  In this paper we present a numerical study on control and observation of
traffic flow using Lagrangian measurements and actuators. We investigate the
effect of some basic control and observation schemes using probe and actuated
vehicles within the flow. The aim is to show the effect of the state
reconstruction on the efficiency of the control, compared to the case using
full information about the traffic. The effectiveness of the proposed state
reconstruction and control algorithms is demonstrated in simulations. They show
that control using the reconstructed state approaches the full-information
control when the gap between the connected vehicles is not too large, reducing
the delay by more than $60\%$ when the gap between the sensor vehicles is
$1.25$~km on average, compared to a delay reduction of almost $80\%$ in the
full-information control case. %It shows that the reconstruction is well
achieved if the distance between two connected and autonomous vehicles is not
too large (more than 1 vehicle per kilometer). %We also show that it is
possible to further improve the control performance by employing a simple
adaptive probe vehicle selection scheme. Moreover, we propose a simple scheme
for selecting which vehicles to use as sensors, in order to reduce the
communication burden. Numerical simulations demonstrate that %It also
demonstrates that it is possible to reduce the communication burden using a
triggering mechanism with with this triggering mechanism, the delay is reduced
by around $65\%$, compared to a reduction of $72\%$ if all connected vehicles
are communicating at all times.
