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Being one of the most promising applications enabled by connected andautomated vehicles (CAV) technology, Cooperative Adaptive Cruise Control (CACC)is expected to be deployed in the near term on public roads.} Thus far, themajority of the CACC studies have been focusing on the overall networkperformance with limited insights on the potential impacts of CAVs onhuman-driven vehicles (HVs).This paper aims to quantify such impacts bystudying the high-resolution vehicle trajectory data that are obtained frommicroscopic simulation. Two platoon clustering strategies for CACC- an ad hoccoordination strategy and a local coordination strategy-are implemented.Results show that the local coordination outperforms the ad hoc coordinationacross all tested market penetration rates (MPRs) in terms of networkthroughput and productivity. According to the two-sampleKolmogorov-\textcolor{re}{Smirnov} test, however, the distributions of the hardbraking events (as a potential safety impact) for HVs change significantlyunder local coordination strategy. For both of the clustering strategy, CAVsincrease the average lane change frequency for HVs. The break-even point foraverage 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 changefrequency following a monotonically increasing pattern in response to MPR, andit reaches the highest 5.48 per vehicle at 40% MPR. Lastly, the interactionstate of the car-following model for HVs is analyzed. It is revealed that thecomposition of the interaction state could be influenced by CAVs as well. Oneof the apparent trends is that the time spent on approaching state declineswith the increasing presence of CAVs.

Influence of CAV Clustering Strategies on Mixed Traffic Flow Characteristics: An Analysis of Vehicle Trajectory Data