In the last years, Floating Car Data (FCD) has risen as a technology that has the potential to replace legacy sensors, reduce associated installation and maintenance costs and offer new possibilities in the field of traffic management. FCD refers to the use of sources such as in-vehicle systems with network connectivity or road users’ mobile phones to collect relevant traffic information (e.g. vehicle speed or intensity). There is extensive literature on the feasibility of using FCD for traffic management purposes and on the added value of fusing legacy sensors data with FCD. These studies have been mostly experimental and a literature review shows that there is no academic work on how to deploy and integrate legacy sensors data and FCD in an actual live setting. This research aims to fill that gap by designing a flexible ICT Architecture that integrates FCD into Rijkswaterstaat’s Traffic Management and Information Systems. Taking an architecture point of view to address this challenge is not only novel but also appropriate because it provides a comprehensive view of what changes need to take place at different levels of the organization (business, information systems and technology) for this to happen. This research follows a design science research approach, where four main phases can be distinguished: (1) Problem formulation, (2) Building, (3) Reflection and learning and (4) Formalization of learning. The Building phase is further subdivided into the specification of design principles and requirements and the development of the solution ICT Architecture. The outcomes of this research are relevant not only to Rijkswaterstaat but also to other organizations looking to integrate FCD in their traffic systems, to design science researchers and to ICT architects.

In this research, insight is gained into the impacts of truck platooning on traffic performance and safety at motorway on-ramps in mixed traffic using microscopic simulation. Thereby different platooning strategies and platoon configurations are considered, that for instance differ in desired platoon inter-vehicle gaps and platoon sizes. The effects are considered for a broad range of traffic intensities and penetration rates of equipped trucks. Merging problems are observed, but these can be solved if the platoon members yield to create a gap for merging vehicles.

Significant developments in the field of Advanced Driver Assistance Systems (A prove traffic management and prediction, driver comfort, and vehicle safety. However, a detailed knowledge of the way vehicles move is crucial for these applications. The mobility behavior has also changed over the last decades significantly. Both individual mobility and the mobility of goods is nowadays an essential part of our economy. The latest trends in vehicle automation are the predicted first signs of a partially/autonomous future of mobility. Whether this future will be with or without individual vehicle ownership will become apparent in the coming years. For the moment, all researchers can do is to work on new topics tomake the future of mobility faster, more efficient, quieter, and safer. Detailed knowledge of state-of-the-art automation systems is essential in order to gain further insights into their effects, e.g., on traffic, the travel behavior of people, the security gaps caused by these systems, and passenger comfort.

This study explores the possibility of developing a short-term traffic flow prediction model that can be used to convert installed adaptive controllers to predictive controllers with minimal hardware changes. By using the prediction model’s outputs to virtually trigger vehicle loop detectors, the outputs of an adaptive controller can be extracted in advance of actual vehicle arrivals. This will enable service providers to send out time to green/red (T2G/R) information or green light optimal speed advice (GLOSA), which are driver assistance use cases that aim to efficiently guide vehicles through intersections, in anticipation of known upcoming signal states. The main requirements for developing the prediction model are that it should be scalable to different intersection configurations, adaptable to different traffic conditions and should encompass the nonlinearity of traffic flow behavior. Since it fits these criteria, the developed model is a multi-task learning recurrent neural network with exogenous inputs (NARX), which is designed to match the traffic flow simulation abilities of a well adopted analytical traffic flow progression model. Both models were tested for a corridor in Delft that experiences almost consistent free flow conditions, and on an intersection in Haarlem with varying traffic conditions. For both case studies the neural network outperformed the analytical model. Most notably, it was better adaptable to long queues at intersections, had lower average error values, made fewer large errors, and better recognized the effect of a source/sink. When interfaced with an adaptive controller to test the predictive control methodology proposed, the superiority of the designed neural network model over the analytical model became more prominent. At no point did the neural network’s prediction errors result in queue spill-back, which was not true for the analytical model. However, the overall accuracy of the NARX model was still not yet satisfactory enough for practical application (especially for highly under-saturated traffic conditions) without the use of corrective measures. Nonetheless, due to its significant superiority over the widely used analytical model, with regards to both accuracy and adaptability, this model can be considered as a new starting point for traffic flow progression modeling.

Traffic congestion causes detrimental effects on economy and society in terms of travel time delay, increased vehicle collision risk and increased air pollution. To tackle that, new intelligent transportation systems are being developed. One of these emerging technologies comprise automated driving systems. Adaptive Cruise Control (ACC), enabling a vehicle to follow its leader automatically, is an automated driving system which has been available in the vehicle market. Literature shows that traffic flow performance may not significantly benefit from ACC due to traffic instability and large headways. As an extension of ACC, Cooperative ACC is designed to improve traffic stability and throughput by using Vehicle-to-Vehicle (V2V) communication. Thanks to the anticipation of the downstream traffic, a short following gap can be realized by CACC which is highly expected to considerably increase road capacities. Before CACC vehicle is allowed and promoted in the market, it is crucial for policy makers and road operators to gain insights into the traffic flow impacts of CACC systems. Existing studies show that CACC can increase traffic throughput at high vehicle market penetration. However, the realistic effects of CACC on traffic flow have not been adequately revealed because the behaviour of CACC vehicles has not been realistically modelled. The multiple CACC driving modes, the degraded operation to ACC and the human driver take-over control when it is out of the CACC operational design domain, have not yet been explicitly included in existing CACC impact studies. A scientific gap prevails in modelling the complex CACC behaviour and investigating its actual influence on traffic flow especially at realistic networks with a single bottleneck and interacting bottlenecks.@en

The Dutch governmental organisation Rijkswaterstaat contributes to the smooth and safe flow of traffic, as both traffic jams and accidents cost society large amounts of money each day. Roads are designed for the current traffic composition. Due to the promotion of Adaptive Cruise Control (ACC) systems, utilisation of these systems is expected to increase. Society benefits from insights into the effects these systems have on traffic flow, as they can help to reduce traffic jams and accidents. ACC systems are designed to increase driving comfort by taking over throttling and braking from the human driver. For optimal driver acceptance, these systems show similar driving behaviour to that of human drivers. However, this is not entirely possible due to limited anticipation. To predict how differences in driving behaviour affect traffic flows, researchers usually perform simulations using parametric car-following models. However, research shows contradictory findings. The goal of this research was to gain insights into the performance of commonly applied parametric car-following models on representing the driving behaviour of ACC systems. Optimal model calibration was obtained by investigating the sensitivity of the model calibration to synthetic data. Investigated were the calibration methodology and the quality and quantity of calibration data. Models are calibrated to real-world driving data from an Audi A4 from 2017. These models were used to assess the capability of representing typical highway scenarios: steady-state car-following, cut-in, cut-out, hard-braking and stop-and-go scenarios. The considered models were the Intelligent Driver Model (IDM) model, which has previously been applied to model the driving behaviour of human drivers, the newly developed simplified ACC (sACC) model and a variant on this model. Insights in the sensitivity of the model calibration were obtained by performing a sensitivity analysis on synthetic data. Essential factors in achieving an optimal model calibration are: 1) the model closely matches the driving behaviour in the data, 2) noise levels are as low as possible and 3) the data should contain as many situations as possible that are also included in the model. The dataset must be sufficiently long to include all these situations and to allow the model to develop its dynamics entirely. Using these insights, a calibration was performed on real-world ACC driving data from an Audi A4 (2017). For the ACC system, it was found: 1) the ACC system exhibits non-linear driving behaviour, 2) the acceleration depends on the current velocity and distance to the desired velocity, 3) the system does not consider an intelligent braking strategy and is thus not able of handling safety-critical driving situations and 4) the model includes a sub-controller which ensures comfortable driving behaviour. Except for the comfortable sub-controller, the non-linear IDM model considers all of these factors and thus best represents the driving behaviour. The linear sACC model cannot represent standing conditions, which is resolved in the alternative version. The linearity allows for a better representation of the behaviour of the comfortable sub-controller. However, it disallows for an accurate representation of the dynamics by the models.