. .. Apprentissage-structuré,

. .. Conclusions-du-chapitre, 267 .2 Map-matcher : un programme pour recaler les traces GPS, p.272

, Dans cette seconde partie de chapitre, nous conclurons ce travail de thèse en explorant les capacités de l'apprentissage structuré pour la détection de la signalisation routière. À nouveau, par manque de données (de traces GPS mais aussi de la cartographie routière

C. Dans-le, Ce choix, motivé par la nécessité de garantir un balayage homogène de la position de l'élément recherché au sein de la fenêtre glissante, pose en retour des problèmes lors de la séparation du jeu de données en deux sous-ensembles : entraînement et validation. Par ailleurs, nous pourrions objecter que, les instances individuelles étant partiellement communes, elles sont nécessairement corrélées, et nous sortons donc du cadre classique de l'apprentissage, tel que formulé dans la section 1.3.1. De plus, quand bien même les instances auraient été formées disjointement, la proximité spatiale de l'emprise des fenêtres glissantes induit inévitablement une corrélation des variables cibles. Par exemple, sachant que la fenêtre glissante X i est positive, la probabilité que la fenêtre suivante X i+1 soit également positive peut être considérée comme plus faible (en particulier dans le cas où la largeur des fenêtres est plutôt réduite, i.e. typiquement inférieure à une cinquantaine de mètres) ou à l'inverse plus élevée (en considérant que la positivité de X i nous informe sur le fait que X i , et par suite X i+1 , se situent probablement dans une zone urbaine de forte densité, nous avons utilisé des instances présentant un recouvrement de 90%, vol.3

G. Lu, . Dhurandhar, and . Dobra, 2010) offrent plusieurs stratégies pour répondre à cette problématique. La méthode la plus simple consiste à diviser l'entraînement en deux blocs distincts : de Markov locale, globale et pairwise (Gandolfi et Lenarda, 2017) constituent trois notions équivalentes, et y sont donc nécessairement toutes vérifiées. Sur le modèle de droite de la figure 5.16, la propriété locale se traduit par l'indépendance conditionnelle de X à toutes les autres variables sachant les variables grisées (qui constituent la couverture de Markov, i.e. la zone dont l'observation permet une caractérisation probabiliste complète de la variable à inférer X, 2003.

, Malgré tous ces avantages pratiques, contrairement aux réseaux bayésiens, les modèles di

, Etant donné un ensemble (éventuellement vide) de noeuds observés dans, S'étant munit d'un modèle graphique (dirigé ou non) pré-entraîné (i.e. dont les lois conditionnelles ou les potentiels de cliques ont été inférés à l'aide d'un jeu d'entraînement), 2007.

D. Potere, Horizontal Positional Accuracy of Google Earth's High-Resolution Imagery Archive, Sensors, vol.8, issue.12, pp.7973-7981, 2008.

E. D. Kaplan and J. C. Hegarty, Understanding GPS : Principles and Applications, 2006.

J. Biagioni and J. Eriksson, Inferring road maps from global positioning system traces : Survey and comparative evaluation, Transportation Research Record : Journal of the Transportation Research Board, pp.61-71, 2012.

T. Suzuki and N. Kubo, Simulation of GNSS Satellite Availability in Urban Environments Using Google Earth, Proceedings of the ION 2015 Pacific PNT Meeting, Honolulu, Hawaii, pp.1069-1079, 2015.

B. Li, S. Zhang, A. G. Dempster, and C. Rizos, Impact of RNSS on positioning in the Asia-Oceania region, Journal of Global Positioning, vol.10, issue.2, pp.114-124, 2011.

T. Takasu, N. Kubo, A. Yasuda, G. Rtk-gps, and . Symposium, , 2007.

X. Li, M. Ge, X. Dai, M. Fritsche, J. Wickert et al., Accuracy and reliability of multi-GNSS real-time precise positioning, Journal of Geodesy, vol.89, pp.607-635, 2015.

P. Brown, Annexe B Dans cette section, nous détaillons les développements menés dans la section 4.2. On note K : R ? R + une fonction noyau, c'est-à-dire symétrique, à valeurs positives et d'intégrale 1 sur l'ensemble des réels. De plus, 2011.

, t n ? R + (pour des raisons d'efficacité numérique, nous supposerons plus loin que les timestamps sont précis à la seconde près

. Pour-un-facteur-d'échelle-h-?-r-+, on note K h la transformée de la fonction K par une homothétie de rapport h : .3 PPED : un plugin d'acquisition de la vérité terrain Ce programme a été développé en grande partie avec l

, Le panneau de contrôle est représenté à droite de l'écran. (2) Sélection aléatoire d'un sous-ensemble de cellules pour contrôle de la qualité de saisie, Figure 35 -De haut en bas : (1) Saisie des points dans une grille régulière

C. Abraham, P. Cornillon, E. Matzner-løber, and N. Et-molinari, Unsupervised curve clustering using b-splines, Scandinavian journal of statistics, vol.30, issue.3, pp.581-595, 2003.

A. F. Agarap, Deep learning using rectified linear units (relu), 2018.

Y. Ahres, L. Janssen, J. Kangaspunta, and A. Et-jambulapati, Real-time dense map matching with naive hidden markov models : Delay versus accuracy, 2014.

Y. Aksoy, T. Oh, S. Paris, M. Pollefeys, and W. Et-matusik, Semantic soft segmentation, ACM Transactions on Graphics (TOG), vol.37, issue.4, p.72, 2018.

S. Albarqouni, C. Baur, F. Achilles, V. Belagiannis, S. Demirci et al., Aggnet : deep learning from crowds for mitosis detection in breast cancer histology images, IEEE transactions on medical imaging, vol.35, issue.5, pp.1313-1321, 2016.

D. M. Allen, The prediction sum of squares as a criterion for selecting predictor variables, 1971.

H. Alt, C. Knauer, and C. Et-wenk, Matching polygonal curves with respect to the fréchet distance, Annual Symposium on Theoretical Aspects of Computer Science, pp.63-74, 2001.

B. Aminian, V. Renaudin, D. Borio, and G. Et-lachapelle, Indoor doppler measurement and velocity characterization using a reference-rover receiver, International Conference ION GNSS, vol.4, pp.3069-3079, 2010.

C. Andrieu, Modélisation fonctionnelle de profils de vitesse en lien avec l'infrastructure et méthodologie de construction d'un profil agrégé, 2013.

C. Andrieu, G. S. Pierre, and X. Et-bressaud, A functional analysis of speed profiles : smoothing using derivative information, curve registration, and functional boxplot, 2013.
URL : https://hal.archives-ouvertes.fr/hal-00915475

C. Andrieu, G. Saint-pierre, and X. Et-bressaud, Estimation of space-speed profiles : A functional approach using smoothing splines, Intelligent Vehicles Symposium (IV), pp.982-987, 2013.
URL : https://hal.archives-ouvertes.fr/hal-02193742

A. K. Aniruddha and R. V. Babu, Visual object tracking via random ferns based classification, Acoustics, Speech and Signal Processing, pp.6533-6537, 2014.

C. Annunziata, C. D'apice, P. Benedetto, and R. Et-luigi, Optimization of traffic on road networks, Mathematical Models and Methods in Applied Sciences, vol.17, issue.10, pp.1587-1617, 2007.

C. Antoniou, R. Balakrishna, and H. N. Et-koutsopoulos, A synthesis of emerging data collection technologies and their impact on traffic management applications, European Transport Research Review, vol.3, issue.3, pp.139-148, 2011.

K. Anuar, F. Habtemichael, and M. Et-cetin, Estimating traffic flow rate on freeways from probe vehicle data and fundamental diagram, Intelligent Transportation Systems (ITSC), pp.2921-2926, 2015.

A. Anwar, W. Zeng, and S. Et-arisona, Time-space diagram revisited, Transportation Research Record : Journal of the Transportation Research Board, issue.2442, pp.1-7, 2014.

A. Arai, A. Witayangkurn, T. Horanont, X. Shao, and R. Et-shibasaki, Understanding the unobservable population in call detail records through analysis of mobile phone user calling behavior : A case study of greater dhaka in bangladesh, Pervasive Computing and Communications (PerCom, pp.207-214, 2015.

M. Arnaud and X. Emery, Estimation et interpolation spatiale : méthodes déterministes et méthodes géostatistiques, 2000.

F. Attal, Classification de situations de conduite et détection des événements critiques d'un deux roues motorisé, 2015.

B. Auder and A. Fischer, Projection-based curve clustering, Journal of Statistical Computation and Simulation, vol.82, issue.8, pp.1145-1168, 2012.
URL : https://hal.archives-ouvertes.fr/hal-00565541

O. Aydemir and T. Kayikcioglu, Wavelet transform based classification of invasive brain computer interface data. Radioengineering, vol.20, pp.31-38, 2011.

L. Azizi, Champs aléatoires de Markov cachés pour la cartographie du risque en épidémiologie, 2011.

F. Bahoken and A. Olteanu-raimond, Designing origin-destination flow matrices from individual mobile phone paths : The effect of spatiotemporal filtering on flow measurement, ICC'13-26th International Cartographic Conference, p.15, 2013.
URL : https://hal.archives-ouvertes.fr/hal-01011987

H. Bar-gera, Evaluation of a cellular phone-based system for measurements of traffic speeds and travel times : A case study from israel, Transportation Research Part C : Emerging Technologies, vol.15, issue.6, pp.380-391, 2007.

J. Bardet, Théorème de cochran et applications en statistiques, 2006.

M. Barret, Traitement statistique du signal : Estimation, filtrage de Wiener, méthodes récursives, détection. Ellipses, 2009.

C. Barreyre, B. Laurent, J. Loubes, B. Cabon, and I. Et-toulouse, Détection d'événements atypiques dans des données fonctionnelles. Les journées de la Statistique, 2016.

M. Bartos, H. Park, T. Zhou, B. Kerkez, and R. Et-vasudevan, Vehicles as sensors : high-accuracy rainfall maps from windshield wiper measurements, 2018.

G. E. Batista, R. C. Prati, and M. C. Et-monard, A study of the behavior of several methods for balancing machine learning training data, SIGKDD Explor. Newsl, vol.6, issue.1, pp.20-29, 2004.

M. Bejiga, A. Zeggada, A. Nouffidj, and F. Et-melgani, A convolutional neural network approach for assisting avalanche search and rescue operations with uav imagery, Remote Sensing, vol.9, issue.2, p.100, 2017.

A. Bel-hadj-ali, Qualité géométrique des entités géographiques surfaciques : Application à l'appariement et définition d'une typologie des écarts géométriques, 2001.

J. S. Bendat and A. G. Piersol, Random data : analysis and measurement procedures, vol.729, 2011.

L. Bentabet, S. Jodouin, D. Ziou, and J. Vaillancourt, Road vectors update using sar imagery : a snake-based method, IEEE Transactions on Geoscience and Remote Sensing, vol.41, issue.8, pp.1785-1803, 2003.

A. Berlinet, G. Biau, and L. Et-rouviere, Functional supervised classification with wavelets, Annales de l'ISUP, vol.52, p.19, 2008.
URL : https://hal.archives-ouvertes.fr/hal-00459437

J. Besag, On the statistical analysis of dirty pictures, Journal of the Royal Statistical Society : Series B (Methodological), vol.48, issue.3, pp.259-279, 1986.

P. Besse, Etude descriptive d'un processus : Approximation et interpolation, 1979.

P. Besse and C. Thomas-agnan, Le lissage par fonctions splines en statistique, revue bibliographique. Statistique et Analyse des données, vol.14, pp.55-84, 1989.

P. C. Besse, B. Guillouet, J. Loubes, and F. Royer, Review and perspective for distance-based clustering of vehicle trajectories, IEEE Transactions on Intelligent Transportation Systems, vol.17, issue.11, pp.3306-3317, 2016.

C. Bettini, X. S. Wang, and S. Et-jajodia, Protecting privacy against location-based personal identification, Workshop on Secure Data Management, pp.185-199, 2005.

J. Biagioni and J. Eriksson, Inferring road maps from global positioning system traces : Survey and comparative evaluation, Transportation Research Record : Journal of the Transportation Research Board, pp.61-71, 2012.

J. Biagioni and J. Eriksson, Map inference in the face of noise and disparity, Proceedings of the 20th International Conference on Advances in Geographic Information Systems, pp.79-88, 2012.

G. Biau, L. Devroye, and G. Et-lugosi, Consistency of random forests and other averaging classifiers, Journal of Machine Learning Research, vol.9, pp.2015-2033, 2008.
URL : https://hal.archives-ouvertes.fr/hal-00355368

G. Biau, L. Devroye, and G. Et-lugosi, On the performance of clustering in hilbert spaces, IEEE Transactions on Information Theory, vol.54, issue.2, pp.781-790, 2008.
URL : https://hal.archives-ouvertes.fr/hal-00290855

F. Bijleveld, A. Vasenev, T. Hartmann, and A. G. Et-doree, Real-time and post processing of gps data in the field of visualizing asphalt paving operations, pp.1-8, 2011.

F. Biljecki, H. Ledoux, and P. Et-van-oosterom, Transportation mode-based segmentation and classification of movement trajectories, International Journal of Geographical Information Science, vol.27, issue.2, pp.385-407, 2013.

H. L. Bodlaender and A. M. Koster, Treewidth computations i. upper bounds. Information and Computation, vol.208, pp.259-275, 2010.

O. Bonin, Modèle d'erreurs dans une base de données géographiques et grandes déviations pour des sommes pondérées ; application à l'estimation d'erreurs sur un temps de parcours, 2002.

P. Bosser, Gnss : systèmes globaux de positionnement par satelitte, cours de l'ecole nationale des sciences géographiques, 2011.

H. Bostrom, Estimating class probabilities in random forests, Sixth International Conference on Machine Learning and Applications (ICMLA 2007), pp.211-216, 2007.

J. Boureau, Manuel d'interprétation des photographies aériennes infrarouges : application aux milieux forestiers et naturels, 2008.

D. Bouteloup, Calculs topométriques : cours de l'ecole nationale des sciences géographiques, 2010.

K. W. Bowyer, N. V. Chawla, L. O. Hall, and W. P. Kegelmeyer, SMOTE : synthetic minority over-sampling technique, 2011.

F. Boyer, Agrégation externe de mathématiques equations différentielles ordinaires, 2012.

L. Breiman, Bagging predictors. Machine learning, vol.24, pp.123-140, 1996.

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

L. Breiman, Consistency for a simple model of random forests, 2004.

L. Breiman, Heuristics of instability and stabilization in model selection, The annals of statistics, vol.24, issue.6, pp.2350-2383, 1996.

L. Breiman, J. Friedman, C. J. Stone, and R. A. Et-olshen, Classification and regression trees, 1984.

C. Buisson, Impact de la fraction de véhicules équipés de capteurs sondes et du nombre de données utilisées sur la précision d'une estimation de temps de parcours : Evaluation de la qualité métrologique par une simulation simplifiée, 2017.

L. Cao and J. Krumm, From gps traces to a routable road map, Proceedings of the 17th ACM SIGSPATIAL international conference on advances in geographic information systems, pp.3-12, 2009.

S. Chakrabarti, B. Dom, and P. Et-indyk, Enhanced hypertext categorization using hyperlinks, ACM SIGMOD Record, vol.27, pp.307-318, 1998.

T. Chalko, Estimating accuracy of gps doppler speed measurement using speed dilution of precision (sdop) parameter, NU Journal of Discovery, vol.6, pp.4-9, 2009.

E. W. Chambers and D. Letscher, On the height of a homotopy, CCCG, vol.9, pp.103-106, 2009.

E. W. Chambers, D. Letscher, T. Ju, and L. Liu, Isotopic fréchet distance, CCCG, 2011.

A. Chen, A. Ramanandan, and J. A. Farrell, High-precision lane-level road map building for vehicle navigation, Position Location and Navigation Symposium (PLANS), pp.1035-1042, 2010.

C. Chen and Y. Cheng, International Workshop on Geoscience and Remote Sensing. ETT and GRS, Education Technology and Training, vol.1, pp.508-511, 2008.

Q. Chen, X. Song, H. Yamada, and R. Et-shibasaki, Learning deep representation from big and heterogeneous data for traffic accident inference, Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, AAAI'16, pp.338-344, 2016.

Q. Chen, X. Song, H. Yamada, and R. Et-shibasaki, Learning deep representation from big and heterogeneous data for traffic accident inference, AAAI, pp.338-344, 2016.

Y. Chen and J. Krumm, Probabilistic modeling of traffic lanes from gps traces, Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems, pp.81-88, 2010.

Q. Cheng, L. Nouveliere, and O. Et-orfila, A piecewise vehicle fuel consumption model for eco-driving assistance system, International Symposium on Dynamics of Vehicles on Road and Tracks (IAVSD 2013), pp.elec-proc, 2013.
URL : https://hal.archives-ouvertes.fr/hal-01009720

S. Cheung, Proof of hammersley-clifford theorem. Unpublished, 2008.

Y. K. Cheung and O. Daescu, Fréchet distance problems in weighted regions, International Symposium on Algorithms and Computation, pp.97-111, 2009.

H. C. Choe, R. E. Karlsen, G. R. Gerhart, and T. J. Et-meitzler, Wavelet-based ground vehicle recognition using acoustic signals, Wavelet Applications III, vol.2762, pp.434-446, 1996.

L. Chun-lin, A tutorial of the wavelet transform, 2010.

B. Conan-guez, Modélisation supervisée de données fonctionnelles par perceptron multi-couches, 2002.

F. Cottet, Traitement des signaux et acquisition de données, 1997.

A. Criminisi, J. Shotton, and E. Et-konukoglu, Decision forests for classification, regression, density estimation, manifold learning and semi-supervised learning. Microsoft Research Cambridge, vol.5, p.12, 2011.

G. Cybenko, Approximation by superpositions of a sigmoidal function, Mathematics of control, signals and systems, vol.2, pp.303-314, 1989.

S. Dabiri and K. Heaslip, Inferring transportation modes from gps trajectories using a convolutional neural network. Transportation research part C : emerging technologies, vol.86, pp.360-371, 2018.

T. Dalenius, Finding a needle in a haystack or identifying anonymous census records, Journal of official statistics, vol.2, issue.3, p.329, 1986.

M. Daniels, Classification of percussive sounds using wavelet-based, 2010.

D. Ministry-of-energy and U. Et-climate, Analysis of geospatial data requirement to support the operation of autonomous cars, 2017.

I. Daubechies, Orthonormal bases of compactly supported wavelets, Communications on pure and applied mathematics, vol.41, pp.909-996, 1988.

J. J. Davies, A. R. Beresford, and A. Hopper, Scalable, distributed, real-time map generation, IEEE Pervasive Computing, vol.5, issue.4, pp.47-54, 2006.

D. Debarr and H. Wechsler, Spam detection using clustering, random forests, and active learning, Sixth Conference on Email and Anti-Spam. Mountain View, California, pp.1-6, 2009.

L. Deng, The mnist database of handwritten digit images for machine learning research, 2012.

, IEEE Signal Processing Magazine, vol.29, issue.6, pp.141-142

B. Després, Lois de conservations eulériennes, lagrangiennes et méthodes numériques, vol.68, 2010.

J. Deville, Méthodes statistiques et numériques de l'analyse harmonique, Annales de l'INSEE, pp.3-101, 1974.

A. Dhurandhar and A. Dobra, Collective vs independent classification in statistical relational learning, 2010.

T. G. Dietterich and G. Bakiri, Error-correcting output codes : A general method for improving multiclass inductive learning programs, AAAI, pp.572-577, 1991.

E. W. Dijkstra, a note on two problems in connexion with graphs, IEEE Transactions on Systems Science and Cybernetics SSC4, vol.1, pp.269-271, 1959.

E. Dimitriadou, K. Hornik, F. Leisch, D. Meyer, A. Weingessel et al., , 2009.

V. Dizier and M. Margollé, Réseau de neurones convolutif pour la détection et la localisation de feux tricolores à partir de traces gps. stage de recherche des elèvesingénieurs de l'ensg, 2eme année, 2018.

T. Do, S. Lallich, N. Pham, and P. Et-lenca, Un nouvel algorithme de forêts aléatoires d'arbres obliques particulièrement adapté à la classification de données en grandes dimensions, EGC, pp.79-90, 2009.

A. Doche, Bmw, premier constructeur à intégrer les données gps partagées avec here, pp.2019-2020, 2018.

D. L. Donoho and J. M. Johnstone, Ideal spatial adaptation by wavelet shrinkage, biometrika, vol.81, issue.3, pp.425-455, 1994.

B. Dupont, Haute-garonne : le département expérimente la voiture connectée avec météo france et continental, pp.2019-2020, 2018.

X. Dupré, Receiving operator characteristic (roc), 2013.

Y. Dupuis, P. Merriaux, P. Subirats, R. Boutteau, X. Savatier et al., , 2014.

, Gps-based preliminary map estimation for autonomous vehicle mission preparation, Intelligent Robots and Systems (IROS 2014), pp.4241-4246, 2014.

S. Edelkamp and S. Schrödl, Route planning and map inference with global positioning traces, Computer science in perspective, pp.128-151, 2003.

B. Efron, Bootstrap methods : another look at the jackknife, Breakthroughs in statistics, pp.569-593, 1992.

J. Ehrlich, Quelle infrastructure pour le véhicule autonome. Routes/Roads, vol.373, pp.43-46, 2017.

J. Ehrlich, A. Bacelar, S. Et, and B. , Véhicules traceurs : une solution bas coût innovante et prometteuse pour la surveillance des réseaux routiers, Séminaire international "Technologies de prévention et de réduction des effets des catastrophes et apport des STI à l'exploitation des réseaux, 2014.

M. El-habib-boukhobza and M. Mimi, Classification automatique de la densité des tissus mammaires, Traitement du Signal, vol.33, pp.441-460, 2016.

A. M. Ellison, Effect of seed dimorphism on the density-dependent dynamics of experimental populations of atriplex triangularis (chenopodiaceae), American Journal of Botany, vol.74, issue.8, pp.1280-1288, 1987.

N. Eltchaninoff, Géoroute, une base de données routières pour la france. CFC N°1 49 -septembre, 1996.

R. L. Eubank, Nonparametric regression and spline smoothing, 1999.

I. Evtimov, K. Eykholt, E. Fernandes, T. Kohno, B. Li et al., Robust physical-world attacks on deep learning models, vol.1, p.1, 2017.

F. Ferraty and P. Vieu, Nonparametric functional data analysis : theory and practice, 2006.

P. Flajolet and R. Sedgewick, Analytic combinatorics, 2009.
URL : https://hal.archives-ouvertes.fr/inria-00072739

R. Flamary, Apprentissage statistique pour le signal : applications aux interfaces cerveau-machine, 2011.
URL : https://hal.archives-ouvertes.fr/tel-00687501

R. W. Floyd, algorithm 97 : Shortest path, Communications of the ACM, vol.5, issue.6, p.345, 1962.

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

J. Friedman, T. Hastie, and R. Et-tibshirani, Sparse inverse covariance estimation with the graphical lasso, Biostatistics, vol.9, issue.3, pp.432-441, 2008.

M. Fu, J. Li, and P. Zhou, Design and implementation of bidirectional dijkstra algorithm, JOURNAL-BEIJING INSTITUTE OF TECHNOLOGY-ENGLISH EDITION, pp.366-370, 2003.

A. Gandolfi and P. Lenarda, A note on gibbs and markov random fields with constraints and their moments, Mathematics and Mechanics of Complex Systems, vol.4, issue.3, pp.407-422, 2017.

U. Gather and V. Schultze, Robust estimation of scale of an exponential distribution, Statistica Neerlandica, vol.53, issue.3, pp.327-341, 1999.

R. Geisberger, P. Sanders, D. Schultes, and D. Et-delling, Contraction hierarchies : Faster and simpler hierarchical routing in road networks, In International Workshop on Experimental and Efficient Algorithms, pp.319-333, 2008.

S. Geman and D. Et-geman, Stochastic relaxation, gibbs distributions, and the bayesian restoration of images, Readings in computer vision, pp.564-584, 1987.

R. Genuer, J. Poggi, and C. Et-tuleau-malot, Variable selection using random forests, Pattern Recognition Letters, vol.31, issue.14, pp.2225-2236, 2010.
URL : https://hal.archives-ouvertes.fr/hal-00755489

G. Giambartolomei, The Karhunen-Loeve Theorem, 2015.

F. Giannotti and D. Pedreschi, Mobility, data mining and privacy : Geographic knowledge discovery, 2008.

P. Gilliéron and F. Peyret, Comment recueillir des informations de position provenant de véhicules traceurs, 2018.

J. Gilsinger and M. Jaï, Éléments d'analyse fonctionnelle : fondements et applications aux sciences de l'ingénieur, 2010.

C. Giraud, Introduction to high-dimensional statistics, 2014.

J. Girres, Modèle d'estimation de l'imprécision des mesures géométriques de données géographiques, 2012.

J. Girres and G. Et-touya, Quality assessment of the french openstreetmap dataset, Transactions in GIS, vol.14, issue.4, pp.435-459, 2010.
URL : https://hal.archives-ouvertes.fr/hal-02320425

C. Y. Goh, J. Dauwels, N. Mitrovic, M. T. Asif, A. Oran et al., Online map-matching based on hidden markov model for real-time traffic sensing applications, Intelligent Transportation Systems (ITSC), pp.776-781, 2012.

L. Gonçalves, A. Subtil, M. R. Oliveira, and P. Bermudez, Roc curve estimation : An overview, REVSTAT-Statistical Journal, vol.12, issue.1, pp.1-20, 2014.

D. Gorinevsky, Monotonic regression filters for trending deterioration faults, American Control Conference, vol.6, pp.5394-5399, 2004.

B. Gregorutti, Forêts aléatoires et sélection de variables : analyse des données des enregistreurs de vol pour la sécurité aérienne, 2015.

B. Gregorutti, B. Michel, and P. Saint-pierre, Correlation and variable importance in random forests, Statistics and Computing, vol.27, issue.3, pp.659-678, 2017.
URL : https://hal.archives-ouvertes.fr/hal-00879978

D. Grejner-brzezinska, C. Toth, and Y. Yi, On improving navigation accuracy of gps/ins systems. Photogrammetric engineering & remote sensing, vol.71, pp.377-389, 2005.

A. Grenapin, Pourquoi les embouteillages coûtent 677 euros aux foyers français, Le Point, 2013.

D. Guichon and F. Piel, Sources de données flottantes et mobiles liées au trafic : principales fonctionnaliées pour l'appui à la gestion du trafic, 2013.

F. Günther and S. Fritsch, neuralnet : Training of neural networks, The R journal, vol.2, issue.1, pp.30-38, 2010.

Z. Guo, Q. Chen, G. Wu, Y. Xu, R. Shibasaki et al., Village building identification based on ensemble convolutional neural networks, Sensors, vol.17, issue.11, p.2487, 2017.

A. Haar, Zur theorie der orthogonalen funktionensysteme, Mathematische Annalen, vol.69, issue.3, pp.331-371, 1910.
URL : https://hal.archives-ouvertes.fr/hal-01333722

A. Haghani, M. Hamedi, K. Sadabadi, S. Young, and P. Et-tarnoff, Data collection of freeway travel time ground truth with bluetooth sensors, Transportation Research Record : Journal of the Transportation Research Board, pp.60-68, 2010.

J. M. Hammersley and P. Clifford, Markov fields on finite graphs and lattices. Unpublished manuscript, p.46, 1971.

J. A. Hanley and B. J. Mcneil, The meaning and use of the area under a receiver operating characteristic (roc) curve, Radiology, vol.143, issue.1, pp.29-36, 1982.

B. E. Hansen, Lecture notes on nonparametrics, Lecture notes, 2009.

P. E. Hart, N. J. Nilsson, and B. Raphael, A formal basis for the heuristic determination of minimum cost paths, IEEE Transactions on Systems Science and Cybernetics, vol.4, issue.2, pp.100-104, 1968.

P. E. Hart, N. J. Nilsson, and B. Raphael, A formal basis for the heuristic determination of minimum cost paths, IEEE transactions on Systems Science and Cybernetics, vol.4, issue.2, pp.100-107, 1968.

D. J. Harvey and D. R. Wood, The treewidth of line graphs, Journal of Combinatorial Theory, Series B, vol.132, pp.157-179, 2018.

U. H. Hernandez-belmonte, V. Ayala-ramirez, and R. E. Sanchez-yanez, A comparative review of two-pass connected component labeling algorithms, Mexican International Conference on Artificial Intelligence, pp.452-462, 2011.

F. Hillnertz, Incremental self learning road map, 2014.

G. Hoang, B. Denis, J. Härri, and D. T. Et-slock, Breaking the gridlock of spatial correlations in gps-aided ieee 802.11 p-based cooperative positioning, IEEE Transactions on Vehicular Technology, vol.65, issue.12, pp.9554-9569, 2016.

K. Hornik, M. Stinchcombe, and H. Et-white, Multilayer feedforward networks are universal approximators, Neural networks, vol.2, issue.5, pp.359-366, 1989.

H. Hotelling, Analysis of a complex of statistical variables into principal components, Journal of educational psychology, vol.24, issue.6, p.417, 1933.

J. Hua, Z. Shen, and S. Et-zhong, We can track you if you take the metro : Tracking metro riders using accelerometers on smartphones, IEEE Transactions on Information Forensics and Security, vol.12, issue.2, pp.286-297, 2017.

W. Huber, M. Lädke, and R. Et-ogger, Extended floating-car data for the acquisition of traffic information, 1999.

A. T. Ihler, I. John, W. F. Et-willsky, and A. S. , Loopy belief propagation : Convergence and effects of message errors, Journal of Machine Learning Research, vol.6, pp.905-936, 2005.

E. Ising, Beitrag zur theorie des ferromagnetismus, Zeitschrift für Physik A Hadrons and Nuclei, vol.31, issue.1, pp.253-258, 1925.

S. Islam, Estimation of annual average daily traffic (aadt) and missing hourly volume using artificial intelligence, 2016.

A. Jacovi, O. S. Shalom, and Y. Goldberg, Understanding convolutional neural networks for text classification, 2018.

K. Jebreen, Modèles graphiques pour la classification et les séries temporelles, 2017.

E. Kaplan and C. Hegarty, Understanding GPS : principles and applications, 2005.

J. Z. Kato, Modelisations markoviennes multiresolutions en vision par ordinateur. Application a la segmentation d'images SPOT, 1994.

H. J. Kelley, Gradient theory of optimal flight paths, Ars Journal, vol.30, issue.10, pp.947-954, 1960.

D. P. Kingma and J. Ba, Adam : A method for stochastic optimization, 2014.

T. Kloks, Treewidth : computations and approximations, vol.842, 1994.

A. Koita, D. Daucher, and M. Et-fogli, New probabilistic approach to estimate vehicle failure trajectories in curve driving, Probabilistic Engineering Mechanics, vol.34, pp.73-82, 2013.
URL : https://hal.archives-ouvertes.fr/hal-00866049

D. Koller and N. Friedman, Probabilistic graphical models : principles and techniques, 2009.

V. Kolmogorov and R. Zabih, What energy functions can be minimizedvia graph cuts ?, IEEE Transactions on Pattern Analysis & Machine Intelligence, issue.2, pp.147-159, 2004.

K. P. Körding and D. M. Wolpert, Bayesian decision theory in sensorimotor control, Trends in cognitive sciences, vol.10, issue.7, pp.319-326, 2006.

P. Krishnamurthy, D. Tipper, and J. Joshi, Position location technologies for wireless systems, 2013.

F. R. Kschischang, The wiener-khinchin theorem. the edward s. rogers sr. department of electrical and computer engineering university of toronto, 2017.

F. R. Kschischang, B. J. Frey, and H. Loeliger, Factor graphs and the sum-product algorithm, IEEE Transactions on information theory, vol.47, issue.2, pp.498-519, 2001.

S. K. Kumar, On weight initialization in deep neural networks, 2017.

M. B. Kursa, rferns-random ferns method implementation for the general-purpose machine learning, Journal of Statistical Software, 2012.

K. Kuwata and R. Shibasaki, Estimating crop yields with deep learning and remotely sensed data, Geoscience and Remote Sensing Symposium (IGARSS), pp.858-861, 2015.

P. Lamb and S. Thiébaux, Avoiding explicit map-matching in vehicle location, 6th World Conference on Intelligent Transportation Systems (ITS-99), 1999.

L. Landrieu, Learning structured models on weighted graphs, with applications to spatial data analysis, Paris Sciences et Lettres, 2016.
URL : https://hal.archives-ouvertes.fr/tel-01750023

S. Lannuzel, Référentiels géodésiques -coordonnées, 2000.

S. Lathuilière, P. Mesejo, X. Alameda-pineda, and R. Et-horaud, Deepgum : Learning deep robust regression with a gaussian-uniform mixture model, Proceedings of the European Conference on Computer Vision (ECCV), pp.202-217, 2018.

A. Laureshyn, K. Åström, and K. Et-brundell-freij, From speed profile data to analysis of behaviour : classification by pattern recognition techniques, IATSS research, vol.33, issue.2, pp.88-98, 2009.

G. Leduc, Road traffic data : Collection methods and applications, Working Papers on Energy, Transport and Climate Change, issue.55, p.1, 2008.

Y. Lee, Y. Suh, and R. Shibasaki, A simulation system for gnss multipath mitigation using spatial statistical methods, Computers & Geosciences, vol.34, issue.11, pp.1597-1609, 2008.

J. Li, Q. Qin, J. Han, L. Tang, and K. H. Et-lei, Mining trajectory data and geotagged data in social media for road map inference, Transactions in GIS, vol.19, issue.1, pp.1-18, 2015.

W. Li, M. Jiang, Y. Chen, and M. C. Et-lin, Estimating urban traffic states using iterative refinement and wardrop equilibria, IET Intelligent Transport Systems, vol.12, issue.8, pp.875-883, 2018.

X. Li, G. Zhang, H. H. Huang, Z. Wang, and W. Et-zheng, Performance analysis of gpu-based convolutional neural networks, 45th International Conference on Parallel Processing (ICPP), pp.67-76, 2016.

A. Liaw and M. Wiener, Classification and regression by randomforest, vol.2, pp.18-22, 2002.

A. Liaw and M. Wiener, Classification and regression by randomforest. R news, vol.2, pp.18-22, 2002.

G. Lindgren, H. Rootzén, and M. Et-sandsten, Stationary stochastic processes for scientists and engineers, 2013.

C. Liu, Y. Chan, S. H. Alam-kazmi, and H. Fu, Financial fraud detection model : based on random forest, International journal of economics and finance, vol.7, issue.7, 2015.

X. Liu, J. Biagioni, J. Eriksson, Y. Wang, G. Forman et al., Mining largescale, sparse gps traces for map inference : Comparison of approaches, Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD '12, pp.669-677, 2012.

S. Lloyd, Least squares quantization in pcm, IEEE transactions on information theory, vol.28, issue.2, pp.129-137, 1982.

J. Lopes, J. Bento, E. Huang, C. Antoniou, and M. Et-ben-akiva, Traffic and mobility data collection for real-time applications, Intelligent Transportation Systems (ITSC), pp.216-223, 2010.

S. López-pintado and J. Romo, On the concept of depth for functional data, Journal of the American Statistical Association, vol.104, issue.486, pp.718-734, 2009.

M. Lotfi, A. Solimani, A. Dargazany, H. Afzal, and M. Et-bandarabadi, Combining wavelet transforms and neural networks for image classification, SSST 2009. 41st Southeastern Symposium on, pp.44-48, 2009.

J. Loubes, É. Maza, M. Lavielle, and L. Et-rodriguez, Road trafficking description and short term travel time forecasting, with a classification method, Canadian Journal of Statistics, vol.34, issue.3, pp.475-491, 2006.

G. Louppe, Understanding random forests : From theory to practice, 2014.

Q. Lu and L. Getoor, Link-based classification, Proceedings of the 20th International Conference on Machine Learning (ICML-03), pp.496-503, 2003.

F. Lv, W. Wang, Y. Wei, Y. Sun, J. Huang et al., Detecting fraudulent bank account based on convolutional neural network with heterogeneous data, Mathematical Problems in Engineering, 2019.

M. Maboudi, J. Amini, M. Hahn, and M. Et-saati, Road network extraction from vhr satellite images using context aware object feature integration and tensor voting, Remote Sensing, vol.8, issue.8, p.637, 2016.

S. Macskassy and F. Provost, Confidence bands for roc curves : Methods and an empirical study, Proceedings of the First Workshop on ROC Analysis in AI, 2004.

S. A. Macskassy and F. Provost, Classification in networked data : A toolkit and a univariate case study, Journal of machine learning research, vol.8, pp.935-983, 2007.

P. C. Mahalanobis, On the generalized distance in statistics, 1936.

S. Mallik, Intelligent transportation system, International Journal of Civil Engineering Research, vol.5, issue.4, pp.367-372, 2014.

G. Marcou, Décentralisation : approfondissement ou nouveau cycle ? CAHIERS FRANCAIS-PARIS, pp.8-14, 2004.

J. Marin, D. Vázquez, A. M. López, J. Amores, and B. Et-leibe, Random forests of local experts for pedestrian detection, Proceedings of the IEEE international conference on computer vision, pp.2592-2599, 2013.

E. Massaro, C. Ahn, C. Ratti, P. Santi, R. Stahlmann et al., The car as an ambient sensing platform, Proceedings of the IEEE, vol.105, pp.3-7, 2017.

R. B. Mcmaster, A statistical analysis of mathematical measures for linear simplification. The American Cartographer, vol.13, pp.103-116, 1986.

G. Menardi and N. Torelli, Training and assessing classification rules with imbalanced data, Data Mining and Knowledge Discovery, pp.1-31, 2014.

Y. Méneroux, D. Manandhar, S. Ranjit, G. Saint-pierre, and R. Et-shibasaki, Positional accuracy control in dense urban environment with low-cost receiver and multiconstellation gnss, Proc. 9th Multi-GNSS Asia-MGA Conference, 2017.

, Développement des véhicules autonomes : orientations stratégiques pour l'action publique, 2018.

M. W. Mitchell, Bias of the random forest out-of-bag (oob) error for certain input parameters, Open Journal of Statistics, vol.1, issue.03, p.205, 2011.

D. Mitrovi?, Learning driving patterns to support navigation, 2004.

H. Miyazaki, K. Kuwata, W. Ohira, Z. Guo, X. Shao et al., Development of an automated system for building detection from high-resolution satellite images, Earth Observation and Remote Sensing Applications (EORSA), pp.245-249, 2016.

J. Mockus, Bayesian approach to global optimization : theory and applications, vol.37, 2012.

A. T. Moreno and A. García, Use of speed profile as surrogate measure : Effect of traffic calming devices on crosstown road safety performance, Accident Analysis & Prevention, vol.61, pp.23-32, 2013.

N. Morizet, N. Godin, J. Tang, E. Maillet, M. Fregonese et al., Classification of acoustic emission signals using wavelets and random forests : Application to localized corrosion, Mechanical Systems and Signal Processing, vol.70, pp.1026-1037, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01804466

J. Moussouris, Gibbs and markov random systems with constraints, Journal of statistical physics, vol.10, issue.1, pp.11-33, 1974.

M. Munoz-organero, R. Ruiz-blaquez, and L. Et-sánchez-fernández, Automatic detection of traffic lights, street crossings and urban roundabouts combining outlier detection and deep learning classification techniques based on gps traces while driving, Computers, Environment and Urban Systems, vol.68, pp.1-8, 2018.

S. Mustière and T. Devogele, Matching networks with different levels of detail, GeoInformatica, vol.12, issue.4, pp.435-453, 2008.

A. Myronenko and X. Song, Point set registration : Coherent point drift, IEEE transactions on pattern analysis and machine intelligence, vol.32, pp.2262-2275, 2010.

E. Nadaraya, On non-parametric estimates of density functions and regression curves, Theory of Probability & Its Applications, vol.10, pp.186-190, 1965.

G. Nason, Choice of the threshold parameter in wavelet function estimation, Wavelets and statistics, pp.261-280, 1995.

G. Nason and M. M. Maechler, The wavethresh package, 2006.

C. Negulescu, Interpolation : cours de prépération à l'agrégation, option calcul scientifique et modélisation, 2007.

J. Neville and D. Jensen, Iterative classification in relational data, Proc. AAAI-2000 Workshop on Learning Statistical Models from Relational Data, pp.13-20, 2000.

P. Newson and J. Krumm, Hidden markov map matching through noise and sparseness, Proceedings of the 17th ACM SIGSPATIAL international conference on advances in geographic information systems, pp.336-343, 2009.

C. Nguyen, Y. Wang, and H. N. Et-nguyen, Random forest classifier combined with feature selection for breast cancer diagnosis and prognostic, Journal of Biomedical Science and Engineering, vol.6, issue.05, p.551, 2013.

M. Ozuysal, P. Fua, and V. Et-lepetit, Fast keypoint recognition in ten lines of code, Computer Vision and Pattern Recognition, 2007. CVPR'07. IEEE Conference on, pp.1-8, 2007.

A. Palomino-garibay, A. T. Camacho-gonzalez, R. A. Fierro-villaneda, I. Hernandez-farias, D. Buscaldi et al., A random forest approach for authorship profiling, Proceedings of CLEF, 2015.

E. Parzen, On estimation of a probability density function and mode. The annals of mathematical statistics, vol.33, pp.1065-1076, 1962.

K. Pearson, Ix. mathematical contributions to the theory of evolution.-xix. second supplement to a memoir on skew variation, Philosophical Transactions of the Royal Society of London. Series A, Containing Papers of a Mathematical or Physical Character, pp.429-457, 1916.
URL : https://hal.archives-ouvertes.fr/in2p3-00803018

M. Petovello, How does a gnss receiver estimate velocity ? Inside GNSS, pp.38-41, 2015.

R. Pfister, K. A. Schwarz, M. Janczyk, R. Dale, and J. Et-freeman, Good things peak in pairs : a note on the bimodality coefficient, Frontiers in psychology, vol.4, p.700, 2013.

K. Phoon, S. Huang, and S. Et-quek, Simulation of second-order processes using karhunen-loeve expansion, Computers & structures, vol.80, issue.12, pp.1049-1060, 2002.

B. Picinbono, Signaux aléatoires -Tome 2 Fonctions aléatoires et modèles avec problèmes résolus, 1998.

T. Postadjian, A. Le-bris, H. Sahbi, and C. Et-mallet, Investigating the potential of deep neural networks for large-scale classification of very high resolution satellite images, ISPRS Annals, vol.4, pp.183-190, 2017.
URL : https://hal.archives-ouvertes.fr/hal-02384451

R. B. Potts, Some generalized order-disorder transformations, Mathematical proceedings of the cambridge philosophical society, vol.48, pp.106-109, 1952.

V. Protschky, C. Ruhhammer, and S. Et-feit, Learning traffic light parameters with floating car data, IEEE 18th International Conference on Intelligent Transportation Systems, pp.2438-2443, 2015.

E. Purson, P. Bonanaud, B. Levilly, E. Klein, and A. Et-bacelar, Evaluations simultanées de différentes technologies innovantes de recueil de données trafic pour le calcul de temps de parcours en temps réel, Congrès ATEC ITS France 2015 : Les Rencontres de la Mobilité Intelligente, 2015.

L. Qiang, G. Qian, M. Lixin, and Q. Et-mingyao, Measuring variability of arterial road traffic condition using archived probe data, Journal of Transportation Systems Engineering and Information Technology, vol.12, issue.2, pp.41-46, 2012.

J. Qiu and R. Wang, Road map inference : A segmentation and grouping framework, ISPRS International Journal of Geo-Information, vol.5, issue.8, p.130, 2016.

M. A. Quddus, W. Y. Ochieng, and R. B. Noland, Current map-matching algorithms for transport applications : State-of-the art and future research directions. Transportation research part c : Emerging technologies, vol.15, pp.312-328, 2007.

, R : A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, 2008.

M. Raafat, B. Abdullah, M. Taher, and M. N. Et-moustafa, Towards privacypreserving driver's drowsiness and distraction detection : A differential privacy approach, International Journal of Computing and Digital Systems, issue.05, p.5, 2016.

J. Ramsay, G. Hooker, and S. Et-graves, Functional data analysis with R and MAT-LAB, 2009.

J. O. Ramsay and C. Dalzell, Some tools for functional data analysis, Journal of the Royal Statistical Society. Series B (Methodological), pp.539-572, 1991.

J. O. Ramsay and . Silverman, Functional Data Analysis. Springer series in statistics, 2005.

J. O. Ramsay and B. W. Silverman, Applied functional data analysis : methods and case studies, 2007.

P. Ranacher, R. Brunauer, W. Trutschnig, S. Van-der-spek, and S. Et-reich, Why gps makes distances bigger than they are, International Journal of Geographical Information Science, vol.30, issue.2, pp.316-333, 2016.

S. Ranjit, M. Nagai, I. Rittaporn, T. Ajjanapanya, F. Hilding et al., Gps enabled taxi probe's big data processing for traffic evaluation of bangkok using apache hadoop distributed system, Asian Transportation Research Society Symposium, pp.291-296, 2014.

B. W. Remondi, Computing satellite velocity using the broadcast ephemeris, GPS solutions, vol.8, issue.3, pp.181-183, 2004.

G. Resconi, Conflict compensation, redundancy and similarity in databases federation, Transactions on Computational Collective Intelligence XIV, pp.120-135, 2014.

I. Rish, An empirical study of the naive bayes classifier, IJCAI 2001 workshop on empirical methods in artificial intelligence, vol.3, pp.41-46, 2001.

W. D. Roberts, Gps time correlation and its implication for precise navigation, 1993.

S. Rogers and M. Girolami, A first course in machine learning, 2016.

O. Ronneberger, P. Fischer, and T. Brox, U-net : Convolutional networks for biomedical image segmentation, International Conference on Medical image computing and computer-assisted intervention, pp.234-241, 2015.

M. Rosenblatt, Remarks on some nonparametric estimates of a density function, The Annals of Mathematical Statistics, pp.832-837, 1956.

S. Ruder, An overview of gradient descent optimization algorithms, 2016.

P. Saarikivi, State of the art of floating car measurements. MOBI-ROMA project, 2011.

G. Saint-pierre, Identification du nombre de composants d'un mélange gaussien par chaînes de Markov à sauts réversibles dans le cas multivarié ou par maximun de vraisemblance dans le cas univarié, vol.3, 2003.

A. Saltelli, K. Chan, and E. Scott, Wiley series in probability and statistics, Sensitivity analysis, 2000.

A. Saltelli, K. Chan, and E. M. Scott, Sensitivity analysis. Wiley series in probability and statistics, 2000.
URL : https://hal.archives-ouvertes.fr/inria-00386559

G. Saporta, Méthodes exploratoires d'analyse de données temporelles, Cahiers du bureau universitaire de recherche opérationnelle, pp.37-38, 1981.

K. Schliep, K. Hechenbichler, and M. K. Et-schliep, The kknn package. Unknown, 2007.

M. Schmidt, Ugm : A matlab toolbox for probabilistic undirected graphical models, 2007.

S. Schroedl, K. Wagstaff, S. Rogers, P. Langley, and C. Et-wilson, Mining GPS traces for map refinement, Data mining and knowledge Discovery, vol.9, issue.1, pp.59-87, 2004.

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

P. D. Seymour and R. Thomas, Graph searching and a min-max theorem for treewidth, Journal of Combinatorial Theory, Series B, vol.58, issue.1, pp.22-33, 1993.

R. D. Shachter, Bayes-ball : The rational pastime (for determining irrelevance and requisite information in belief networks and influence diagrams), 2013.

G. Shafer, Dempster-shafer theory, Encyclopedia of artificial intelligence, vol.1, pp.330-331, 1992.

R. Shirato, Dynamic map development in sip-adus, SIP-adus Workshop, 2016.

M. Siddiqui, Some problems connected with rayleigh distributions, Journal of Research of the National Bureau of standards, vol.66, issue.2, pp.167-174, 1962.

P. Sillard, Estimation par moindres carrés, 2001.

B. W. Silverman, Density estimation for statistics and data analysis, vol.26, 1986.

D. Simon, Kalman filtering with state constraints : a survey of linear and nonlinear algorithms, IET Control Theory & Applications, vol.4, issue.8, pp.1303-1318, 2010.

K. Simonyan and A. Et-zisserman, Very deep convolutional networks for large-scale image recognition, 2014.

R. Sirefelt, Master's thesis in Complex Adaptive Systems, 2015.

A. Sohr, E. Brockfeld, and S. Et-krieg, Quality of floating car data, Conference Proceedings, paper, number 02392, pp.11-15, 2010.

N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Et-salakhutdinov, Dropout : a simple way to prevent neural networks from overfitting, The Journal of Machine Learning Research, vol.15, issue.1, pp.1929-1958, 2014.

R. S. Stankovi? and B. J. Falkowski, The haar wavelet transform : its status and achievements, Computers & Electrical Engineering, vol.29, issue.1, pp.25-44, 2003.

A. Steiner and A. Leonhardt, Map-generation algorithm using low-frequency vehicle position data, 2011.

H. J. Suermondt and G. F. Cooper, Probabilistic inference in multiply connected belief networks using loop cutsets, International Journal of Approximate Reasoning, vol.4, issue.4, pp.283-306, 1990.

S. Sumathi, H. L. Beaulah, and R. Vanithamani, A wavelet transform based feature extraction and classification of cardiac disorder, Journal of medical systems, vol.38, issue.9, p.98, 2014.

Y. Sun and M. G. Et-genton, Functional boxplots, Journal of Computational and Graphical Statistics, vol.20, issue.2, pp.316-334, 2011.

C. Suquet, Lois des grands nombres, 2003.

C. Sutton and A. Mccallum, An introduction to conditional random fields. Foundations and Trends® in Machine Learning, vol.4, pp.267-373, 2012.

L. Sweeney, Simple demographics often identify people uniquely, Health, vol.671, pp.1-34, 2000.

S. Tesfamariam and Z. Liu, Earthquake induced damage classification for reinforced concrete buildings, Structural safety, vol.32, pp.154-164, 2010.

T. M. Therneau and E. J. Atkinson, An introduction to recursive partitioning using the rpart routines, 1997.

R. Tibshirani, Regression shrinkage and selection via the lasso, Journal of the Royal Statistical Society. Series B (Methodological), pp.267-288, 1996.

K. Torkkola, S. Venkatesan, and H. Liu, Sensor selection for maneuver classification, The 7th International IEEE Conference on, pp.636-641, 2004.

Q. Truong, G. Touya, and C. Et-de-runz, Towards vandalism detection in openstreetmap through a data driven approach, GIScience 2018. Schloss Dagstuhl-Leibniz-Zentrum fuer Informatik, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01856185

S. Tully, G. Kantor, and H. Et-choset, Inequality constrained kalman filtering for the localization and registration of a surgical robot, Intelligent Robots and Systems (IROS), pp.5147-5152, 2011.

B. Vallet, Homological persistence for shape based change detection between digital elevation models, ISPRS Annals, vol.3, p.2, 2013.

C. J. Van-rijsbergen, Foundation of evaluation, Journal of documentation, vol.30, issue.4, pp.365-373, 1974.

K. Van-winden, Automatically deriving and updating attribute road data from movement trajectories, 2014.

K. Van-winden, F. Biljecki, . Et-van-der, and S. Spek, Automatic update of road attributes by mining gps tracks, Transactions in GIS, vol.20, issue.5, pp.664-683, 2016.

D. Vats and R. D. Nowak, A junction tree framework for undirected graphical model selection, The Journal of Machine Learning Research, vol.15, issue.1, pp.147-191, 2014.

F. Vauglin, Modèles statistiques des imprécisions géométriques des objets géographiques linéaires, 1997.

M. Villamizar, A. Garrell, A. Sanfeliu, and F. Et-moreno-noguer, Online humanassisted learning using random ferns, Pattern Recognition (ICPR), 2012 21st International Conference on, pp.2821-2824, 2012.

A. Viterbi, Error bounds for convolutional codes and an asymptotically optimum decoding algorithm, IEEE Trans. Inform. Theory, vol.13, issue.13, pp.260-269, 1967.

M. J. Wainwright and M. I. Jordan, Graphical models, exponential families, and variational inference. Foundations and Trends® in Machine Learning, vol.1, pp.1-305, 2008.

E. Walter, Méthodes numériques et optimisation, 2016.

C. Wang, P. Hao, G. Wu, X. Qi, T. Lyu et al., Intersection and stop bar position extraction from crowdsourced gps trajectories, 2017.

Y. Wang, Y. Zheng, and Y. Et-xue, Travel time estimation of a path using sparse trajectories, Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining, pp.25-34, 2014.

Z. Wang, C. Lai, X. Chen, B. Yang, S. Zhao et al., Flood hazard risk assessment model based on random forest, Journal of Hydrology, vol.527, pp.1130-1141, 2015.

R. Warnant, L. Van-de-vyvere, and Q. Et-warnant, Positioning with single and dual frequency smartphones running android 7 or later, 2018.

C. Wei-lun, Gabor wavelet transform and its application, 2011.

C. Wenk, Geodesic fréchet distance inside a simple polygon, ACM Transactions on Algorithms (TALG), vol.7, issue.1, p.9, 2010.

C. K. Wilson, S. Rogers, and S. Et-weisenburger, The potential of precision maps in intelligent vehicles, IEEE International Conference on Intelligent Vehicles, pp.419-422, 1998.

A. Witayangkurn, T. Horanont, and R. Et-shibasaki, The design of large scale data management for spatial analysis on mobile phone dataset, Asian Journal of Geoinformatics, vol.13, issue.3, 2013.

T. Wohlfarth, Machine-learning pour la prédiction des prix dans le secteur du tourisme en ligne, 2013.

A. Wolfermann, W. Alhajyaseen, and H. Et-nakamura, Modeling speed profiles of turning vehicles at signalized intersections, 3rd International Conference on Road Safety and Simulation RSS2011, 2011.

S. Worrall and E. Nebot, Automated process for generating digitised maps through gps data compression, Australasian Conference on Robotics and Automation, 2007.

X. Xie, K. Bing-yungwong, H. Aghajan, P. Veelaert, and W. Et-philips, Inferring directed road networks from gps traces by track alignment, ISPRS International Journal of Geo-Information, vol.4, issue.4, pp.2446-2471, 2015.

J. S. Yedidia, W. T. Freeman, and Y. Weiss, Understanding belief propagation and its generalizations. Exploring artificial intelligence in the new millennium, vol.8, pp.236-239, 2003.

F. Zaklouta, B. Stanciulescu, and O. Et-hamdoun, Traffic sign classification using kd trees and random forests, The 2011 International Joint Conference on Neural Networks, pp.2151-2155, 2011.

A. Z. Zambom and R. Dias, A review of kernel density estimation with applications to econometrics, 2012.

L. Zhang, F. Thiemann, and M. Sester, Integration of gps traces with road map, Proceedings of the Third International Workshop on Computational Transportation Science, pp.17-22, 2010.

Q. Zhang and I. Couloigner, Automated road network extraction from high resolution multi-spectral imagery, Proceedings of ASPRS 2006 Annual Conference, pp.1-05, 2006.

H. Zhao, J. Kumagai, M. Nakagawa, and R. Et-shibasaki, Semi-automatic road extraction from high-resolution satellite image. International Archives of Photogrammetry Remote Sensing And Spatial Information Sciences, vol.34, pp.406-411, 2002.

Y. Zheng, Trajectory data mining : an overview, ACM Transactions on Intelligent Systems and Technology (TIST), vol.6, issue.3, p.29, 2015.

E. Zivot, Econometric Theory I : Estimation and Inference (first quarter, second year PhD), 2009.

V. Zverovich and E. Avineri, Braess' paradox in a generalised traffic network, Journal of Advanced Transportation, vol.49, issue.1, pp.114-138, 2015.