The rise of large language models (LLMs) has made natural language-driven route planning an emerging research area that encompasses rich user objectives. Current research exhibits two distinct approaches: direct route planning using LLM-as-Agent and graph-based searching strategies. However, LLMs in the former approach struggle to handle extensive map data, while the latter shows limited capability in understanding natural language preferences. Additionally, a more critical challenge arises from the highly heterogeneous and unpredictable spatio-temporal distribution of users across the globe. In this paper, we introduce a novel LLM-Assisted route Planning (LLMAP) system that employs an LLM-as-Parser to comprehend natural language, identify tasks, and extract user preferences and recognize task dependencies, coupled with a Multi-Step Graph construction with iterative Search (MSGS) algorithm as the underlying solver for optimal route finding. Our multi-objective optimization approach adaptively tunes objective weights to maximize points of interest (POI) quality and task completion rate while minimizing route distance, subject to three key constraints: user time limits, POI opening hours, and task dependencies. We conduct extensive experiments using 1,000 routing prompts sampled with varying complexity across 14 countries and 27 cities worldwide. The results demonstrate that our approach achieves superior performance with guarantees across multiple constraints.

Extreme weather is responsible for major delays of air traffic flow management. Due to the climate changes, the storms track and intensity will also change in Europe, increasing the horizontal flight inefficiency by about 4%. In this context, an accurate short-term forecasting of extreme weather at airport spatial scale will be very useful for the aviation mangers and controllers. This work is part of the H2020 SESAR ALARM project partially devoted to nowcast extreme weather events in the area of Milano Malpensa airport. We used ground-based weather sensors, Global Navigation Satellite System (GNSS) receivers, a C-band radar and lightning detectors distributed around Malpensa to develop a machine learning model able to nowcast the rain rate, the wind speed and the lightning occurrences.

Air transportation is essential for society, and it is increasing gradually due to its importance. To improve the airspace operation, new technologies are under development, such as Unmanned Aircraft Systems (UAS). In fact, in the past few years, there has been a growth in UAS numbers in segregated airspace. However, there is an interest in integrating these aircraft into the National Airspace System (NAS). The UAS is vital to different industries due to its advantages brought to the airspace (e.g., efficiency). Conversely, the relationship between UAS and Air Traffic Control (ATC) needs to be well-defined due to the impacts on ATC capacity these aircraft may present. Throughout the years, this impact may be lower than it is nowadays because the current lack of familiarity in this relationship contributes to higher workload levels. Thereupon, the primary goal of this research is to present a comprehensive review of the advancements in the integration of UAS in the National Airspace System (NAS) from different perspectives. We consider the challenges regarding simulation, final approach, and optimization of problems related to the interoperability of such systems in the airspace. Finally, we identify several open challenges in the field based on the existing state-of-the-art proposals.

Complying with the EU Regulation on multimodal transportation services requires sharing data on the National Access Points in one of the standards (e.g., NeTEx and SIRI) indicated by the European Commission. These standards are complex and of limited practical adoption. This means that datasets are natively expressed in other formats and require a data translation process for full compliance. This paper describes the solution to turn the authoritative data of three different transport stakeholders from Italy and Spain into a format compliant with EU standards by means of Semantic Web technologies. Our solution addresses the challenge and also contributes to build a multi-modal transport Knowledge Graph of interlinked and interoperable information that enables intelligent querying and exploration, as well as facilitates the design of added-value services.

Iranian-born bioengineer researcher Nima Enayati works on a robotic surgery machine during an interview with the Associated Press at the Polytechnic University of Milan, Italy, Tuesday, Jan. 31, 2017. An Iranian researcher at Milan's Polytechnic University, Enayati was refused check-in Monday at Milan's Malpensa Airport for his U.S.-bound flight on Turkish Airlines after the Trump administration's executive order came down. Iranian-born bioengineer researcher Nima Enayati works on a robotic surgery machine during an interview with the Associated Press at the Polytechnic University of Milan, Italy, Tuesday, Jan. 31, 2017. An Iranian researcher at Milan's Polytechnic University, Enayati was refused check-in Monday at Milan's Malpensa Airport for his U.S.-bound flight on Turkish Airlines after the Trump administration's executive order came down. Iranian-born bioengineer researcher Nima Enayati stands as he works on a robotic surgery machine during an interview with the Associated Press at the Polytechnic University of Milan, Italy, Tuesday, Jan. 31, 2017.