian models, 2017.

M. Paula, P. D. Murray, R. Mcnicholas, and . Browne, A mixture of common skew-t factor analysers, Stat, vol.3, issue.1, pp.68-82, 2014.

T. Nam, . Theresa, and . Pardo, Conceptualizing smart city with dimensions of technology, people, and institutions, Proceedings of the 12th annual international digital government research conference: digital government innovation in challenging times, pp.282-291, 2011.

Y. Andrew, . Ng, Y. Michael-i-jordan, and . Weiss, On spectral clustering: Analysis and an algorithm, Advances in neural information processing systems, pp.849-856, 2002.

, Forecasting vs regression, 2018.

J. William-paisley, D. M. Blei, and M. , Bayesian nonnegative matrix factorization with stochastic variational inference, 2014.

B. Pan, Y. Zheng, D. Wilkie, and C. Shahabi, Crowd sensing of traffic anomalies based on human mobility and social media, Proceedings of the 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, pp.344-353, 2013.

J. Pang, The Effect of Urban Transportation Systems on Employment Outcomes and Traffic Congestion, 2018.

F. Papon, J. Beauvais, S. Midenet, E. Come, N. Polombo et al., Evaluation of the bicycle as a feeder mode to regional train stations, Transportation research procedia, vol.25, pp.2721-2740, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01373935

K. Pearson and . Liii, on lines and planes of closest fit to systems of points in space. The London, Edinburgh, and Dublin Philosophical Magazine, Journal of Science, vol.2, issue.11, pp.559-572, 1901.

M. Pelletier, M. Trépanier, and C. Morency, Smart card data in public transit planning: a review, 2009.

C. Peng, X. Jin, K. Wong, M. Shi, and P. Liò, Collective human mobility pattern from taxi trips in urban area, PloS one, vol.7, issue.4, p.34487, 2012.

M. Poussevin, E. Tonnelier, N. Baskiotis, V. Guigue, and P. Gallinari, Mining ticketing logs for usage characterization with nonnegative matrix factorization, International Workshop on Modeling Social Media, pp.147-164, 2014.
URL : https://hal.archives-ouvertes.fr/hal-01356359

R. Sathya and A. Abraham, Comparison of supervised and unsupervised learning algorithms for pattern classification, International Journal of Advanced Research in Artificial Intelligence, vol.2, issue.2, pp.34-38, 2013.

E. Scornet, G. Biau, and J. Vert, Consistency of random forests, The Annals of Statistics, vol.43, issue.4, pp.1716-1741, 2015.
URL : https://hal.archives-ouvertes.fr/hal-00990008

L. Scrucca, M. Fop, T. B. Murphy, and A. E. Raftery, mclust 5: clustering, classification and density estimation using Gaussian finite mixture models. The R journal, vol.8, p.289, 2016.

A. F. George and . Seber, Multivariate observations, vol.252, 2009.

Z. Shokri, M. Selim, and . Ismail, K-means-type algorithms: A generalized convergence theorem and characterization of local optimality, IEEE Transactions, issue.1, pp.81-87, 1984.

F. Shahnaz, W. Michael, P. Berry, R. Pauca, and . Plemmons, Document clustering using nonnegative matrix factorization. Information Processing & Management, vol.42, pp.373-386, 2006.

H. Steinhaus, Sur la division des corp materiels en partie, Bull. Acad. Polon. Sci, vol.1, issue.804, p.801, 1956.

D. Steinley, . Michael, and . Brusco, Selection of variables in cluster analysis: an empirical comparison of eight procedures, Psychometrika, vol.73, issue.1, pp.125-144, 2008.

Y. Stella and J. Shi, Multiclass spectral clustering, p.313, 2003.

J. Charles and . Stone, Consistent nonparametric regression. The annals of statistics, pp.595-620, 1977.

W. Stute, Asymptotic normality of nearest neighbor regression function estimates, The Annals of Statistics, vol.12, issue.3, pp.917-926, 1984.

L. Dennis, C. Sun, and . Fevotte, Alternating direction method of multipliers for non-negative matrix factorization with the beta-divergence, Acoustics, Speech and Signal Processing (ICASSP), 2014 IEEE International Conference on, pp.6201-6205, 2014.

R. Tibshirani, G. Walther, and T. Hastie, Estimating the number of clusters in a data set via the gap statistic, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol.63, issue.2, pp.411-423, 2001.

E. Tonnelier, N. Baskiotis, V. Guigue, and P. Gallinari, Anomaly detection in smart card logs and distant evaluation with twitter: a robust framework, 2018.

F. Toqué, M. Khouadjia, E. Come, M. Trepanier, and L. Oukhellou, Short & long term forecasting of multimodal transport passenger flows with machine learning methods, Intelligent Transportation Systems (ITSC), 2017 IEEE 20th International Conference on, pp.560-566, 2017.

G. Trompiz and M. Mazzilli, Veolia to sell stake in transport firm to germany's rethmann. Reuters, vol.9, 2018.

. John-w-tukey, Exploratory data analysis, vol.2, 1977.

. Frank-g-van-oort, Urban growth and innovation: Spatially bounded externalities in the Netherlands, Routledge, 2017.

U. V. Luxburg, A tutorial on spectral clustering, Statistics and computing, vol.17, issue.4, pp.395-416, 2007.

S. Weisberg, Applied linear regression, vol.528, 2005.

S. Wold, K. Esbensen, and P. Geladi, Principal component analysis. Chemometrics and intelligent laboratory systems, vol.2, pp.37-52, 1987.

J. H. Wolfe, Object cluster analysis of social areas, 1963.

M. Wu, Collaborative filtering via ensembles of matrix factorizations, Proceedings of KDD Cup and Workshop, 2007.

W. Xu, X. Liu, and Y. Gong, Document clustering based on nonnegative matrix factorization, Proceedings of the 26th annual international ACM SIGIR conference on Research and development in information retrieval, pp.267-273, 2003.

Y. Yang, Can the strengths of AIC and BIC be shared? A conflict between model indentification and regression estimation, Biometrika, vol.92, issue.4, pp.937-950, 2005.

Z. Yang, J. Corander, and E. Oja, Low-rank doubly stochastic matrix decomposition for cluster analysis, Journal of Machine Learning Research, vol.17, issue.187, pp.1-25, 2016.

J. Yuan, Y. Zheng, and X. Xie, Discovering regions of different functions in a city using human mobility and pois, Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining, pp.186-194, 2012.

L. Zelnik, -. , and P. Perona, Self-tuning spectral clustering, Advances in neural information processing systems, pp.1601-1608, 2005.

J. Zhang, Y. Zheng, and D. Qi, Deep spatio-temporal residual networks for citywide crowd flows prediction, AAAI, pp.1655-1661, 2017.

Y. Zheng, Location-based social networks: Users, Computing with spatial trajectories, pp.243-276, 2011.

Y. Zheng, Y. Liu, J. Yuan, and X. Xie, Urban computing with taxicabs, Proceedings of the 13th international conference on Ubiquitous computing, pp.89-98, 2011.

Y. Zheng, L. Capra, O. Wolfson, and H. Yang, Urban computing: concepts, methodologies, and applications, ACM Transactions on Intelligent Systems and Technology (TIST), vol.5, issue.3, p.38, 2014.