Automated vehicle technology can be beneficial for many aspects of transport, especially, improving traffic flow stability and efficiency. However, the influence of different automated driving styles on traffic efficiency is still not fully understood. Transport systems are very complex and non-linear, i.e. many participants with different characteristics interact with each other and the aggregated result of their interactions could cause a remarkable change in the entire network. Considering that automated vehicles with different driving styles interact with the environment in different ways, we try to understand the influence of different automated driving styles (e.g., cautious, normal, aggressive) on the important variables in traffic flow theory (e.g., speed) to reveal their impact on network efficiency. Characteristics of these driving styles are extracted by clustering the highD dataset and then, translated into different car-following models for simulation in the SUMO traffic simulator environment. Multiple scenarios of mixed traffic conditions (i.e. ranging different ratios of driving styles) are simulated on the network of Munich inner city.