  This paper presents two case studies where a macroscopic model-based approach
for traffic state estimation, which we have recently developed, is employed and
tested. The estimation methodology is developed for a "mixed" traffic scenario,
where traffic is composed of both ordinary and connected vehicles. Only average
speed measurements, which may be obtained from connected vehicles reports, and
a minimum number (sufficient to guarantee observability) of spot sensor-based
total flow measurements are utilised. In the first case study, we use NGSIM
microscopic data in order to test the capability of estimating the traffic
state on the basis of aggregated information retrieved from moving vehicles and
considering various penetration rates of connected vehicles. In the second case
study, a longer highway stretch with internal congestion is utilised, in order
to test the capability of the proposed estimation scheme to produce appropriate
estimates for varying traffic conditions on long stretches. In both cases the
performances are satisfactory, and the obtained results demonstrate the
effectiveness of the methodology, both in qualitative and quantitative terms.
