  Being one of the most promising applications enabled by connected and
automated vehicles (CAV) technology, Cooperative Adaptive Cruise Control (CACC)
is expected to be deployed in the near term on public roads.} Thus far, the
majority of the CACC studies have been focusing on the overall network
performance with limited insights on the potential impacts of CAVs on
human-driven vehicles (HVs).This paper aims to quantify such impacts by
studying the high-resolution vehicle trajectory data that are obtained from
microscopic simulation. Two platoon clustering strategies for CACC- an ad hoc
coordination strategy and a local coordination strategy-are implemented.
Results show that the local coordination outperforms the ad hoc coordination
across all tested market penetration rates (MPRs) in terms of network
throughput and productivity. According to the two-sample
Kolmogorov-\textcolor{re}{Smirnov} test, however, the distributions of the hard
braking events (as a potential safety impact) for HVs change significantly
under local coordination strategy. For both of the clustering strategy, CAVs
increase the average lane change frequency for HVs. The break-even point for
average lane change frequency between the two strategies is observed at 30%
MPR, which decreases from 5.42 to 5.38 per vehicle. The average lane change
frequency following a monotonically increasing pattern in response to MPR, and
it reaches the highest 5.48 per vehicle at 40% MPR. Lastly, the interaction
state of the car-following model for HVs is analyzed. It is revealed that the
composition of the interaction state could be influenced by CAVs as well. One
of the apparent trends is that the time spent on approaching state declines
with the increasing presence of CAVs.
