. Enfin, chacune des 7 classes a été soumise à une analyse de corrélation canonique . L'objectif est ici de

. La-valeur-canonique, Un seuil fixé 5.8. UN EXEMPLE D'APPLICATION à 0,1 a été choisi pour retenir le coefficient de corrélation entre les variables d'entrée et de sortie

. Tns, 9 -Les chemins les plus rapides versus l'état des arcs E/S Chemin le plus rapide Etat du svstème l-10 1-3-7-10(1) 1-3-8-10(1) 1-3-8-10(1) 1-3-8, pp.8-9

I. Si, 9) est de rang 3 Cependant nous observons les rangs 1 et 3 pour I'arc (4,9) Nous observons que la corrélation est positive (+o,tt; pour le rang 3 et négative (-0,i3) pour Ie rang 1. Cela signifie que le chemin 2-3-8-10 est le plus rapide si

S. {r, S) -3 ET r(3,8) : 3 ET r(4,8) : 3 ET r(1,3) : 3 ET r(2,3) : 3 } ALORS le chemin le plus rapide est

S. {r, 8) : 1 ET r(3,8) : 1 ET r(4,8) -1 ET r(1,3) -1 ET r, pp.3-4

S. {r, 8) -1 ET r(3,8) : 1 ET r(4,8) : 1 } ALORS Ie chemin te ptus rapide est, p.10

S. {r, 8) : 1 ET r(3,8) : 1 ET r(4,8) -1 ET r(1,3) : 1 } ALORS ie chemin le plus rapide est, p.10

S. Ir, 1 ET r(2,4) -1 ) ALORS Ie chemin le plus rapide est, p.10

S. {r, 1 ET r(2,5) -1 ET r(5,9) -1 } ATORS Ie chemin le plus rapide est, p.10

S. {r, 9) t IET r(1,5) -3 ET r(2,5) -3} ATORS Ie chemin le plus rapide est

S. Ir, 5) : 1 ET r(2,5) : 1 ET r(5,9) : 1 ET r(4,9) : 1) ATORS le chemin le plus rapide est

S. {r, 9) -1 ET r(1,5) + | ET r(1,4) * 1) ALORS le chemin le plus rapide est

. La-tableau-5, 10 présente les flux d'entrée et les états initiaux des arcs utilisés pour la simulation

. La-tableau-5, 11 donne Ia résultat de Ia simulation, c'est-à-dire Ie chemin le plus rapide entre chaque couple

L. Tableaux, 1-8.5 montrent les resultats de I'algorithme HRP-1' Nous voyons que, à la fin de cet algorithme, 109 sous-réseaux sont obtenus' Les résultats de l'algorithme HRP-2 sont presentés dans les tableaux 8.6-8'10

. Tne, 10 Sous réseaux fournis par HRP-2 pour le réseau Parisien Sous réseaux Noeuds Noeuds externes Densité

S. Bibliographie-[-abb+981-algers and . Bernauer, Review of micro-simulation models, Rep, p.3, 1998.

C. Ambroise, F. Badran, S. Thiriâ, and G. Sèze, Hierarchical clustering of self-organizing maps for cloud classification, Neurocomputing, vol.30, issue.1-4, pp.1-447, 1998.
DOI : 10.1016/S0925-2312(99)00141-1

M. V. Aerde, . Ahgs, B. V. Arem, and J. Hogema, Integration : A model for simulating integrated traffic networks, Transportati'on System Research Group The microscopic simulation model mixic L2, 1994.

A. Appo3a-]-awasthi, M. Parent, and J. Proth, Computing travel time on a single lane using system analysis, ITSC conference, 2003.

A. Awasthi, A. Parent, M. Proth, and J. , Estimating travel time in a single lane system-i, ITS Madri,d, world Transportati, 2003.

A. Awasthi, M. Parent, and J. Proth, Simulation system for estimating travei time in a network, IEEE SMC conferencel Washi, 2003.

A. Awasthi, M. Parent, and J. , Decomposition of an urban network for implementing a hierarchical route guidance system, 2004.

D. Ben-avraham and J. Kohler, Mean-fierd (n,m)-cruster approximation for lattice models, Phys. Reu. A 4b,8ïbg Analysis and simulation of traffic flow, http : I I www, 1992.

. Bcfggg, J. Barcelo, J. Casas, J. Ferrer, and D. Garcia, Modelrirrg advanced transport telematic applications with microscopic simu- Iators : The case with aimsun2, Trffic and Mobili,ty : simulati,on Econom,ics Enu

E. Ben-naim, L. Krapivsky, and S. Redner, Kinetics of clustering in traffic flows, Physical Review E, vol.60, issue.2, p.822
DOI : 10.1103/PhysRevLett.60.871

D. Bui, P. Nelson, S. Narsimhan, and . Uniuersity, Computational realizations of the entropy condition in modeiling congested traffic flow, Lgg2)

D. Branston, Models of single lane time headway clistributions , Transportati,on Science 1A, pp.125-173, 1976.

D. Buckley, A semlpoisson model of traffic flow, Transportati ,on Sc'ience, pp.107-132, 1968.

R. Chandler, R. Hermatr, E. Montroll, D. Karger, D. Pedetsen et al., Traffic Dynamics: Studies in Car Following, Metacor-a macroscopic modelling tool for corridor application to the stockholm test site, Research Report, pp.165-184, 1958.
DOI : 10.1287/opre.6.2.165

C. Daganzo and C. Daganzo, The cell transmission model: A dynamic representation of highway traffic consistent with the hydrodynamic theory, Transportation Research Part B: Methodological, vol.28, issue.4, pp.269-287, 1994.
DOI : 10.1016/0191-2615(94)90002-7

C. Daganzo, A continuum theory of traffic dynamics for freeways with special lanes, Transportation Research Part B: Methodological, vol.31, issue.2, pp.83-102, 1997.
DOI : 10.1016/S0191-2615(96)00017-3

T. Dijker and P. Knoppers, Fosim 5.0 user manuar, Technical speci'ficat'ion report, Delft (Ini,uersity of Technology, Transporr and Planni,ng D epartment, N etherlands, I S S, p.2004, 2004.

A. Dolgui and O. Zaikin, eueueing network models for a capacity allocation problem, pp.31-317, 2000.

A. Faghri and J. Hua, Evaluation of artificial neural network applications in transportation engineering, Transportation Research Record, vol.7992, 1358.

A. George and J. Liu, Computer Solution of Large Sparse Pos'iti,ue Definite Systems, 1981.

H. Garcia, J. Proth, M. Gourgand, X. Sun, and N. Tchernev, Group technology in production management : the short horizon planning level, Appti,ed stochast'ic models and data analys'is,I : 25 34 Impact of empry vehicle flow on the performance of flexible manufacturing system based on agv transport, European simulati, 1985.

I. Gourgand, M. Tanguy, A. Tchernev, and N. , A generic hybrid flow-shop simulation model using simula, llth European Si,mulati,on Multi,conference, 2000.

. Hboa-]-hoogendoorn and P. Bovy, Gas-kinetic modeling and simulation of pedestrian flows, Transportati, Research Board, pp.0-1662, 2000.

. Hbobl-hoogendoorn and P. Bovy, Modelling multiple userclass traffic flow, Research B, vol.302, pp.123-146, 2000.

S. Hoogendoorn and P. Bovy, Gas-kinetic modeling and simulation of pedestrian flows, Transportati, pp.0-1662, 2000.

. Hoogendoorn and P. Bovy, A new estimation technique for vehicle-type specifi c headway distributio ns, Transportati,on Research Record 1646, p.28, 1998.

D. Heibing, Verkehrsdynam'ik -neue Phgsi'kali'sche M odellieri,ng skon zept e, Sp ring er-Verl a g, 1997.

D. Helbing, Modeling multi-lane traffic flow with queuing effects, Physica A: Statistical Mechanics and its Applications, vol.242, issue.1-2, pp.175-194, 1997.
DOI : 10.1016/S0378-4371(97)00183-0

P. Hansen and B. Jaumard, Cluster analysis and mathematical programming, mathemati,cal programm'ing, pp.9-11, 1997.

B. Lhspesl-hendrickson, L. , and R. , An Improved Spectral Graph Partitioning Algorithm for Mapping Parallel Computations, lHLe5l lHMSesl IHoo99] lHRe5l lHSEe4l lHSLo2I, pp.452-469, 1995.
DOI : 10.1137/0916028

L. E. Haefner and L. Ming-shiun, Traffic flow simulation for an urban freeway corridor, Presented at CrossRoads, 1998.

S. Hoogendoorn, Multiclass continuum modelling of multiclass traffic flow, 1999.

M. Heath and P. Raghavan, A Cartesian Parallel Nested Dissection Algorithm, SIAM Journal on Matrix Analysis and Applications, vol.16, issue.1, pp.235-253, 1995.
DOI : 10.1137/S0895479892238270

H. Haj-salem and N. Elloumi, Metacor : A macroscopic simulation model of corridor traffi.c, and its calibration and validation at the corridor péripherique in paris, 1994.

H. Haj-salem and J. Lebacque, Reconstruction of false and missing data using first order traffic flow models, 2002.

H. Haj-salem and M. Papageorgiou, Metacor : A dynamic macroscopic simulation tool for corridor traffic, Proceedi,ngs oJ C ES A 98-I EEE, 1998.

M. Jepsen, On the speed-flow relationships in road traffic : A model of driver behaviour, Proceedi,ngs of the Thi,rd, International Sympos'ium on H'ighway Capaci, pp.297-379, 1993.

M. Lebeaux and M. , Classi'ficati'on automati,que pour l'analyse des données, 'issue I : Méthodes et algori'thmes, lJD88l IJepe8] BIBLIOGRAPHIE 23r uL78l lJMFeel lKAell [KKe8a] lKKe8bl lKL70l [Koh88] lKPMeel lKRo2l Jambu, 1978.

A. Jain, M. Murty, F. , and P. , Data clustering: a review, ACM Computing Surveys, vol.31, issue.3, p.31, 1999.
DOI : 10.1145/331499.331504

G. Klein and J. Aronson, Optimal clustering : A model and method, Naual research log'isti'cs, pp.447-461, 1991.

G. Karypis and V. Kumar, A Fast and High Quality Multilevel Scheme for Partitioning Irregular Graphs, SIAM Journal on Scientific Computing, vol.20, issue.1, pp.359-392, 1998.
DOI : 10.1137/S1064827595287997

G. Karypis and V. Kumar, Multilevel k-way partitioning scheme for irregular graphs journal of parallel and distributed computing, 1998.

B. Kernighan and S. Lin, An efficient heuristic procedure for partitioning graphs, The Bell System Techni, cal Journal, vol.9307, issue.2, p.29, 1970.

T. Kohonen, Self organ'izati,on and associ,atiue memorE, 1988.

4. Kotsialos, M. Papageorgiou, and A. Messmer, Optimal co-ordinated and integrated motorway network traffic control, Proceedi'ngs of the llth International Symposi'um oJ Transpor'tati,on and Trffic Theory Jerusalem, pp.621-644, 1999.

L. Kisgyorgy and R. Rilett, Travel time prediction by advanced neural network, Peri,odi'ca Polytechnica Ser Ci,ui'l Engi, pp.46-61, 2002.

R. Kuhne, Macroscopic free way model for dense traffic, stop-start waves and incident detection, 1984.

J. Lebacque, Semi-macroscopic simulation of urban traffic, 1984.

J. Lebacque, A two-phase extension of the lwr model based on the boundedness of traffic acceleration, Proceedi,ngs of ISTTT, 2002.

. Legendre, Program k-means, http : / / ww w. f as. umo ntreal. ca/ b i, of fl eg en dre

W. Leutzbach, An introduction to the theory of trffic flow, 1988.

J. Lebacque, H. Haj-salem, J. Lebacque, and M. Khoshyaran, Speed limit control : a problem formulation and theoretical discussions, proceedings of rRIS- TAN IV, First order macroscopic traffic flow models for networks in the context of dynamic assignment , Proceedi,ngs of EURO WGT 95, 1998.

J. Lebacque and J. Lesort, The godunov scheme and what it means for first order traffic flow models, Proceed,ings oJ the 13th Internat'ional Symposi,um of Transportati,on and Traffic Theory Lyon, p.677, 1996.

J. Lebacque and J. Lesort, Macroscopic traffic flow models : a question of order, Proceedi,ngs of the llth Internati,onal Symposi,um of Transportati,on and TrfficTheory Je- rusalem. P., BIBLIOGRAPHIE 233, 1999.

A. Lyrintzis, G. Liu, M. , and P. , Development and comparative evaluation of high-order traffic flow models, Transportati,on Research Record, pp.174-183, 1547.

G. Liu, A. Lyrintzis, M. , and P. , Improved High-Order Model for Freeway Traffic Flow, Transportation Research Record: Journal of the Transportation Research Board, vol.1644, pp.37-46, 1998.
DOI : 10.3141/1644-05

C. Leo and R. Pretty, Numerical simulation of macroscopic continuum traffic models, Transportation Research Part B: Methodological, vol.26, issue.3, pp.207-220, 1992.
DOI : 10.1016/0191-2615(92)90025-R

R. Lipton and R. Tarjan, A Separator Theorem for Planar Graphs, Math'emati'cs, pp.177-189, 1979.
DOI : 10.1137/0136016

M. Lighthill and G. Whitham, On kinematic waves ii : a tlreory of traffic flow on long, crowded roads, Proceedi,ngs of the Royal Soci,ety of London sertes 4, pp.377-345, 1955.

M. V. Maarseveen, The theory of martingales in stochastic systems theory -surveillance and control of freeway traffic flow, 1982.

P. Michalopoulos, D. Beskos, Y. , and Y. , Multilane traffic flow dynamics: Some macroscopic considerations, Transportation Research Part B: Methodological, vol.18, issue.4-5, pp.377-395
DOI : 10.1016/0191-2615(84)90019-5

P. Michaiopoulos, D. Beskos, Y. , and Y. , Analysis of interrupted flow by finite difference methods, Transportation Research B, issue.188, pp.409-427, 1984.

B. Meila, M. Heckerman, and D. , An experimental comparison of several clustering and initializatian methods, Proc. (Jncertai ,nty i,n Artifi,cial Intelligence, pp.386-425, 1998.

J. Marcotorchino, J. M. Proth, and J. And-janesen, Data analysis i,n real li,fe enu,ironmenfs, 1gg5)

L. Magne, S. Rabut, and J. Gabard, Towards an hybrid macro-micro traffic flow simulation model, 2000.

G. Miller, S. Teng, W. Thurston, and S. Vavasis, Automatic mesh partitioning, In sparse Matrir computati,ons : Graph Theory Issues and Algori,thms IMA volumes i,n Mathemati,cs and, i,ts Appl'icat'ions. Spri, 1993.

. Mur95 and F. Murtagh, Interpreting the kohonen self-organizing feature map using contiguity-constrained clustering, Pattern Recogni ,ti,on Letters, pp.399-408, 1995.

G. Miller, Density graphs and separators, 1n second Annual ACM-9IAM symposi,um on Di,screte Argorithms, pp.331-336, 1991.

. Nag96 and K. Nagel, Particle hopping models and traffic flow theory, pp.46-4672, 1996.

P. Nelson, D. Bui, and A. , A novel traffic stream modei deriving from a bimodal kinetic equilibrium, Proceedi'ngs oJ the IFAC conference, pp.799-804, 1997.

. New93 and G. Newell, A simplified theory of kinematic waves in highway traffic, part 1 : General theory, Transportati, Research B, vol.278, issue.4, pp.281-287, 1993.

. Ranka and G. Fox, Fast and parallel mapping algorithms for irregular and adaptive problems, Journal of supercomputi, vol.10, pp.119-140, 1996.

. Pap89 and M. Papageorgiou, Dynamic modelling, assignment, and route guidance in traffic networks, Transportati,on Research B, vol.23, pp.29-48, 1989.

H. Payne, Freflo : A macroscopic simulation model of freeway traffic, Transportati'on Research Record, pp.68-77, 1979.

S. Paveri-fontana, On Boltzmann-like treatments for traffic flow: A critical review of the basic model and an alternative proposal for dilute traffic analysis, Transportation Research, vol.9, issue.4, pp.225-235, 1975.
DOI : 10.1016/0041-1647(75)90063-5

I. Prigogine and R. Herman, Ki,neti,c Theory of Vehicular Trffic, 1971.

L. Pipes, An Operational Analysis of Traffic Dynamics, Journal of Applied Physics, vol.1, issue.3, pp.24274-287, 1953.
DOI : 10.1080/17470214808416741

D. Park and L. C. Rillett, Forecasting multiple period freeway link travel times using modular neural networks, Transportati'on Research Record 1617 National Research counc'il. washi, 1993.

L. Prigogine, A boltzmann-like approach to the statistical theory of traffic flow, 1961.

4. Pothen, H. Simon, and K. Liou, Partitioning Sparse Matrices with Eigenvectors of Graphs, SIAM Journal on Matrix Analysis and Applications, vol.11, issue.3, pp.11-430, 1990.
DOI : 10.1137/0611030

4. Pothen, H. Simon, L. Wang, and S. Barnard, Towards a fast implementation of spectral nested dissection, Proceedings Supercomputing '92, pp.42-47, 1992.
DOI : 10.1109/SUPERC.1992.236711

A. Quere and M. Gourgand, Exact methods and stochastic algorithms to solve an assignation problem in a hybrid flowshop, pp.671-675, 1993.

R. Smith, J. Chou, and E. And-romeijn, Approximating shortest paths in large scale networks with application to intelligent transportation systems, INFORMS Journal on Computi, vol.ng, issue.2, pp.0-163, 1998.

S. Smulders, Control of freeway traffic flow by variable speed signs, Transportati,on Research B 2f, p.732, 1990.

A. Schadschneider and M. Schreckenberg, Cellular automata for traffic flow : Analytical results, Proceedi,ngs of the workshop on Trffic and Granular Flow Jiili'ch

J. Taplin, Simulation models of traffic flow, Slth Annual Conference of the Operati,onal Research Soci'ety of New Zealand, pp.10-11, 1999.

E. Todosiev and L. Barbosa, A proposed model for the driver-vehicle system, Trffic Engi'neeri'ng 31, pp.7-20, 1964.

C. Tampère, S. Hoogendoorn, and B. Arem, Gas kinetic traffic flow modelling including continuous driver behavior model, Preprints of the 82nd Annual Meeti'ng of the Transpor-tation Research Board, 2003.

M. Tleiber, A. Hennecke, and D. Helbing, Derivation properties , and simulation of a gaskinetic-based, non-local traffic modeI , Physi, pp.239-253, 1999.

B. Lrhheel, R. Vermijs, M. Papendrecht, H. Lutje, R. Spelberg et al., Short term forecasting of the level of service on a motorway network, by using a microscopic simulation model, Proceedi ,ngs of the Znd Erasmus-Network conference on transportati,on and trffic engi, 1995.

J. Ward, Hierarchical Grouping to Optimize an Objective Function, Journal of the Ameri,can Stati,sti,cal Assoc'iat'ion, pp.2-6, 1963.
DOI : 10.1007/BF02289263

Q. Yang, H. Koutsopoulos, and M. Ben-akiva, A simulation iaboratory for evaluating dynamic traffic management systems, Transportation Research Board 79th Annual Meeting, 2000.

H. Zhang, A finite difference model of non-equilibrium traffic flow models, Transportati, pp.337-365, 2001.

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