  This paper addresses the two problems of flow and density reconstruction in
Road Transportation Networks with heterogeneous information sources and cost
effective sensor placement. Following standard macroscopic modeling approaches,
the network is partitioned in cells, whose density of vehicles changes
dynamically in time according to first order conservation laws. The first
problem is to estimate the flow and the density of vehicles using two sources
of information, namely standard fixed sensors, precise but expensive, and
Floating Car Data, less precise due to low penetration rates, but already
available on most of the main roads. A data fusion algorithm is proposed to
merge the two sources of information for observing density and flow of
vehicles. The second problem is to place the sensors in the network by trading
off between cost and performance. A relaxation of the problem is proposed based
on the concept of Virtual Variances. The efficiency of the proposed strategies
is shown on a synthetic regular grid and in the real world scenario of Rocade
Sud in Grenoble, France, a ring road 10.5 km long.
