Fouille de données billettiques pour l'analyse de la mobilité dans les transports en commun

Abstract : Ticketing logs are being increasingly used to analyse mobility in public transport. The spatial and temporal richness as well as the volume of these data make them useful for understanding passenger habits and predicting origin-destination flows. Information on the operations carried out on the transportation network can also be extracted in order to detect atypical events (or anomalies), such as an unusual increase or decrease in the number of validations.This thesis focuses on developing new tools to process ticketing log data. We are particularly interested in two challenges that seem to be not yet fully resolved in the literature: help with data quality as well as the modeling and monitoring of passengers' temporal habits.One of the main challenges in data quality is the construction of a robust methodology capable of detecting atypical situations in any context (day of the week, holidays, public holidays, etc.). To this end, two steps were deployed, namely clustering for context estimation and detection of anomalies. The evaluation of the proposed methodology is conducted on a real dataset collected on the Rennes public transport network. By cross-comparing the obtained results with the social and cultural events of the city, it is possible to assess the impact of these events on transport demand, in terms, of severity and spatial influence on neighboring stations.The second part of the thesis focuses on the modeling and the tracking of the temporal activity of passengers. A Gaussian mixture model is proposed to partition passengers into clusters according to the hours they use public transport. The originality of the methodology compared to existing approaches lies in obtaining continuous time profiles in order to finely describe the time routines of each passenger cluster. Cluster memberships are also cross-referenced with passenger data (card type) to obtain a more accurate description of each cluster. The cluster membership over the years has also been analyzed in order to study how the use of transport evolves
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Anne-Sarah Briand. Fouille de données billettiques pour l'analyse de la mobilité dans les transports en commun. Analyse classique [math.CA]. Université Paris-Est, 2017. Français. ⟨NNT : 2017PESC1235⟩. ⟨tel-01757105⟩

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