  This paper studies the energy and traffic impact of a proposed Anticipative
Cruise Controller in a PTV VISSIM microsimulation environment. We dissect our
controller into two parts: 1. the anticipative mode, more immediately
beneficial when automated vehicle fleet penetration is low, and 2. the
connected mode, beneficial in coordinated car-following scenarios and high
automated vehicle penetrations appropriate for autonomous vehicle specific
applications. Probabilistic constraints handle safety considerations, and
vehicle constraints for acceleration capabilities are expressed through the use
of powertrain maps. Real traffic scenarios are then modeled using time headway
distributions from traffic data. To study impact over a range of demands, we
vary input vehicle volume from low to high and then vary automated vehicle
penetration from low to high. When examining all-human driving scenarios,
network capacity failed to meet demand in high-volume scenarios, such as
rush-hour traffic. We further find that with automated vehicles introduced
which utilize probabilistic constraints to balance safety and traffic
compactness, network capacity was improved to support the high-volume
scenarios. Finally, we examine energy efficiencies of the fleet for
conventional, electric, and hybrid vehicles. We find that automated vehicles
perform at a 10% - 20% higher energy efficiency over human drivers when
considering conventional powertrains, and find that automated vehicles perform
at a 3% - 9% higher energy efficiency over human drivers when considering
electric and hybrid powertrains. Due to secondary effects of smoothing traffic
flow, energy benefits also apply to human-driven vehicles that interact with
automated ones. Such simulated humans were found to drive up to 10% more
energy-efficiently than they did in the baseline all-human scenario.
