  Research in transportation frequently involve modelling and predicting
attributes of events that occur at regular intervals. The event could be
arrival of a bus at a bus stop, the volume of a traffic at a particular point,
the demand at a particular bus stop etc. In this work, we propose a specific
implementation of probabilistic graphical models to learn the probabilistic
dependency between the events that occur in a network. A dependency graph is
built from the past observed instances of the event and we use the graph to
understand the causal effects of some events on others in the system. The
dependency graph is also used to predict the attributes of future events and is
shown to have a good prediction accuracy compared to the state of the art.
