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.

Automated vehicle control provides advantages in transport efficiency, redundancy, human-safety, flexibility, and parallelism. Extensive research has been dedicated to direct-following lateral control methods, using a single preview point as reference signal. However, these methods give rise to significant tracking errors. As alternative to a single point, a continuous path can be used as reference signal for vehicle control. Using a continuous reference path, accurate and comfortable control actions can be computed, while avoiding reference tracking errors. Therefore, robust reference path generation is an essential part of lateral vehicle control.

The goal of this research is to develop a generic, robust reference path generator for lateral vehicle control. Currently in path generation, the state-of-the-art method is based on repetitive polynomial fitting. This method inherently contains two main weaknesses. Firstly, it is not robust to sensor noise and other real-world disturbances. Secondly, as a result of the repetitive fitting, a discontinuous path is generated. This is undesirable, because it leads to an unfeasible reference path, since vehicles can only produce and track continuous trajectories. Moreover, a discontinuous reference path results in the need for path smoothing when used in comfortable lateral vehicle control. Fundamentally, this smoothing leads to inaccurate control, caused by manipulation of the original, discontinuous reference path. To overcome these weaknesses, a new Model-based Path Generation method is presented.

The development of a new, generic method for robust path generation is the main contribution of this research. This new path generation method is capable of producing feasible vehicle trajectories based on unfeasible waypoints. The performance of this method is evaluated in simulations and experiments, benchmarking it against polynomial fitting path generation. Furthermore, path generation robustness is assessed based on the outcome of a disturbance sensitivity analysis. The results are in accordance with the hypothesis stating that the new method outperforms the benchmark method in terms of path accuracy, robustness, continuity, and general applicability.

At large-scale events, the large inflow of visitors travelling by car often causes queues on the roads in the direct surroundings of the event. These queues can spill back to the main road network, where through traffic is driving as well: this through traffic will be hindered by the congestion, while those travellers don’t have the event venue as their destination. To prevent or at least reduce this unnecessary hindrance caused by car traffic at events, this Master thesis has researched control methodologies to optimise advice that is given to individual travellers using in-car systems. In individual traffic management (ITM), the event visitors will receive advice for their route and departure time choice. ITM can be used to optimise the decisions of the event visitors, to prevent queue spillback and other traffic issues at events. In this thesis, the design trade-offs for an ITM controller are identified: these trade-offs determine the actual design of an ITM controller aimed specifically at car traffic around large-scale events. Conceptually, the controller design is composed of a one-shot prediction model and mathematical optimisations of the individual route and departure time advice. These optimisations target a constrained system optimal traffic state in the network. Simulations of the designed controller show that ITM has potential to alleviate the congestion (and spillback) hindrance at large-scale events, by distributing event visitors over alternative routes and departure times. The benefits found in the simulations advocate the use of ITM at an event (or event-like conditions) in practice. Therefore, the application of the ITM controller design in practice is also discussed in this research. The discussion identifies the need to develop a (software) tool that is able to execute the controller’s actions in an offline or real-time manner. Furthermore, the possible barriers and opportunities for the implementation of ITM in the current and future traffic system are assessed.