Shared Automated Vehicles (SAVs) hold significant potential to redefine urban mobility services. However, the applicability and optimization of their operational strategies in diverse urban contexts remain unclear, particularly concerning the complex interactions with existing public transport systems. This study systematically investigates how urban heterogeneity, including city scale, network topology, and demand patterns, modulates SAV fleet management strategies to balance operator costs and user utility within a multi-modal transportation context.

To this end, this study develops a comprehensive simulation framework that combines an advanced ride-pooling candidate generation algorithm (ExMAS) with an innovative two-stage vehicle assignment optimization process, and integrates a nested Logit model to quantify the competition structure with public transport. This framework is applied to 37 Dutch cities of varying scales to derive generalizable findings.

The study finds that population scale is a fundamental determinant of the required fleet size, exhibiting a strong linear relationship, especially in large cities. The key to enhancing operational efficiency, however, lies in more nuanced urban structure metrics. In large cities, longer commuting distances combined with more complex networks (higher node degrees) foster ride-pooling potential. In contrast, for small cities, the local compactness of the network (higher clustering coefficient) and demand density are critical for improving vehicle turnover efficiency. Furthermore, the research confirms that a uniform pricing strategies are unlikely to achieve best performance on pooling efficiency, highlighting the necessity of implementing differentiated pricing based on user preferences and city scale. In competition with public transport, the study's Public Transport Competitiveness Index reveals a non-monotonic relationship with travel distance. A state of competitive balance is observed for short-distance trips, while PT holds a distinct advantage in the medium-distance range. For long-distance trips, the inherent speed advantage of SAVs allows them to become the more competitive option. This external competition also structurally increases the internal pooling rate of the SAV system.

The findings of this study provide critical strategic insights for SAV operators and policymakers, emphasizing the critical importance of adjusting fleet management, service design, and pricing strategies according to specific urban characteristics and the competitive environment, thereby providing decision support for achieving an efficient and sustainable urban transportation system.

Human factor models has been an increasingly more popular topic in traffic models. The objective of these models vary, from simulating cooperative driving to understanding the behaviour of distracted drivers. Regardless of these diverse objectives, the reasons motivating these researches boil down to one single reason, safety. By better understanding human behaviour it should be possible to increase the safety of drivers on the road. One model which offers a systematic approach of studying human factors is the task-capability interface (TCI) model, it models the underlying human thought process and uses it as a proxy for other human factors. This has made the model quite successful in replicating various human factors, including distractions. Multiple papers have studied distractions using the TCI model as a tool but they all had their own specific approach to distractions. This leads to the identified gap in literature: how can distraction be systematically modelled in a TCI traffic model.\\
To fill this gap a distraction framework has been developed. This framework relies on the low-level characteristics of distractions and separates their lifecycle into three stages. These stages are the distraction trigger, intensity and effect. In order to verify if this framework is capable of systematically and accurately modelling distractions it was subjected to a validation test. To this end the distraction framework was incorporated into the Multi-scale model, which was found to be the most fitting TCI model, this resulted in the Distraction model. The new Distraction model was subsequently calibrated with a genetic algorithm to two different datasets with vastly different distraction, a continuous mental-visual distraction and a spontaneous auditory distraction. The results were compared to the results of specialized Distraction models.\\
The validation test results show that the Distraction model has shown limited improvements over the specialized baseline models and that most of the time its performance is equivalent. To be more specific the Distraction model is significantly better at estimating the headway of drivers compared to the baseline models when calibrating for single drivers. That said when it's used as a calibrated model it loses this edge and its performance is fully equivalent to the baseline models. With these results it can be concluded that the distraction framework functions as intended. Despite the limited amount of different distractions in the validation test it has shown that it is capable of systematically modelling distraction on a similar level as other specialized models. This also shows that the main benefits of the framework are its systematic approach and flexibility and not its performance capabilities.

This study examines the impact of Adaptive Cruise Control (ACC) systems on traffic flow efficiency and road safety, with a particular emphasis on transition control between automated and manual driving. Empirical data from the OpenACC database and the SAE Level 2 naturalistic driving study were analyzed to capture key ACC characteristics, which were then used in simulations to reflect the variability among different ACC systems. Simulations were conducted on a section of the Dutch A13 highway, with ACC market penetration rates (MPRs) set at 25% and 75%. The ACC and transition control models were integrated as external driver models in PTV VISSIM under the assumption of nearly ideal human driver responses, excluding potential response delays and the string instability typically associated with ACC systems.

The results indicate that ACC systems with transition control models significantly enhance traffic flow efficiency and safety, particularly at higher MPRs. The introduction of ACC vehicles reduced congestion and increased time-to-collision (TTC) values, reflecting improved traffic safety. However, the study highlights limitations, including simplified human driving behaviors and the exclusion of string stability effects. Future research should focus on more complex driving scenarios, such as urban environments, and enhance data collection methods to further understand ACC system performance in diverse traffic conditions.

Traffic congestion is a challenge that frequently emerges due to changes in the roadways such as tunnels and sags, causing capacity reduction. The capacity drop phenomenon exacerbates traffic congestion, due to decreased queue discharge rates. Among the strategies employed for traffic management, Variable Speed Limit (VSL) control is a common approach to alleviate congestion and mitigate capacity drop. The control is expected to be more powerful when integrated with Connected Vehicles (CVs). However, the intricate interplay between the Market Penetration Rate (MPR) of CVs and the parameters governing VSL remains under-explored.
This study seeks to quantify the relationship between the MPR of CVs and the key VSL parameters, encompassing factors like the optimal acceleration length and speed limits. The VSL control is applied to CVs at the road section upstream of a tunnel bottleneck modelled by a continuum car-following model with bounded acceleration. The findings of this research suggest a minimum MPR of CVs essential for preventing capacity drop and reaching the maximal outflow. Intriguingly, this challenges the conventional notion that regulating solely the leading vehicle suffices to govern the behaviours of all following vehicles. The value of the minimum MPR threshold is affected by the acceleration behaviours vehicles exhibit.
Furthermore, this study underscores the importance of considering the MPR of CVs when devising the optimal speed limit and acceleration length for the VSL. It shows that while a higher speed limit can lead to higher throughput, the optimal acceleration length increases exponentially with the increasing speed limit, particularly in cases with low MPR of CVs. While adopting a relatively lower speed limit, the acceleration length can be reduced to 0m for all levels of MPR of CVs. In summary, it suggests that when implementing VSL in practice, a balance between the resulted throughput, the required acceleration length and the robustness of VSL across different levels of MPR of CVs should be considered.

This thesis aims to answer the question "What are the properties of a resilient road network?" The resilience of a road network indicates the magnitude and consequences of a disruption, which can be events such as a traffic accident or a flood. The literature is not in agreement about the exact definition of road network resilience, and the way to quantify it. A literature search is done to look for definitions and resilience metrics. Eleven metrics are found that quantify resilience in road networks to accidents. These eleven metrics are compared based on whether they are on a network level, and how many variables are used. Two metrics are chosen, one based on travel time, and one based on space-mean flow. Another metric, based on the outflow of the network is added.

The three chosen metrics are then compared in small networks to see if they give the same results. The small network consist of five nodes with different link configurations. The conclusion about which network is the most resilient changes with the resilience metrics. The metric based on travel time is deemed the best for this type of research. The metric based on network outflow can have distorted results due to high peaks, and the metric based on space-mean flow is better suited for a different experiment design.

To see how network parameters such as link density influence the resilience, a simulation with random networks is done. 266 networks with nine nodes in random locations, and random links between them are simulated. It is concluded that networks with a lower link density have a higher resilience. This is not only illustrated by the relation between resilience and link density, but also by several other network parameters which are related to link density such as average number of lanes and connectivity. A reason that these networks are more resilient could be that there is less spillback, the links in the high density networks are longer and have more lanes, so congestion will not spread to other links as fast.

With the steadily increasing road traffic demand, congestion on freeways has become a major problem. With the development of communication technology and automated vehicles, there are more opportunities for DTMs to be studied extensively at the coordination level and cooperation level. In the existing literature, the coordination of RM (ramp metering) + VSL (variable speed limit) + RG(route guidance) has rarely been studied. Besides, some coordination studies have limited discussion on the interaction between DTMs in non-coordinated cases. Based on these research gaps, the research question of this thesis is: What is the impact of coordinating RM, VSL and RG on a road structure where they have potential counter-effects on each other when following local objectives?

This research aims to explore the network performance on the non-coordination cases and coordinated cases. The simulation is built in SUMO, while the DTM control measures are implemented via the Traffic Control Interface (TraCI). In the coordination, all controller parameters (metered flows, speed limits, and split rate) work together to improve traffic in this two-bottleneck system. The coordination strategy has a model predictive control structure. The cell transmission model (CTM) is used as the prediction model. In this case, all
the effects of considered DTM measures can be integrated into the expression of the inflow to the first cell on the basis of certain assumptions. Considering that there are 5 control variables, genetic algorithm (GA) is used for the optimisation process in the MPC control. The objective is maximising the total outflow after bottlenecks on two routes.

The conclusion of the research is that there is benefit for applying DTM coordination on such a road structure with two parallel freeways. However, the improvement caused by a predictive RG is the major part of the benefit. This is because that the simulation cannot capture the capacity drop phenomenon. The simulation results indicate that a predictive RG is more effective compared to coordinated control. It also leads to the best travel time equity among routes than the coordinated control. Based on the findings, it is more prior for this kind of road structure to improve the in-vehicle RG on this road structure than to apply coordinated control.