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.

As the demand for transport of goods and passengers increases, the capacity of railway networks is becoming more and more saturated. Railway infrastructure managers aim to increase the rail network capacity with the minimum possible investments in infrastructure. Next generation signalling concepts such as Moving Block (MB) and Virtual Coupling (VC) are expected to bring significant capacity benefits to railway corridors. The aim of this thesis is to identify capacity benefits of Virtual coupling over ETCS L3 MB considering a realistic operational setup, where different rolling stock characteristics, driving behavior and traffic information updates exist. Inspired by safety distance car-following models, the multi-state train-following model developed by Quaglietta et al. (2020) is enhanced with a dynamic term, called Dynamic Safety Margin (DSM). The DSM ensures that the following train can come to a standstill at any time applying service braking, even in the worst-case scenario when the leading train applies emergency braking. The DSM accounts for positioning errors, the Vehicle-to-Vehicle (V2V) communication update delay, different train control delay times, and braking capabilities of trains running as a convoy. An One-at-a-time (OAT) sensitivity analysis of the proposed model is conducted to evaluate the importance of selected model parameters in railway capacity. The model parameters under investigation concern the V2V update delay, the train control delay time, and the braking rate of the following train. The braking rate of the following train is proved to have a significant impact on railway capacity, implying that trains with better braking performance imply shorter time headways. To identify potential capacity benefits of the proposed model over ETCS L3 MB, operational scenarios are executed through microscopic simulation in the EGTRAIN tool. The scenarios consider three different rolling stock types, being suburban, regional, and intercity trains. For trains running on open track without stops, the proposed model promises the highest capacity benefits over ETCS L3 MB, for trains with improved braking and acceleration characteristics. For train services with stops, all the train convoy combinations show promising capacity benefits, which are higher for heterogeneous train convoys. Moreover, headways can be reduced to the minimum possible when faster trains follow slower trains. Finally, this model enables multiple train convoy formation based on a predecessor-follower communication topology, where a safety distance is always ensured between trains.