Abstract
Estimating fastest paths on large networks is a crucial problem for dynamic route guidance systems. This chapter presents a data mining based approach for approximating fastest paths on urban road networks. The traffic data consists of input flows at the entry nodes, system state of the network or the number of cars present in various roads of the roads, and the paths joining the various origins and the destinations of the network. To find out the relationship between the input flows, arc states and the fastest paths of the network, we developed a data mining approach called hybrid clustering. The objective of hybrid clustering is to develop IF-THEN based decision rules for determining fastest paths. Whenever a driver wants to know the fastest path between a given origin-destination pair, he/she sends a query into the path database indicating his/her current position and the destination. The database then matches the query data against the database parameters. If matching is found, then the database provides the fastest path to the driver using the corresponding decision rule, otherwise, the shortest path is provided as the fastest path.
A numerical experiment is provided to demonstrate the utility of our approach.
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References
Ambroise, C., Badran, F., Thiria, S., Séze, G.: Hierarchical Clustering of Self-Organizing Maps for Cloud Classification. Neurocomputing 30(1-4), 47–52 (2000)
Awasthi, A., Chauhan, S.S., Parent, M., Proth, J.M.: Partitioning algorithms for large scale urban networks (under submission) (2008)
CISIA, SPAD Reference Manuals, Centre International de Statistique et d’Informatique Appliquées, France (1997)
Cutting, D., Karger, D., Pedersen, J., Tukey, J.W.: Scatter/Gather: a cluster based approach to browsing large document collections. In: Proc. 15th Annual Int. ACM SIGIR Conf., Copenhagen (1992)
Diday, E., Simon, J.C.: Clustering Analysis. In: Fu, K.S. (ed.) Digital Pattern Recognition, pp. 47–94. Springer, Heidelberg (1976)
Diday, E., Govaert, G., Lechevallier, Y., Sidi, J.: Clustering in pattern recognition. In: 4th International Joint Conference on Pattern Recognition, Kyoto, Japan (1978)
Diday, E., Lemaire, J., Pouget, P., Testu, F.: Elements d’analyse des donnes, Dunod, Paris (1983)
Dougherty, M.S.: A review of neural networks applied to transport. Transportation Research 3(4), 247–260 (1995)
Everitt, B.S.: Cluster Analysis. Heinemann Educational Books (1974)
Hansen, P., Jaumard, B.: Cluster analysis and mathematical programming. Mathematical Programming 79, 191–215 (1997)
Jain, A.K., Murty, M.N., Flynn, P.J.: Data Clustering: A Review. ACM Computing Surveys 31(3), 264–323 (1999)
Kisgyorgy, L., Rillett, L.R.: Travel Time prediction by advanced neural network. Periodica Polytechnica Ser Civil Engineering 46(1), 15–32 (2002)
Kohonen, T.: Self organization and associative memory, 2nd edn. Springer, Berlin (1988)
Klein, G., Aronson, J.E.: Optimal Clustering: a model and method. Naval research Logistics 38, 447–461 (1991)
Legendre, P.: Program K-means (2001), http://www.fas.umontreal.ca/biol/legendre/
Marcotorchino, J.F., Proth, J.M., Janesen, J.: Data analysis in real life environments. North Holland, Amsterdam (1985)
Meila, M., Heckerman, D.: An experimental comparison of several clustering and initialization methods. In: Proc. Uncertainty in Artificial Intelligence, pp. 386–395 (1998)
Murtagh, F.: Interpreting the Kohonen self-organizing feature map using contiguity-constrained clustering. Pattern Recognition Letters 16, 399–408 (1995)
Park, D., Rillett, L.R.: Forecasting multiple period freeway link travel times using modular neural networks, Transportation Research Record, 1617, National Research Council., Washington D.C (1998)
Smith, R., Chou, J., Romeijn, E.: Approximating Shortest Paths in Large Scale Networks with Application to Intelligent Transportation Systems. INFORMS Journal on Computing 10(2), 163–179 (1998)
Tomassone, R., Danzart, M., Daudin, J.J., Masson, J.P.: Discrimination et classement, Masson, Paris (1988)
Roux, M.: Algorithmes de classification, Masson, Paris (1986)
Ward, J.H.: Hierarchical grouping to optimize an objective function. Journal of the American Statistical Association 58, 236–244 (1963)
Zhan Benjamin, F.: Three Fastest Shortest Path Algorithms on Real Road Networks: Data Structures and Procedures. Journal of Geographic Information and Decision Analysis 1(1), 70–82 (1997)
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Awasthi, A., Chauhan, S.S., Parent, M., Lechevallier, Y., Proth, J.M. (2009). A Data Mining Approach for Adaptive Path Planning on Large Road Networks. In: Abraham, A., Hassanien, AE., de Leon F. de Carvalho, A.P., Snášel, V. (eds) Foundations of Computational, IntelligenceVolume 6. Studies in Computational Intelligence, vol 206. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01091-0_13
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DOI: https://doi.org/10.1007/978-3-642-01091-0_13
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