Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · 1908.08651

Trajectory-Based Urban Air Mobility (UAM) Operations Simulator (TUS)

Euclides C. Pinto Neto·Derick M. Baum·Jorge Rady de Almeida Junior·João Batista Camargo Junior·Paulo Sérgio Cugnasca
View original source ↗Read source document ↗Export metadata ↓

Abstract

Nowadays, the demand for optimized services in urban environments to provide better society wellness is increasing. In this sense, ground transportation in dense urban environments has been facing challenges for many years (e.g., congestion and resilience). One import outcome of the effort made toward the creation of new concepts for enhancing urban transportation is the Urban Air Mobility (UAM) concept. UAM aims at enhancing city transportation services using manned and unmanned vehicles. However, these operations bring many challenges to be faced, e.g., the interaction between the controller agent and autonomous vehicles. Furthermore, trajectory planning is not a simple task due to several factors. Firstly, the trajectories must consider a reduced minimum separation as eVTOL vehicle are expected to operate in complex urban environments. This leads the trajectory planning process to observe safety primitives more restrictively once the airspace is expected to comport many vehicles that follow small minimum separation standards. Thereupon, the main goal of the Trajectory-Based UAM Operations Simulator (TUS) is to simulate the Trajectory-Based UAM operations in urban environments considering the presence of both manned and unmanned eVTOL vehicles. For this, a Discrete Event Simulation (DES) approach is adopted, which considers an input (i.e., the eVTOL vehicles, their origin and destination, and their respective trajectories) and produces an output (which describes if the trajectories are safe and the elapsed operation time). The main contribution of this simulation tool is to provide a simulated environment for testing and measuring the effectiveness (e.g., flight duration) of trajectories planned for eVTOL vehicles.

Explore related subjects

cs.CY

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Euclides C. Pinto Neto, Derick M. Baum, Jorge Rady de Almeida Junior, João Batista Camargo Junior, Paulo Sérgio Cugnasca. 2019-08-23. Trajectory-Based Urban Air Mobility (UAM) Operations Simulator (TUS). https://arxiv.org/abs/1908.08651

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Political Persuasion and Endorsement in Large Language Models

Large Language Models (LLMs) are increasingly employed as proxies for human behavior in computational social science. However, their tendency to internalize biases from training data raises concerns about their reliability in politically sensitive domains, specifically regarding their endorsement of persuasive language. In this work, we examine whether LLMs endorse persuasion-infused messages and whether partisan persona prompting modulates such endorsement. We evaluate six LLMs from different geographic regions on content annotated with persuasion techniques drawn from real-world media sources, measuring the likelihood of endorsement using a five-point Likert scale. The models are prompted as either a neutral social media user or as a user with left- or right-leaning political views. Results show that, without political conditioning, LLMs generally do not endorse messages containing persuasion techniques, although model-level differences emerge, and that partisan persona prompting increases polarization of endorsement, particularly for persuasion-infused content. Endorsement further varies across persuasion techniques and topics. These findings reveal how political conditioning influences LLMs' endorsement behavior toward persuasion-infused content, raising concerns about their deployment as agents in politically sensitive environments and complicating their use as reliable simulators of human political cognition.

cs.CY↗
arXiv2026

When Technically Plausible Advice Is Unsafe: A Cross-Ecosystem Measurement of Online Support for Technology-Facilitated Abuse

Technology-facilitated abuse (TFA) creates an adversarial setting where sound cybersecurity advice can be unsafe: changing credentials or resetting devices may alert an abuser, destroy evidence, or increase escalation risk. Victims seek guidance from search engines, peer forums, and conversational AI, often evaluated for relevance and correctness rather than contextual safety. We measure whether these sources meet victims' needs. From a decade of r/Stalking narratives, we construct 2,797 victim-derived queries spanning 11 misuse categories. We analyze 27,162 Google webpages, 2,476 Reddit query--thread responses, and 250 responses from three general-purpose LLMs and two survivor-support chatbots. Our framework measures technical quality and damaging guidance, plus secondary-link integrity on webpages, toxicity on Reddit, and trauma-informed support in conversational systems. We find failures & risks that relevance, accuracy, or actionability alone do not capture. Web Search and conversational systems frequently return relevant information; Reddit responses are less consistently relevant and actionable. In our evaluated accuracy sample, 17.3% of webpages, 13.3% of Reddit threads, and 19.6% of conversational AI responses contained damaging guidance. Further, 65.5% of victim queries led to a webpage with a secondary URL flagged by multiple VirusTotal engines, over 20% received a toxic Reddit comment, and every conversational system produced guidance that overlooked escalation risk. Specialization did not guarantee better support: HopeChat underperformed general-purpose LLMs on several dimensions, while Ruth remained limited in trauma-informed support. These findings expose a gap between technical quality and contextual safety. Safe TFA assistance requires risk-aware recommendations, trustworthy sources, uncertainty communication, and human support, beyond technically plausible answers.

cs.CY↗
arXiv2026

Auditing Institutional Heterogeneity for Generative AI in Patient Education: A Large-Scale Study of 102 US Transplant Handbooks

Health systems are rapidly deploying generative-AI assistants that answer patient questions from institution-authored education materials, on the premise that grounding in local content yields consistent guidance. Do the underlying documents themselves agree? We use a structured-output large-language-model judge to audit 1{,}772{,}261 pairwise comparisons across 102 patient-education handbooks from 23 US solid-organ transplant centers, paired with 1{,}115 patient-derived questions (TransplantQA). Four findings bear directly on deployment: (1) same-center cross-organ agreement exceeds cross-center same-organ agreement by $0.024$ in the primary analysis (Holm-adjusted $p=0.011$), with sensitivity to document selection; (2) information gaps concern topics relevant to underrepresented subgroups, with reproductive health a \emph{double jeopardy}: 82\% absence and 86\% judge-rated high significance among divergent/contradictory pairs; (3) judge-derived themes form 991 clusters, with immunosuppression and pregnancy timing among the highest judge-rated priorities; (4) question and observed-coverage features predict high-divergence questions retrospectively (AUC $0.77$). We discuss implications for deploying patient-facing generative AI in transplant care.

cs.CY↗