A Scalable Approach for Short-Term Predictions of Link Traffic Flow by Online Association of Clustering Profiles

1. Attanasi, A. et al. (2017) A hybrid method for real-time short-term predictions of traffic flows in urban areas, IEEE International Conference on MODELS and Technologies for Intelligent Transportation Systems IEEE, pp. 878-883, 2017Search in Google Scholar

2. Chun, F. (1997) Spectral graph theory, CBMS Regional Conference Series in Mathematics, 92. Conference Board of the Mathematical Sciences, Washington. 1997.Search in Google Scholar

3. David, A. and Vassilvitskii, S. (2007) k-means++: The advantages of careful seeding. Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms, Society for Industrial and Applied Mathematics.Search in Google Scholar

4. Dueck, D., Frey, B. J., Jojic, N., Jojic, V., Giaever, G., Emili, A., Musso, G., Hegele, R. (2008a) Constructing treatment portfolios using affinity propagation. In: Vingron, M., Wong, L. (eds.) RECOMB 2008. LNCS, 4955, pp. 360–371. Springer, Heidelberg (2008).Search in Google Scholar

5. Dueck, D., Frey, B. J. (2007a) Non-metric affinity propagation for unsupervised image categorization. In: Proceedings of the IEEE 11th International Conference on Computer Vision (ICCV ‘11), October 2007, Rio de Janeiro, Brazil. IEEE Press, pp. 1–8.10.1109/ICCV.2007.4408853Search in Google Scholar

6. Dueck, D. et al. (2008b) Constructing Treatment Portfolios Using Affinity Propagation, International Conference on Research in Computational Molecular Biology (RECOMB), March 2008, Singapore.Search in Google Scholar

7. Dueck, D. and Frey, B. J. (2007b) Non-metric affinity propagation for unsupervised image categorization, International Conference on Computer Vision (ICCV), October 2007, Rio de Janeiro, Brazil10.1109/ICCV.2007.4408853Search in Google Scholar

8. Ester, M., Kriegel, H. P., Sander, J. and Xu, X. (1996) A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise. In: Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining, Portland, OR, AAAI Press, pp. 226-231.Search in Google Scholar

9. Gentile, G., Meschini, L. (2011) Using dynamic assignment models for real-time traffic forecast on large urban networks. In: Proceedings of the 2nd International Conference on Models and Technologies for Intelligent Transportation Systems, 2011, Leuven, Belgium.Search in Google Scholar

10. Lazic, N., Givoni, I. E., Aarabi, P. and Frey, B. J. (2009) FLoSS: Facility Location for Subspace Segmentation, 12th International Conference on Computer Vision (ICCV), October 2009, Kyoto, Japan.10.1109/ICCV.2009.5459302Search in Google Scholar

11. McAfee, A., Brynjolfsson, E. (2012) Big data: The management revolution, Harvard Business Review, 90(10), pp. 60-68.Search in Google Scholar

12. Soni Madhulatha, T. (2012) An Overview on Clustering Methods, IOSR Journal of Engineering, 2(4), 2012, April, pp. 719-725.Search in Google Scholar

13. Transport for London (2016). Traffic Modelling Guidelines Version 3.0, http://content.tfl.gov.uk/traffic-modelling-guidelines.pdf, Retrieved 10-March-2016Search in Google Scholar

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