Abstract : In this thesis, we explore two problems related to managing and mining moving object trajectories. First, we study the problem of sampling trajectory data streams. Modern location-aware devices are capable of capturing and transmitting their position at very high rates. Storing the entirety of the trajectories provided by such devices can entail severe storage and processing overheads. Therefore, adapted sampling techniques are necessary in order to discard unneeded positions and reduce the size of the trajectories while still preserving their key spatiotemporal features. In streaming environments, this process needs to be conducted "on- the-fly" since the data are transient and arrive continuously. To this end, we introduce a new sampling algorithm called Spatiotemporal Stream Sampling (STSS). This algorithm is computationally-efficient and guarantees an upper bound for the approximation error introduced during the sampling process. Experimental results show that STSS achieves good performances and can compete with more sophisticated and costly approaches. The second problem we study is clustering trajectory data in road network environments. Most of prior work assumed that moving objects can move freely in an Euclidean space and did not consider the presence of an underlying road network and its influence on evaluating the similarity between trajectories. We present three approaches to clustering such data: the first approach discovers clusters of trajectories that traveled along the same parts of the road network; the second approach is segment-oriented and aims to group together road segments based on trajectories that they have in common; the third approach combines both aspects and simultaneously clusters trajectories and road segments. Through extensive case studies, we show how these approaches can be used to reveal useful knowledge about flow dynamics and characterize traffic in road networks. We also provide experimental results where we evaluate the performances of our propositions.