In recent years, bike sharing systems have become very popular in many major 1 cities. Thanks to the data they generate, their activity can be tracked down, giving 2 an overall view of how human activities are spread over time and space. We propose 3 in the present article a novel method to extract mobility patterns that occur in such 4 large-scale transportation systems. The trips made by the users are first represented 5 as flows between the different stations of the system, describing a network whose 6 structure evolves over time. A decomposition technique is then proposed using 7 non-negative matrix factorisation, to express the resulting temporal networks as 8 a mixture of sub-networks, each of them characterising the different behaviours 9 of users over time and space. This method is applied on the Lyon’s bike sharing 10 system, and it is emphasised that key spatio-temporal elements of urban activity 11 are retrieved, capturing known phenomena such as commuting. This approach 12 could be easily extended to large-scale transportation systems exhibiting a network 13 structure, paving the way to an unsupervised modelling of mobility patterns. 14
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