This thesis investigates the integration of vehicles with differing levels of automation and connectivity within suburban traffic systems, focusing on their impact on road safety, traffic efficiency, and risk, particularly concerning vulnerable road users. By employing a Cooperative and Connected Automotive Mobility (CCAM) framework, the study examines how vehicles that share real-time information and intentions under different CCAM configurations influence the dynamics of suburban mobility. Utilizing simulation tools like SUMO and Artery, this research conducts multiple traffic scenario simulations to capture the interactions between automated, connected, and conventional vehicles. The simulations specifically target the implementation of Intelligent Transport Systems (ITS) protocols such as ETSI ITS-G5, directed by the European standard, assessing their efficacy in fostering safer and more efficient suburban environments. The parameters used to determine the performance of a scenario are number of emergency brakes, collisions, average vehicle speed, average relative speed and ratio of departed speed. The findings aim to provide actionable insights into deploying advanced vehicular technologies, ensuring their beneficial integration into increasingly complex suburban traffic networks, thus supporting global road safety initiatives like Vision Zero. This project shows that safety-wise, the general mix of vehicles that provide the safest traffic conditions are the heterogenous mix of 50% automated, with higher levels of connectivity contributing to better metric scores from an efficiency standpoint.