Automated vehicles (AV) are expected to change our future way of transportation and bring many positive effects, such as better road/vehicle safety, reduced environmental costs but also increased on-board productivity. The commonly adopted approach to capture the effect of increased on-board productivity is to reduce the ‘penalty’ associated with travel time. This implies that people will show less aversion to longer travel times (i.e. congestion), prioritise arriving close to the preferred arrival time and thus, increase the peak congestion. However, a distinction should be made between the type of performed activity, since this could likely affect the departure time preferences and thus can have an impact on the congestion pattern. Previous work has shown that travellers with fully automated vehicles in which home activities could better be performed, preferred to depart earlier, and conversely travellers with automated vehicles in which work activities could better be performed, preferred depart later. In addition, the results suggested that automated vehicles could likely increase severe congestion in the future. However, these insights have been obtained with the use of a theoretical single link setting. Up to this point differentiation between activities has not yet been investigated for real-life road networks in which route choice can be incorporated. Therefore this research aims to investigate network effects of activity based departure time choice with fully automated vehicles. It uses the extended α-β-γ scheduling preferences by B. Pudāne and implemented these within a traffic simulation model. This model was assigned to a case study network of the ‘Haaglanden’ region. Results showed that travellers which use AVs to engage in home activities, increase congestion more to the beginning of the morning peak. Vice versa, travellers which use AVs to engage in work activities move congestion more to the end of the morning peak. Travellers which use AVs for both home and work activities would increase congestion in the middle. Considering route choice, a shift in vehicle-kilomtres (VKM) could be observed from the main road network to the underlying road network with the introduction of AVs. This has a negative impact regarding traffic safety, noise and air pollution. Although one might argue that future AVs will have increased safety levels and a more eco-friendly way of driving. Lastly, it was observed that with mixed traffic situations, non-AV users were ‘forced’ to shift their departure times out of the peak moments, move to less congested periods. Conversely, AV users were less affected by these longer travel times and prioritised their preferred arrival times. It is recommended that further research is conducted to include the dynamic modelling element of blocking back. In addition, the extended α-β-γ model parameter values should be assessed to arrive at more substantiated values. Moreover, it suggested to further investigate the impact of differentiation towards road capacities for AVs. Furthermore, this study did not investigate heterogeneity among travellers’ preferences, nor did it take into account endogeneity through which people may assign themselves to a certain type of activity. It is suggest to take these aspects into account in further research. This study assumed full automation, whereas it would be interesting to investigate the effects with lower automation as well. Lastly, an important extension of this research would be to also capture the effect of value of travel time differentiation with respect to route choice, as this study did not differentiate in probabilities to choose a specific route per vehicle type.