Estimating, understanding, and improving the robustness of networks has many application areas such as bioinformatics, transportation, or computational linguistics. Accordingly, with the rise of network science for modeling complex systems, many methods for robustness estimation and network dismantling have been developed and applied to real-world problems. The state-of-the-art in this field is quite fuzzy, as results are published in various domain-specific venues and using different datasets. In this study, we report, to the best of our knowledge, on the analysis of the largest benchmark regarding network dismantling. We reimplemented and compared 13 competitors on 12 types of random networks, including ER, BA, and WS, with different network generation parameters. We find that network metrics, proposed more than 20 years ago, are often non-dominating competitors, while many recently proposed techniques perform well only on specific network types. Besides the solution quality, we also investigate the execution time. Moreover, we analyze the similarity of competitors, as induced by their node rankings. We compare and validate our results on real-world networks. Our study is aimed to be a reference for selecting a network dismantling method for a given network, considering accuracy requirements and run time constraints.

There exists a tremendous number of research surveys on various aspects of UAV logistics, mobility and monitoring tasks in the literature. These surveys have been published in distinct venues, often having a significant overlap in goals and key findings. In this study, we provide a meta review across nearly 100 extant UAV surveys and overview papers, extract their key messages, and investigate the extent of being complementary. We develop the AERIAL framework, which aggregates the major challenges on the way to a successful application of UAVs for logistics, mobility, and monitoring. We believe that AERIAL framework and meta review contribute towards a clearer understanding of the scientific UAV landscape challenges and the identification of potential directions for future research studies.

Large Language Models (LLMs), based on their highly developed ability to comprehend and generate human-like text, promise to revolutionize all aspects of society. These LLMs facilitate complex language understanding, translation, content generation, and problem-solving, enabled by vast historical data processing and fine-tuning. Throughout the past year, with the initial release of ChatGPT to the public, many papers have appeared on how to exploit LLMs for the ways we operate and interact with intelligent transportation systems. In this study, we review more than 130 papers on the subject and group them according to their major contributions into the following five categories: autonomous driving, safety, tourism, traffic, and others. Based on the aggregated proposals and findings in the extant literature, this paper concludes with a set of challenges and research recommendations, hopefully contributing to guide research in this young, yet extremely active research domain.

On-demand air mobility services, often called air taxis, are on the way to revolutionize our urban/regional transportation sector by lifting transportation to the third dimension and thus possibly contribute to solving the congestion-induced transportation deadlock many metropolitan regions face today. Although existing research mainly focuses on the design of efficient vehicles and specifically battery technology, in the near future, a new question will arise: Where to locate the vertiports/landing pads for such air taxis? In this study, we propose a vertiport location selection problem. In contrast to existing studies, we allow the demand to be distributed over the whole metropolitan area, modeled as a grid, and exclude certain grid cells from becoming hubs, for example, because of safety/geographical constraints. The combination of these two contributions makes the problem intriguingly difficult to solve with standard solution techniques. We propose a novel variable neighborhood search heuristic, which is able to solve 12 × 12 grid instances within a few seconds of computation time and zero gaps in our experiments, whereas CPLEX needs up to 10 hours. We believe that our study contributes toward the scalable selection of vertiport locations for air taxis. Summary of Contribution: The increasing interest in opening the third dimension, that is, altitude, to transportation inside metropolitan regions raises new research challenges. Existing research mainly focuses on the design of efficient vehicles and control problems. In the near future, however, the actual operation of air taxis will lead to new set of operations research problems for so-called air taxi operations. Our contribution focuses on the optimization of vertiports for air taxi operations in a metropolitan region. We choose to model the problem over a grid-like demand structure, with a novel side constraint: selected grid cells are unavailable as hubs, for example, because of environmental, technical, cultural, or other reasons. This makes our model a special case in between the two traditional models: discrete location and continuous location. Our model is inherently difficult to solve for exact methods; for instance, solving a grid of 12 × 12 grid cells needs more than 10 hours with CPLEX, when modeled as a discrete location problem. We show that a straightforward application of existing neighborhood search heuristics is not suitable to solve this problem well. Therefore, we design an own variant of mixed variable neighborhood search, which consists of novel local search steps, tailored toward our grid structure. Our evaluation shows that by using our novel heuristic, almost all instances can be solved toward optimality.