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4 results for “Air Traffic Management”
Air Traffic Management hotspots in Europe with airline cost functions
<p>This dataset contains data related to Air Traffic Management hotspots. Hotspots are created in the European airspaces when capacity for some pieces of airspace are foreseen to be infringed due to weather, congestion, strikes, etc. This anonymised dataset records around 5900 hotspots happening at 22 major European airports. These hotspots are generated through a simulator called Mercury that is fed with real data (in particular, real capacity reduction that happened in Europe for over a year, schedules etc) and simulates a day of operation, randomising events like delays, cancellation etc. More details on mercury can be found here [1] and [2].</p> <p>The data, anonymised in terms of airports and airlines, is a dictionary which is structured as follows:</p> <p>- the top level key is the id of the airport, the value is list a of all regulations available for this airport.</p> <p>- each item of the list is a dictionary, with keys:</p> <p> -- 'slot_times': list of all slots available to flights for this hotspot/regulation, in minutes since midnight.</p> <p> -- 'etas': list of initial estimated arrival times of flights involved in the regulation, in minutes since midnight.</p> <p> -- 'flight_ids': list of flight ids (in the same order than etas)</p> <p> -- 'cost_vectors': list of cost vectors. Each item is a list itself, of length equal to the slot_times list. Each element of that list is the estimated cost that the airline owning the flight would incur, were the flight be assigned to this slot, in terms of: maintenance, crew, rebooking fees, market value loss, and curfew infringement, in 2014 euros. This cost is computed within the Mercury model and is based on [3].</p> <p> -- 'airlines_flights': dictionary whose keys are airline ids and values are lists of ids of flights owned by the airline.</p> <p>[1] https://www.sciencedirect.com/science/article/abs/pii/S0968090X21003600 </p> <p>[2] G. Gurtner, L. Delgado, and D.Valput, “An agent-based model for air transportation to capture network effects in assessing delay management mechanisms”, Transportation Research Part C: emerging Technologies, 2021.</p> <p>Pre-print available here: <a href="https://westminsterresearch.westminster.ac.uk/item/v956w/an-agent-based-model-for-air-transportation-to-capture-network-effects-in-assessing-delay-management-mechanisms">https://westminsterresearch.westminster.ac.uk/item/v956w/an-agent-based-model-for-air-transportation-to-capture-network-effects-in-assessing-delay-management-mechanisms</a></p> <p>[3] A. J. Cook and G. Tanner, “European airline delay cost reference values - updated and extended values (Version 4.1),” University of Westminster, London, 2015a</p>
Classification of Artificial Intelligence and eXplainable Artificial Intelligence publications in Air Traffic Management
<p>v1.0 version used and partially published in "A Survey on Artificial Intelligence (AI) and eXplainable AI in Air Traffic Management: Current Trends and Development with Future Research Trajectory". In this version, it references mainly Transportation Reasearch Part C, ICRAT, Journal of ATM, and ATM Seminar, IEEE transaction on ITS, but not only</p>
Real-Time Work Zone Traffic Management via Unmanned Air Vehicles
<p>Highway work zones are prone to traffic accidents when congestion and queues develop. Vehicle queues expand at a rate of 1 mile every 2 minutes. Back-of-queue, rear-end crashes are the most common work zone crash, endangering the safety of motorists, passengers, and construction workers. The dynamic nature of queuing in the proximity of highway work zones necessitates traffic management solutions that can monitor and intervene in real time. Fortunately, recent progress in sensor technology, embedded systems, and wireless communication coupled to lower costs are now enabling the development of real-time, automated, “intelligent” traffic management systems that address this problem. The goal of this project was to perform preliminary research and proof of concept development work for the use of UAS in real- time traffic monitoring of highway construction zones in order to create real-time alerts for motorists, construction workers, and first responders. The main tasks of the proposed system was to collect traffic data via the UAV camera, analyze that a UAV based highway construction zone monitoring systems would be capable of detecting congestion and back-of-queue information, and alerting motorists of stopped traffic conditions, delay times, and alternate route options. Experiments were conducted using UAS to monitor traffic and collect traffic videos for processing. Prototype software was created to analyze this data. The software was successful in detecting vehicle speed from zero mph to highway speeds. Review of available mobile traffic apps were conducted for future integration with advanced iterations of the UAV and software system that has been created by this research. This project has proven that UAS monitoring of highway construction zones and real-time alerts to motorists, construction crews, and first responders is possible in the near term and future research is needed to further development and implement the innovative UAS traffic monitoring system developed by this research.</p>
The NASA Air Traffic Management Ontology (atmonto)
The NASA ATM (Air Traffic Management) Ontology describes classes, properties, and relationships relevant to the domain of air traffic management, and represents information pertinent to a broad and diverse set of interacting components in the US and the global airspace, including flights, aircraft, manufacturers, airports, airlines, air routes, facilities, air traffic advisories, weather phenomena, and many others. Three different variants of the ATM Ontology are provided: atmontoCore, atmonto, and atmontoPlus. The atmonto variant extends the core ontology by adding instances corresponding to key infrastructure components of the US National Airspace System (NAS).
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.