  In an intelligent transportation system, the effects and relations of traffic
flow at different points in a network are valuable features which can be
exploited for control system design and traffic forecasting. In this paper, we
define the notion of causality based on the directed information, a
well-established data-driven measure, to represent the effective connectivity
among nodes of a vehicular traffic network. This notion indicates whether the
traffic flow at any given point affects another point's flow in the future and,
more importantly, reveals the extent of this effect. In contrast with
conventional methods to express connections in a network, it is not limited to
linear models and normality conditions. In this work, directed information is
used to determine the underlying graph structure of a network, denoted directed
information graph, which expresses the causal relations among nodes in the
network. We devise an algorithm to estimate the extent of the effects in each
link and build the graph. The performance of the algorithm is then analyzed
with synthetic data and real aggregated data of vehicular traffic.
