This master thesis project analyses the impact of vehicle automation on a public transport system and private vehicles sharing a common transportation network. The main objectives of the report were to demonstrate the impacts of automation, determine the relevance of different policy instruments for mitigating the impacts of automation, and create a macroscopic model adept at performing this analysis. The model built and used for analysis was created using the combination of two previously built models, developed by Daganzo (2010) and Badia and Jenelius (2021). The combined models optimise the public transport system given specified route spacing and headway values. The four policy instruments used for analysis were congestion charging, fuel tax, vehicle ownership tax, and speed limitation. The analysis of the model results found that the effectiveness of the different policy instruments implemented varies depending on the automation and electrification level. Furthermore, there was found to be a significant difference in influence of the policy instrument depending on the evaluation factor used. The report concludes that congestion charging was determined to be the most effective policy instrument analysed. Though, the most efficient policy instrument, in terms of effectiveness per unit increase, was the fuel tax. This finding, however, requires that other considerations be accounted for, such as social acceptability, and since this falls outside the scope of this report, that is all that can be concluded.