Vehicle automation has the potential to disrupt the status quo of urban transportation, because it adds a new mode of transportation by taking away the task of the driver. The projections of estimations of the impacts of this new technology are based on many uncertainties and are thus largely unknown. A literature review showed that a wide number of effects is possible with automated driving and that no straightforward method to assess future impacts of this new technology exists yet. This thesis developed a method that provides insights into the impacts of vehicle automation in urban areas, without preconceived ideas about the impacts of the different scenarios. Existing knowledge from literature, transportation and land use data, and sociodemographic information were combined in a geographically disaggregated System Dynamic model. This model explored the effects of vehicle automation on the performance of the transportation and spatial system of the case-study city of Copenhagen, Denmark. Different model runs provided insight in the possible range of outcomes. Considerable problems may arise in the transportation network with the introduction of automated driving because, using the car might become very attractive. A city’s land use does, however, not change as much as many could expect. The causes of (un)desirable outcomes were identified with the Patient Rule Induction Method. The ranges of uncertainties in the value of time in an automated vehicle and in the level of adoption of car-sharing were found to influence desirable versus undesirable futures the most. Mitigating measures should focus on these scenarios to prepare for a future with automated driving.

When traffic demand is high and the intersection lacks throughput, oversaturated traffic phenomena occur, such as the oversaturation of a turning bay or intersection link. When such unsafe situations emerge, control strategies can be applied to handle or avoid these spillback effects. In this paper, a spillback component is designed that detects and controls spillback in a real-time controlled traffic system (RTCS) that optimizes multiple vehicle types in its optimization, with a high vehicle priority for the light-rail vehicle (LRV). A spillback control strategy was developed that assigns additional priority to the congested link when spillback is detected. For assessment, a simulation study is conducted to gain insight into the effects of applying spillback control strategies on the traffic and network performance at an isolated intersection and in a corridor. The results showed that the intersection performance decreased when a spillback control strategy was applied in the optimization, but the network performance increased during the peak period. Furthermore, a spillback penalty factor effectively decreases spillback durations while it works well in combination with higher prioritized public transit. A too-high priority negatively impacts the intersection and network performance. However, in some cases, a (high) spillback priority is necessary to provide enough priority so that a growing unsafe situation is avoided. Therefore, it is crucial to determine the intended use and applicability when providing spillback priority. The spillback priority is one of the few priorities that can be configured in the utilized RTCS; it is recommended to research the impacts when varying priorities are used.