Analysis of big data in the field of transportation.

Summary The objective of this thesis is to propose new methodologies to be applied to public transportation data. Indeed, we are surrounded more and more by sensors and computers generating huge amounts of data. In the public transport domain, contactless cards generate data every time we use them, whether for loading or for our trips. In this thesis, we use this data for two distinct purposes. First, we wanted to be able to detect groups of passengers with similar temporal patterns. To do this, we first used non-negative matrix factorization as a pre-processing tool for classification. Then we introduced the NMF-EM algorithm allowing dimension reduction and classification simultaneously for a mixture model of multinomial distributions. In a second step, we applied regression methods to these data in order to be able to provide a range of these probable validations. Similarly, we applied this methodology to the detection of anomalies on the network.