Tracking the evolution of temporal patterns of usage in bicycle-Sharing systems using nonnegative matrix factorization on multiple sliding windows

References

• Bartlett, M. S., Movellan, J. R., & Sejnowski, T. J. (2002). Face recognition by independent component analysis. IEEE Transactions on Neural Networks, 13(6), 1450–1464. doi: 10.1109/TNN.2002.804287
   PubMed Web of Science ®Google Scholar
• Etienne, C., & Latifa, O. (2014). 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), 5(3), 39.
   Web of Science ®Google Scholar
• Froehlich, J., Neumann, J., & Oliver, N. 2009). Sensing and predicting the pulse of the city through shared bicycling. (vol. 9, pp. 1420–1426). Pasadena: IJCAI.
   Google Scholar
• Févotte, C., & Idier, J. (2011). Algorithms for nonnegative matrix factorization with the β-divergence. Neural Computation, 23(9), 2421–2456. doi: 10.1162/NECO_a_00168
   Web of Science ®Google Scholar
• Golder, S. A., Wilkinson, D. M., & Huberman, B. A. (2007). Rhythms of social interaction: Messaging within a massive online network. Communities and technologies 2007 (pp. 41–66). Ann Arbor, MI: Springer.
   Google Scholar
• Guo, L., Tan, E., Chen, S., Zhang, X., & Zhao, Y. E. (2009). Analyzing patterns of user content generation in online social networks. Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 369–378). Paris: ACM.
   Google Scholar
• Hamon, R., Borgnat, P., Févotte, C., Flandrin, P., & Robardet, C. (2015). Factorisation de réseaux temporels: Étude des rythmes hebdomadaires du système vélo'v. Colloque GRETSI 2015.
   Google Scholar
• Hamon, R., Borgnat, P., Flandrin, P., & Robardet, C. (2014). Nonnegative matrix factorization to find features in temporal networks. 2014 IEEE international conference on acoustics, speech and signal processing (ICASSP) (pp. 1065–1069). Florence: IEEE.
   Google Scholar
• Han, Y., & Moutarde, F. (2011). Analysis of network-level traffic states using locality preservative non-negative matrix factorization. 2011 14th international IEEE conference on Intelligent Transportation Systems (ITSC) (pp. 501–506). Washington, DC: IEEE.
   Google Scholar
• Kullback, S., & Leibler, R. A. (1951). On information and sufficiency. The Annals of Mathematical Statistics, 22(1), 79–86. doi: 10.1214/aoms/1177729694
   Google Scholar
• Lee, D. D., & Seung, H. S. (1999). Learning the parts of objects by non-negative matrix factorization. Nature, 401(6755), 788–791. doi: 10.1038/44565
   PubMed Web of Science ®Google Scholar
• Lin, C.-J. (2007). Projected gradient methods for nonnegative matrix factorization. Neural Computation, 19(10), 2756–2779. doi: 10.1162/neco.2007.19.10.2756
   PubMed Web of Science ®Google Scholar
• Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., … Duchesnay, E. (2011). Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 12, 2825–2830.
   Web of Science ®Google Scholar
• Peng, C., Jin, X., Wong, K.-C., Shi, M., & Liò, P. (2012). Collective human mobility pattern from taxi trips in urban area. PLoS One, 7(4), e34487. doi: 10.1371/journal.pone.0034487
   PubMed Web of Science ®Google Scholar
• Sagl, G., Resch, B., Hawelka, B., & Beinat, E. (2012). From social sensor data to collective human behaviour patterns: Analysing and visualising spatio-temporal dynamics in urban environments. Proceedings of the GI-forum (pp. 54–63). Salzbourg: Wichmann-Verlag.
   Google Scholar
• Tran, T. D., Ovtracht, N., & d'Arcier, B. F. (2015). Modeling bike sharing system using built environment factors. Procedia CIRP, 30, 293–298. doi: 10.1016/j.procir.2015.02.156
   Google Scholar
• Vogel, M., Hamon, R., Lozenguez, G., Merchez, L., Abry, P., Barnier, J., … Robardet, C. (2014). From bicycle sharing system movements to users: A typology of Vélo'v cyclists in Lyon based on large-scale behavioural dataset. Journal of Transport Geography, 41, 280–291. doi: 10.1016/j.jtrangeo.2014.07.005
   Web of Science ®Google Scholar
• Xu, W., Liu, X., & Gong, Y. (2003). Document clustering based on non-negative matrix factorization. Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval (pp. 267–273). Toronto: ACM.
   Google Scholar
• Yang, J., & Leskovec, J. (2013). Overlapping community detection at scale: A nonnegative matrix factorization approach. Proceedings of the sixth ACM international conference on Web search and data mining (pp. 587–596). Rome: ACM.
   Google Scholar