Autonomous vehicles offer significant potential for improving traffic efficiency and reducing
fuel consumption, with Model Predictive Control (MPC) being widely used due to its ability
to guarantee constraint satisfaction and safety while providing optimal control performance.
However, car models traditionally used in MPC approaches for vehicle control often overlooks
discrete dynamics like gear changes, which are critical for optimizing vehicle fuel consumption. Advancements have incorporated these discrete dynamics into MPC, resulting in a hybrid model that considers both continuous and discrete dynamics. The incorporation of
the fuel model, along with these discrete dynamics, significantly increases the computational
complexity of the MPC problem, making real-time implementation challenging. To address
this issue, Reinforcement Learning (RL) can be leveraged to simplify the optimization problem by learning policies that determine key discrete components, such as gear selection. This
allows the MPC controller to handle a simpler optimization problem, thereby reducing the
computational burden and enabling real-time control. This research aims to propose a new
approach to integrate RL and MPC for vehicle control, where RL is used to manage gear
transitions and MPC controls the overall vehicle dynamics, offering a computationally efficient solution, while achieving near optimal performance comparable to the conventional
MPC approach.

Emergency maneuvers on highways present one of the most complex challenges for automated driving. High speeds pushing the vehicle towards nonlinear regimes, coupled with the necessity of swift decision making, complicates the collision avoidance problem to the extent that even expert human drivers may struggle to safely avoid collisions.
Lack of sufficient and reliable data limits applicability of model-free and data-driven control approaches in hazardous scenarios, opening the door to model-based and optimization based control approaches. However, the unknown behavior of other road users, the sensitivity of the handling limits (e.g., tire saturation) to road conditions, and the amplification of minor steering adjustments on the lateral trajectory due to high speed necessitate the incorporation of nonlinear models in the design. Such nonlinearities should be balanced with the increased complexity and the need for swift responses to hazard....@en

Spatiotemporal systems are systems whose dynamics depend on time and space and are commonly found in real life. These systems are mathematically modeled using partial differential equations and are also known as distributed-parameter systems. Due to their structure and the high number of variables involved, control and estimation for this class of systems are very challenging. This thesis addresses two problems related to spatiotemporal systems: state estimation and system identification.

Monitoring the states of a control system is important to ensure the behavior of the system is achieving the control objectives. This can be achieved, among others, by using state observers that estimate the states of the systems regularly. First, we present a literature review of observer design methods for distributed parameter systems. In general, the design requires a dimension-reduction approach to implement the observer. From the dimension reduction, the design approaches can be classified into late and early lumping. In the late lumping perspective, model reduction is performed at the end of the observer design. In the early lumping perspective, dimension reduction is applied to the model of the system. We incorporate both approaches in our literature review.

State observer design requires the model of the systems. This thesis also presents a system identification method for distributed-parameter systems. The identification of such systems typically requires spatially dense and regular measurements, followed by selecting sensors that provide significant measurements to the model to reduce the model complexity. However, these requirements may be challenging to fulfill. In case the sensor locations are irregular and sparse in space, we propose the use of lumped-parameter system identification.

For models with a large number of regressors, we propose a method for reducing the number of regressors using a tree representation. The tree is a way to list models with different numbers of regressors. From all possible regressors for the model, the proposed method builds the tree from the simplest models, i.e., models with one regressor. The number of regressors in the models is incrementally increased to one or more models with the best performance. The addition is repeated until the tree contains models with the desired maximum number of regressors.

System identification is typically performed using a complete data set, i.e., for each input sample, there is an associated output sample available. However, there are cases in which some output samples are not recorded in the data set, making the identification data incomplete. This thesis also considers the problem of incomplete data for Takagi-Sugeno (TS) fuzzy system identification using the product space clustering method. This method comprises two steps: fuzzy clustering and rules construction. The first proposed method enables the use of incomplete system identification data to fuzzy c-means clustering algorithm developed for incomplete classification, which yields different estimates for a missing sample. This can be achieved by fusing those different values into a single value. The second proposed method treats missing samples as optimization variables during the identification process. The optimization is repeated until the change of all optimization variables is small.
@en

While various tracking algorithms have demonstrated effectiveness in terrestrial and aerial contexts, their performance in underwater settings remains unexplored. Object tracking in underwater videos presents unique challenges due to variable lighting, water turbidity, and unpredictable camera movement, all of which are likely to hinder the performance of traditional detection and tracking methods. Addressing this gap is crucial for applications such as marine biology research, underwater surveillance, and autonomous underwater vehicles.

This thesis first evaluates existing tracking algorithms, SORT, ByteTrack, and Bag-of-Tricks SORT (BoT-SORT), each incorporating motion estimation and linear data assignment methods on a novel underwater video dataset with a moving camera. The thesis then improves on the SORT algorithm by adopting velocity estimation techniques, a formula-based and an optical flow-based, giving rise to two new algorithms, SORT-V and SORT-OF. Furthermore, the thesis proposes a novel tracker that utilises an Interacting Multiple Model filter to estimate the location of the target object. The evaluation focuses on finding a balance between specific metrics, such as tracking accuracy, identity switches, and the number of tracked and lost trajectories. The results indicate that using velocity estimation techniques improves the tracking accuracy by 11% and tracks more objects by nearly halving the number of lost objects. Incorporating a constant acceleration model in the IMM filter gives the best result, with the highest tracking accuracy and with the least number of identity switches, all in real-time computational speed.

In the evolving landscape of energy systems, microgrids have emerged as a key solution for enhancing energy efficiency and sustainability. Capable of operating independently or alongside the main power grid, microgrids integrate renewable energy sources and ensure local energy distribution. This makes them instrumental in reducing dependencies on centralised power supplies and improving resilience against disruptions. This research addresses the unit commitment problem, a mathematical optimisation challenge where the objective is to coordinate a group of energy production units to meet demand at minimal cost. We model the microgrid as a mixed logical dynamical (MLD) system, incorporating both the continuous and discrete variables involved in the microgrid. Model predictive control (MPC) is selected as the control strategy due to its suitability for controlling hybrid systems and its ability to handle complex constraints.

However, the application of MPC is challenged by the need to solve computationally demanding mixed-integer linear programming (MILP) problems at each control iteration, which are combinatorial. To address this challenge, this research proposes integrating a learning-based method to enhance MPC in microgrids. We propose using transformers to learn and predict the binary decisions in MILP problems, thereby reducing the problem to a more tractable linear programming problem. Transformers are chosen for their ability to recognise patterns in sequential data, a key aspect of the decision-making process in MPC. Furthermore, their capability for parallel processing allows for more efficient training and scalability to larger problems, making them highly suitable for handling the dynamic and complex optimisation tasks found in microgrid control. Simulation experiments show that integrating transformers in the decision of the discrete variables reduces the overall computation with only a slight loss of optimality and, therefore, improves the online applicability of MPC in microgrid control.

This PhD thesis advances resource recovery from wastewater by focusing on two key technologies: Purple Phototrophic Bacteria (PPB) raceway reactors and anaerobic digesters (ADs). To address challenges such as process variability, monitoring limitations, and operational inefficiencies, this research employs three complementary approaches: mechanistic modeling, hybrid system identification, and adaptive predictive control. Mechanistic models offer detailed insights into microbial interactions and process dynamics; hybrid system identification develops low-order models for practical data reconciliation and forecasting; and adaptive model predictive control dynamically optimizes operations to enhance performance. Key contributions include a novel mechanistic model for PPB cultivation in raceway reactors, a temperature-dependent extension for the anaerobic digestion model no.1, a hybrid system identification method to approximate complex mechanistic models, and an adaptive hierarchical process-oriented MPC framework for PPB reactors and ADs to manage variable operations and unknown disturbances. These approaches advance wastewater resource recovery in their intended perspectives, providing efficient solutions for both case studies.@en

Urban rail transit networks are dedicated to providing safe, efficient, and eco-friendly transportation services for passengers. This thesis focuses on innovative model predictive control (MPC) strategies for the integration of passenger flows, timetables, and train speeds in urban rail transit networks. We introduce several innovative MPC frameworks, including bi-level MPC, scenario-based distributed MPC, learning-based MPC, and cooperative distributed MPC. These approaches exhibit significantly improved performance compared to conventional methods.@en

The railway timetable rescheduling problem is a challenging problem in both industry and academia. It is required to calculate a feasible and relatively good timetable within a limited time to reduce the negative impact of disturbances or disruptions. The railway timetable rescheduling problem is typically formulated as a mixed integer linear programming (MILP) problem, which is difficult to solve due to the existence of the integer variables. To address this problem, many optimization-based studies have been conducted. The main advantage of using optimization-based methods is that they are easy to implement and more straightforward. However, the main disadvantage is that most optimization-based methods cannot reach the time requirements for large railway timetable rescheduling problems. There are also some researches using reinforcement learning techniques to solve this problem. By using reinforcement learning, the time requirement could be fulfilled.

In this thesis, an algorithm that combines both reinforcement learning and optimization approaches is proposed to solve the railway timetable rescheduling problem. In the beginning, the reinforcement learning environment is constructed from the railway timetable rescheduling problem. By selecting the independent integer variables as the action, the constraints involving the integer variables are satisfied. After that, a value-based reinforcement learning algorithm is implemented to determine the independent integer variables of the MILP problem. Then, the complete solution of the integer variables could be derived from these independent integer variables. With the solution of integer variables, the MILP problem could be transformed into a linear programming problem, which could be solved efficiently.

Several case studies are conducted in this thesis based on part of the Dutch railway network from Utrecht to 's-Hertogenbosch. The simulation results show that the proposed method makes a great improvement compared with the baseline regarding reducing the total delay of the system. Meanwhile, the reinforcement learning-based method also has an obvious advantage in terms of running time.

Quadrupedal robots possess the ability to move freely in the world and perform a variety of actions that would be unsafe or impractical for humans to perform. In the SNOW project, a quadrupedal robot is tasked with aiding firefighters in rescue missions during house fires by locating humans and assessing their health. Pushing away obstacles that cannot be circumvented otherwise is one of the many capabilities a quadrupedal robot should possess to be of most use in such missions. We develop a proof of concept by solving two problems sequentially: which stance to take on prior to the push and how to perform the push.

The process of stance selection starts with generating a certain amount of stances. Stances are generated starting from a preselected stance appropriate to the goal location and deviating from the 12 joint angles with a normal distribution. All generated stances are ran through a number of filters, which rely on solving for the forward and inverse kinematics of the robot. These filters check if the initial position is sensible and balanced and if the projected final position is close to the goal and balanced. The inverse kinematics are solved using Adaptive-Network-Based Fuzzy Inference Systems (ANFIS), which results in accurate estimations within a time frame that can be used in real-time applications. The final stance is selected by comparing the total displacement of all joint angles per stance, where the lowest total displacement is considered optimal.

The push is controlled by a nonlinear model predictive controller. We strive for a stable push, where the contact between the pusher and the object sticks, by keeping the movement of the end-effector within the motion cone. The motion cone denotes all twists the object can have without slipping at the contact with the pusher and is constructed using the limit surface to model the interaction between the object and the support surface and the generalized friction cone to model the interaction between the pusher and the object. We find that the motion cone predicts stick and slip with an accuracy slightly higher than 80%. Our controller steers accurately to all goals that lie within the motion cone and moves the object with a twist on the edge of the motion cone if the goal location lies outside of the motion cone. The robot remains balanced throughout the pushing motion in the vast majority of cases, but is more at risk of tipping over when pushing heavier objects.

Ambitions to limit climate change are incentivizing the expansion of renewable energy. In particular, offshore wind energy is expected to grow rapidly. To harness the full potential of existing as well as prospected offshore wind farms, the limited capacity of the internal cable network of the offshore wind farm, called the collector system, should be efficiently used.

The operation of the collector system during cable outages presents significant potential in this regard. Currently, during these outages, a conservative approach is taken that under-utilizes the capacity of the collector system and consequently limits power production excessively. The available headroom of the system can be unlocked by optimizing the power routing and turbine setpoints. This optimization problem is the topic of the MSc Thesis, carried out
within Vattenfall.

Two novel optimization-based rerouting and setpoint decision frameworks are developed for collector systems with arbitrary topologies: an open-loop control strategy and a receding horizon control strategy.

The open-loop control strategy assumes that the network can only be reconfigured at the beginning of the outage. It is formulated as a mixed-integer linear programming problem, in which the cables are modeled as binary control variables and the setpoints as continuous control variables.

The receding horizon control strategy is deployed in real time, leveraging cable temperature measurements and power forecasts to derive optimal control actions dynamically. Dynamic thermal rating is applied, which entails that the power flows are constrained based on the cables’ temperatures rather than on a static rating. The resulting control strategy is formulated as a mixed-integer quadratically constrained programming problem.

A case study is performed to compare the performance of the developed strategies to existing strategies. Simulations concerning seven occurred cable outages at an offshore wind farm show an average increase in power production with respect to the industry control strategy of 0.82% for the open-loop control strategy and 4.2% for the receding horizon control strategy.

The renewable generation capacity, particularly solar and wind power installations, has increased steadily in the Netherlands over the course of recent years. Due to the local, small-scale nature of these power plants (compared to conventional power plants), a large share of this generation capacity is installed into the medium- and low-voltage distribution grids. This trend acts as the source of several challenges for distribution system operators (DSOs) such as Stedin Netbeheer B.V. (a DSO in the Netherlands, supporting this project). One of the main problems is the fact that voltage limit violations, particularly upper voltage limit violations, become more frequent.
Conventional voltage control schemes assume a unidirectional power flow (i.e., consumption only) in distribution grids and are unable to keep the limits in case a large share of generation capacity is installed. This is further complicated by the fact that underground cable networks, typical in Western European electricity distribution grids, have lines with large R/X ratios, which reduces the effectiveness of reactive power injection-based voltage control methods.
This MSc project intends to solve the issue of voltage limit violations with a model predictive control (MPC) policy. The considered control actions coordinated by the model predictive controller are the switching of the on-load tap changer (OLTC) mechanism mounted to the primary substation’s transformer, setpoint adjustments of the low-level OLTC control relay, and the active power curtailment of larger photovoltaic plants. A linear, sensitivity-matrix-based model is used for the grid’s state prediction; and the sensitivity values are re-calculated at each sampling time step of the MPC. To avoid the curtailment of photovoltaic (PV) plants when not justified, a conditional curtailment logic is incorporated into the MPC policy: PV plants are only allowed to be curtailed if their local voltage magnitude is above a tuneable threshold. This logic is described and incorporated into the model predictive controller’s optimization problem with the help of binary variables and mixed-integer linear (MIL) constraints. The benefit of incorporating knowledge about future disturbances (load, generation, and external grid voltage profiles) is also tested, in order to assess the potential benefit DSOs could get from forecasting these quantities.
A case study was conducted in which the considered controllers were tested on a section of a Stedin grid that carries the characteristics of a typical Western European medium voltage distribution grid: large R/X ratios and a large installed PV generation capacity both in the form of household generation and larger PV plants. All controllers were simulated in 4 different test cases: a typical summer day, a summer day with the external grid’s overvoltage, a summer day with 2 out of 3 large PV plants not operating, and a typical winter day. All test cases have the control goals of mitigating limit violations and ensuring that the nodal voltage magnitudes are as close as possible to the nominal 1 per unit throughout the day. The winter day test case has the additional goal of avoiding excessive curtailment as PV energy is worth considerably more during these days. All profile data is based on real Stedin measurements. The designed model predictive control policies are compared to two simple control schemes: current compounding, i.e. when the primary substation’s automatic voltage control relay’s setpoint is adjusted based on the active power delivery through the substation’s transformer, and another scheme when current compounding is combined with local active power curtailment controllers for large PV plants. The most important metrics used for comparison are voltage root mean square error (RMSE), the total voltage limit violation area, the percentage of curtailed PV energy, and the number of tap changes over the considered day. The simulations were carried out using Python and DIgSILENT PowerFactory.
The simulation results show that the MPC policy can perform better than the simple control schemes but only when exact knowledge of future profiles is available. In this case, the MPC results in lower voltage RMSE, smaller violation areas, and lower curtailment percentage values, at the expense of using more tap changes in all 4 test cases. In 3 of the 4 test cases, MPC was completely able to eliminate voltage limit violations, showing clearly the advantages of good quality forecasts on future disturbances. Since this exact knowledge about the near future fluctuations is quite ideal, more realistic MPC policies were also tested with no future knowledge and tightened voltage constraints. These simulations brought mixed results when compared with the simple schemes, performing better in terms of voltage RMSE, but worse in terms of the total voltage limit violation area.

This thesis addresses the Learning-Based Control (LBC) of unknown partially observable systems in the Linear Quadratic (LQ) paradigm. In this setting of learning-based LQ control, the control action influences not only the control performance but also the rate at which the system is being learnt, causing a conflict between learning and control (exploration and exploitation), which is particularly challenging to address. This thesis aims to develop a novel LBC algorithm for unknown partially observable systems in the LQG setting that is computationally efficient and can guarantee an optimal exploration-exploitation trade-off, quantified by a metric called regret. The regret quantifies the cumulative performance gap between the LBC policy and the ideal controller having full knowledge of the true system dynamics. The contributions in this thesis involve a novel LBC algorithm deployed in a two-phase structure. The first phase involves injecting Gaussian input signals to obtain an initial system model. The subsequent second phase deploys the proposed LBC strategy in an episodic setting, where the model is updated for each episode, and the resulting updated LQG controller is applied with additive Gaussian signals for exploration. In addition, the thesis establishes strong theoretical guarantees on optimal regret growth.

Nowadays, the high demand for road transportation often reaches a point where it exceeds the capacity of freeway traffic networks, resulting in congestion. Freeway traffic congestion is a major social problem, as it is the reason for increased time delays, higher accident risk and environmental pollution. There is, therefore, the need for a sustainable solution that can be implemented on the existing road infrastructure. Freeway traffic control is considered as such a solution. It uses different control measures, in order to improve the performance of the freeway traffic network, by influencing the drivers’ behaviour.

The Variable Speed Limits (VSLs) are a traffic control measure that aims to increase traffic safety, improve traffic flow and reduce the environmental pollution. Towards the improvement of the freeway traffic flow, easy-to-implement VSL control algorithms are used as mainline metering control approach.

Two easy-to-implement VSL algorithms are reported, namely the Mainstream Traffic Flow Control (MTFC) and the Logic-Based control algorithm for Variable Speed Limits (LB-VSL). The algorithms are usually implemented in an non-adaptive framework. The main contribution of this thesis is that it proposes an adaptive framework for both algorithms, in which the critical density of the freeway traffic network at bottleneck’s location is estimated on-line. This estimated critical density is used to adjust the controllers’ parameters in real-time.

Three different estimation methods for the bottleneck’s critical density are studied, namely the Parameter Estimation (Parameterschatter) method, the Simple Derivative Estimation (SDE) and the Kalman-Filter-based derivative Estimation (KFE) methods. All three methods focus on the real-time estimation of the derivative of the Fundamental Diagram (FD), in order to produce estimations of the critical density.

A case study is performed to evaluate the performance of the three algorithms. A hypothetical 12 km long freeway stretch is used, which contains two VSLs installations and a lane-drop bottleneck. In the first part of the case study an accident scenario is studied, which decreases the critical density at the bottleneck’s location. The second part of the case study evaluates the three adaptive easy-to-implement VSL algorithms under the assumption of a decrease of the critical density across the whole network, due to bad weather conditions.

The microscope is an essential tool for biologists. Since the late 16th century, it has given researchers a better understanding of cell processes and greatly advanced healthcare. In this century, Single molecule localization microscopy (SMLM) has revolutionized optical microscopy by breaking the optical diffraction limit. Sparsely activating emitters in a sample labeled with fluorophores, the object can be reconstructed by estimating their positions using the system point spread function (PSF). These localization algorithms are the state of the art
in optical imaging, using unbiased estimators to reach the theoretical minimum uncertainty, or Cramér-Rao lower bound (CRLB).
While SMLM works well when emitters are sparsely activated, overlap of the emitter images is inevitable for thick or densely labeled samples. When SMLM is used on such images, the estimates become biased and the algorithm cannot find the correct number of emitters. Most techniques also make a deterministic estimate and are incapable of representing the uncertainty of estimates for dense samples.
A three-dimensional, Bayesian multiple emitter fitting algorithm is constructed using reversible jump Markov chain Monte Carlo (RJMCMC). While following the structure of Bayesian multiple-emitter fitting (BAMF), novel RJMCMC moves are designed to sample the parameters. The algorithm also jumps through models, estimating the number of emitters. It asymptotically samples from the posterior, revealing uncertainties in three-dimensional imaging that other techniques are incapable of imaging.
The algorithm was tested with astigmatic and biplane imaging. It has proven capable of consistently finding the correct model when a prior on emitter intensity is used. When separating two emitters, posterior density reconstruction revealed non-Gaussian emitter position uncertainties. Upon further investigation, the posterior density was found to be multimodal, with
both modes representative of the data and indistinguishable in terms of likelihood. This shows the algorithm can quantify three-dimensional PSF degeneracy and can become a vital tool for researchers to analyze their imaging setup. We also expect it to be especially effective when combined with modulation-enhanced localization microscopy (meLM) techniques.

Railway networks are subjected to disruptions on a daily basis, which may make the timetable unimplementable, which in its turn may significantly influence passenger satisfaction. In practice, train dispatchers are responsible for mitigating the influence of disruptions, such as delays of trains, train cancellations, and overcrowdedness at stations. The solutions they propose are highly dependent on their experience, often resulting in low quality solutions. In addition, the disruptions must be solved in a matter of minutes, which is challenging because of the problem scale and computational complexity. Effective models and solution approaches are required to mitigate the influence of disruptions.

In recent decades, railway rescheduling models have been developed to support train dispatchers and to improve rail services. A recent and promising model is the event-activity network model, which is a graph-based formulation that supports a wide variety of rescheduling measures. This thesis extends the event-activity network model by including rolling stock circulation with depot entry and exit operations to increase the practicability of the operator-centric model. In addition, a passenger-centric model is proposed by embedding detailed passenger-related factors into the operator-centric model, where the train capacity is included, and the detailed number of passengers in the railway network is calculated. Therefore, the effect of delays on passengers can be handled properly. The passenger-centric model can help minimize the number of waiting passengers on platforms to avoid overcrowding and to improve passenger satisfaction. In practice, the resulting passenger-centric mixed-integer linear programming (MILP) problem is hard to solve due to the introduction of binary variables for train orders, which are important for calculating the detailed number of passengers. An adaptive large neighborhood search (ALNS) algorithm is introduced to address the complexity due to train orders and to improve the computational efficiency of the passenger-centric method. With properly designed destroy and repair operators, the ALNS algorithm can explore the solution space efficiently. Therefore, a balanced trade-off between solution time and quality can be made.

Case studies are conducted based on the train lines operating between the stations of Utrecht and 's-Hertogenbosch in the Netherlands. The simulation results show that the developed model can explicitly include the number of passengers while considering the rolling stock circulation plan. Compared to directly solving an MILP problem using a commercial solver, ALNS can calculate solutions more efficiently while maintaining a high level of solution quality.

Urban areas with many daily commuters often experience a reduction in traffic flow during rush hours. In some urban areas where this reduction in traffic flow occurs, there are popular destinations for leisure that have multiple large parking facilities. Vehicles searching for vacant parking spots drive at a lower vehicle speed, reducing the traffic flow even further. Traffic flow can be improved by using a traffic signal control strategy and a smart parking solution to increase the speed of vehicles searching for vacant parking spots. This thesis combines the use of Model Predictive Control (MPC) in traffic signal control, with a Parking Resource Allocation Model (PRAM) to further improve the traffic flow on the traffic network. The PRAM considers the travel time of all travel routes to the parking areas, as well as the distribution of vehicles toward the parking areas.
The performance of the combined MPC and PRAM control strategy is compared using two case studies. In both case studies, the traffic network is a simplified representation of the mall of the Netherlands, located near Leidschendam. The first case study simulates traffic based on the morning rush hours, and the second case study simulates traffic based on the evening rush hours. In both case studies, multiple traffic demands are simulated, based on historical traffic data provided by Rijkswaterstaat. No historical data is provided by the mall of the Netherlands so fictive data is used. The performance of an MPC control strategy, an MPC control strategy with the first PRAM, and an MPC control strategy with the second PRAM is compared with a fixed-time control strategy for these traffic demands. The results show that the MPC control strategy reduces the total time spent and vehicle time loss of all the vehicles for the morning rush hours, but not for the evening rush hours. For one traffic demand, the added PRAMs further reduce the total time spent and vehicle time loss of all the vehicles, in both case studies. For the other traffic demands, the added PRAMs increased the total time spent and vehicle time loss. There is no significant difference between the two PRAMs in terms of the total time spent and vehicle time loss. Furthermore, the distribution of vehicles to the parking areas is more evenly for the second PRAM. Since the parking demand is fictive, future research is necessary with accurate parking demand to ensure that the combined MPC and PRAM further improves the traffic flow on the traffic network of the mall of the Netherlands. Moreover, future research is needed to more accurately predict the travel time of travel routes.

Traffic flow predictions are an important component in the rising demand for solutions to cope with the increasing pressure on transportation networks. Especially on a long prediction horizon, traffic flow predictions remain challenging due to the complex, nonlinear nature of traffic flow and the influence of both temporal and external features. To incorporate sequential behavior of time series, without being subject to limitations inherent in recurrence-based models, the transformer is increasingly used. However, whether this model is advantageous on long horizons is still unknown. Therefore, in this paper, first, multiple correlation analyses are applied to identify important features in traffic flow. Next, these are incorporated into a generic transformer-based framework, and the adequacy of the transformer on long prediction horizons is investigated based on real data. To test the genericity of the proposed prediction model, all analyses are conducted for two locations, which are subject to different traffic behavior. The first is located on the ring road of Haarlem and is mainly affected by commuter traffic, whereas the second is located on the road to the coast and has more irregular behavior. Results show that the transformer outperforms baseline prediction models on both short and long horizons, especially when the location is subject to irregular behavior. In addition, the inclusion of external features, such as the day of the week, holidays, and temperature, improves the model performance. Moreover, the genericity of the model is highlighted by its applicability to multiple locations.

Currently, inland waterway shipping mainly includes barges and bulk transportation with little to no variation in volume, product, and route. Since transport over water emits less CO2 per tonne-km than road transport, a potential way to reduce CO2 emissions is to transport more containers via inland waterways. Large delays cause unreliability, which in their turn cause an opportunity cost and increased operational costs. These increased costs have a negative impact on the competitiveness of inland water transport with regards to road transport.

An alternative to the way planning is handled currently is by solving a Vehicle Rotaton Planning Problem (VRPP). The problem is solved by minimizing a cost function. For example, the total time spent in a port area (called the sojourn time) can be minimized.

Solving the VRPP results in a rotation plan that dictates the order in which vessels visit terminals and how many containers they should transport from one terminal to another. There are multiple ways to solve the VRPP, one of which is through the use of the Distributed Pseudotree Optimization Protocol (DPOP), where the vessel operators reach an optimal solution together by sharing only information about those variables that share the same constraints with other variables from other agents. An attractive property of DPOP is that it is possible to include privacy constraints in the algorithm, such that vessel operators only have to share minimal information about their journey and not lose a competitive advantage to one another. For example, the operators might only want to share variables like the arrival time, and only want to share it with operators that have the approximately same arrival time.

To ensure the problem can be solved and that the message size does not run out of memory bounds, the memory-bounded version of DPOP is implemented. Because Memory-Bounded Distributed Pseudotree Optimization Protocol (MB-DPOP) requires an exponential number of messages when the inference width is above a certain threshold, an alternative way of formulating and solving the problem is considered using the Maximum Gain Messaging (MGM) algorithm and a formulation that includes how much vessel operators are willing to change their current rotation plan for a lower overall cost. With this formulation, when something changes in the problem definition, the entire calculation does not have to be done again because starting from the previous solution is possible.

Two simulated case studies in the port area of Rotterdam are done to performed the performance of the algorithms. The results show while MB-DPOP can guarantee some level of privacy, it does not scale very well and is thus not really useful in real life. While the algorithm yields a global solution and can guarantee a certain level of privacy, its execution time is in the order of hours, even for relatively small problem sizes. Conversely, the MGM algorithm with reformulation scales quite well and can possibly guarantee some level of privacy as well with extension. While the algorithm does not come up with a global solution, it can be a decent trade off between a level of privacy, and scalable algorithms that are centralized, such as a genetic algorithm.

Maintenance is a necessary measure to keep the asset, in this case a road network, in good condition. Spending too much on maintenance is clearly not efficient, while not spending enough may cause the condition to drop below a desired value; moreover, it will almost always cost more to correct the emerging damages afterwards.
In this report, a moving horizon optimization approach is presented as a conceptual model to improve the efficiency of maintenance of a road network, compared to the currently used maintenance approach.

To achieve the goals on greenhouse gas emissions, the energy supply and demand is in transition. Distribution power grids therefore are increasingly reaching their capacity limits due to electrification and the vast increase of distributed energy resource (DER) connection requests with large peak power output. Increasing physical grid capacity is a costly operation and take lots of time to realise. Grid operators are therefore allowed to connect additional energy resources to the power grid at the cost of N-1 reserve capacity. Having N-1 reserve capacity means that any grid component can go out of service, without causing overloading of another grid component. When this security principle is abandoned, coupled preventive and corrective control measures might be necessary to preserve security of energy supply.
In this thesis, commissioned by the Dutch distribution grid operator Stedin Netbeheer, a preventive-corrective contingency control method based on differential evolution (DE) is designed to increase the maximum admissible DER generation on a distribution grid. For the contingency analysis process in this contingency control method, the full AC power flow method is compared to the method based on line outage distribution factors. The resulting DE-based preventive-corrective control method using the full AC power flow contingency analysis method allows for significant extra DER generation capacity to be connected to the study case distribution power grid, without requiring expensive grid expansions. The case study of this thesis work is the Stedin Middelharnis distribution power grid. Due to the computational complexity of the preventive-corrective contingency control method and the sparse connectivity of power system data, convolutional neural networks are deployed to reduce the computational time. In the first approach, the convolutional neural network is used to perform contingency analysis. Due to this neural network approach, the time to compute the preventive-corrective control actions is reduced by 40%. However, the accuracy of the control is significantly reduced due to the inaccuracy of the used contingency analysis method. In the second approach, a neural network is trained to determine the coupled preventive control actions. The performance of a neural network with and without convolutional neural networks is compared in this approach. Results show that the convolutional neural network outperforms the neural network without convolutional layers. This second convolutional neural network approach is satisfactory accurate and the computational efficiency of this control method is greatly increased compared to the control method based on DE, making realtime preventive-corrective control possible.