The establishment of areas for safe automated driving in mixed-traffic settings is one major barrier in the development and adoption of Autonomous Vehicles (AVs). This work investigates safety in the interactions between AVs, human-driven vehicles, and vulnerable road users such as cyclists and pedestrians in a simulated urban environment in the Dutch city of Rotterdam. Proposing new junction and pedestrian models, virtual AVs with an occlusion aware driving system are deployed to deliver cargo autonomously. Assessing the impact of various measures, including V2V, V2I, V2X communications, infrastructure modifications, and driving behavior, we show that traffic safety and network efficiency can be achieved in a living lab setting for the considered case. Our findings further suggest that V2X gets implemented, new buildings are not placed close to intersections, and the speed limit of non-arterial roads is lowered.

The distribution chain of two-dose vaccines by an air carrier (KLM cargo) with its practical features is modelled to study the influence of supply uncertainty. First the goal is to find an efficient solution approach for the model which gives good quality solutions in reasonable computation time. Secondly with the developed solution method the supply uncertainty is taken into account using robust optimization. Solving the model using an exact solution method leads to a too large increase of computation time with increase of the model size for the purpose of robust analysis. Nevertheless, given the high reliability of the model the results of this commercial model are used as a reference. To circumvent the high computation time, two alternative solution methods are developed and implemented. Respectively the genetic algorithm and the rolling horizon method. A basic implementation of the genetic algorithm does not provide adequate results and gets trapped in a local optimum. An analysis shows that measures are needed to increase the flexibility and stimulate the algorithm to find so called “transaction-less” events. To achieve this, a toolbox is developed. The toolbox consists of analysis tools (measurement of convergence rate, sparsity and a diversity measurement) and tools intended to improve the convergence and accuracy of the solution. These latter are an adapted mutation operator that stimulates the number of transaction-less events (sparsity) and an approach for directed mutation. Each measure by itself has a positive effect on the initial convergence rate, but the algorithm still gets trapped at a somewhat improved local optimum at a level of approximately 10% above the optimum solution. A strong improvement is found by combining these measures of the toolbox resulting in solutions that approach the optimal solution within less than 5% for a single destination at a very high convergence rate. The best found combination of measures are implemented to solve a multi-destination problem. The results prove to be not as good as the result for the single destination: the gap to the optimal solution is roughly 10% and the convergence rate is somewhat slower. Probably this is due to the fact that the boundary conditions impose a reduction of flexibility for the multi-destination setting. On the other hand this finding might provide a base for worthwhile future work. Since this is solver is not (yet) suitable to be used in the robustness analysis. Three different implementations of the rolling horizon method were made: (i) the straight forward (myopic) approach, (ii) extending the time window with relaxed periods and (iii) a shifting rolling horizon. The straight forward approach is significantly improved by using the relaxed and shifting methods...

The Automatic Identification System (AIS) is used in the maritime domain to improve sea traffic safety by requiring vessels to broadcast real-time information such as identity, speed, location, and course. As it allows global monitoring of almost any larger vessel and has the potential to considerably improve vessel traffic services and collision risk assessment, AIS has been used in an increasing number of applications. This emphasizes why the quality of data transmitted is critical. We are also becoming more aware of the possibilities of spoofing or fabrication of AIS data, which has a direct impact on the dependability of AIS data. This study looks into comparing video surveillance with Automatic Identification System (AIS) data to detect data overlap or spoofing in the maritime environment. An object detection model was proposed and trained to fill the research gap mentioned above. Data from video surveillance and AIS were collected. A comparison of the results of object detection and AIS data was performed using a Python script. The analysis assisted in identifying data variations and understanding the potential and constraints of the current Algorithm. Work for the future is suggested to tackle the current constraints.