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All this data, from the raw video data to the high level interepretation, need to be organized and managed. A high level conceptual data model is presented as an entity-association diagram in Figure~\ref{fig:data-model}. It is a little simplified to avoid some implementation details which can be found in the Traffic Intelligence project. The diagram has two main parts:  \begin{itemize}  \item the entities (objects) resulting from the video analysis (upper half of Figure~\ref{fig:data-model}): the raw data extracted from video analysis is stored as time series (indexed by frame number) of positions and velocities (with corresponding unique keys), and are grouped as road user objects which may have a type (passenger vehicle, truck, cyclist, etc.). Interactions are composed of two road users and may be characterized by several indicators such as TTC, PET, etc.  \item the entities providing the data description or meta data (lower half of Figure~\ref{fig:data-model}): sites, e.g. the different roundabouts studied in this work, are the corner stone of the meta-data. They may correspond to several video sequences (the actual video files), each being characterized by a camera view with camera calibration information parameters  such as a homography matrix (but a camera view can be the same for several files, e.g. when video sequences are split hour by in several files for each  hour). Various types of site features (complementary data) may be added, e.g. the alignments and analysis areas shown as examples in the figure. \end{itemize}  Positions and road users are obviously linked to a camera view, a video sequence and a site (no through an actual key in the postions table as shown in Figure~\ref{fig:data-model}, but through configuration files).