  Stop-and-go traffic poses many challenges to tranportation system, but its
formation and mechanism are still under exploration.however, it has been proved
that by introducing Connected Automated Vehicles(CAVs) with carefully designed
controllers one could dampen the stop-and-go waves in the vehicle fleet.
Instead of using analytical model, this study adopts reinforcement learning to
control the behavior of CAV and put a single CAV at the 2nd position of a
vehicle fleet with the purpose to dampen the speed oscillation from the fleet
leader and help following human drivers adopt more smooth driving behavior. The
result show that our controller could decrease the spped oscillation of the CAV
by 54% and 8%-28% for those following human-driven vehicles. Significant fuel
consumption savings are also observed. Additionally, the result suggest that
CAVs may act as a traffic stabilizer if they choose to behave slightly
altruistically.
