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24 results for “air traffic”
CoMobility project data: Warsaw road traffic, road traffic emissions, and air concentrations for greater Warsaw area
<p><strong>Introduction</strong></p> <p>Data here are for the Greater Warsaw area, Poland originating in the CoMobility project. It contains data relevant to traffic activity, emissions, air quality and related health studies in the area. Files contain road properties along with traffic volume and rushhour delays as well as emissions of NOx, NO2 and PM from road traffic on individual road segment level. Also 500m gridded surface air concentrations are included for PM2.5 and PM10, and for NOx, NO2.</p> <p><strong>Data production</strong></p> <p>Roads are from the macroscopic traffic model MTAW (Warsaw Municipality, 2016) (<em>Model Transportowy Aglomeracji Warszawskiej </em>in Polish). It was developed based on the 2015 comprehensive travel survey in Warsaw and it is the main strategic transport model for the Greater Warsaw area, revised most recently in 2019. </p> <p>The NERVE model (Grythe et al, 2022), developed by NILU, provides detailed estimates of greenhouse gas and air pollutant emissions specifically from road traffic. Using a bottom-up approach, it combines data from regional traffic model (RTM), vehicle fleet composition, and emission factors from the Handbook Emission Factors for Road Transport (HBEFA). NERVE can be set up to calculate emissions at various levels, including road link, municipality, or national levels. It is a tool researchers and policymakers use this model for environmental assessments, policy decisions, and constructing different emission scenarios. Its high level of detail makes it valuable not only for practical emissions estimation but also as a research tool. Emissions for other sources came from the Central Emission Database by the Environmental Protection - National Research Institute (IEP-NRI) in Poland (Gawuc et al., 2021). The background concentrations were taken from the Copernicus Atmospheric Monitoring Services (CAMS) ensemble forecast for 2019 (Marécal et al., 2015)</p> <p>The EPISODE model (Hamer et al. 2020), developed by NILU, is an Eulerian urban dispersion model designed to address the need for an accurate urban air quality model in support of policy, planning, and air quality management. EPISODE operates as a 3D grid model coupled with numerical weather prediction (NWP) data. It simulates dispersion from point and line sources to receptor points, with a focus on the photochemical production of ozone in urban areas. The model’s CityChem extension enhances its capabilities for complex pollution sources, incorporating numerical chemistry solvers, sub-grid photochemistry, and a simplified street canyon model. EPISODE serves as a valuable tool for understanding and managing air quality in urban environments.</p> <p><strong>Data files</strong></p> <p>The data on road traffic contains 60 084 road links that cover the Greater Warsaw area. The file input is a traffic file from the MTAW model and is processed and formatted with NREVE. The format is an ESRI shapefile with the following road parameters:</p> <p>“<em>DISTANCE</em>” -length of road segment in kilometers.</p> <p>“<em>CAPACITY</em>” -Hourly capacity of the road.</p> <p>“<em>SLOPE</em>” -Vertical gradientor slope of the road (in %)</p> <p>“<em>SPEEDLIM</em>” -Signed speed on the road (kilometers per hour)</p> <p>In addition there are traffic volume parameters;</p> <p>“<em>ADT_LIGHT</em>” – Annual Daily Traffic, light vehicles (personal cars + light duty vans) average derived from morning and evening peak hours 2019.</p> <p>“<em>ADT_HEAVY</em>” – Annual Daily Traffic, heavy duty vehicles average derived from morning and evening peak hours 2019.</p> <p>“<em>ADT_BUSES</em>” – Annual Daily Traffic, public transport buses average 2019.</p> <p>“<em>MRN_delay</em>” – delay during morning rush hour peak (%)</p> <p>“<em>EVE_delay</em>” – delay during evening rush hour peak (%)</p> <p>The files also contain the annual emissions:</p> <p>“<em>EM_NOx</em>” – 2019 annual emissions of NOx (gram).</p> <p>“<em>EM_ NO2</em>” – 2019 annual emissions of NOx (gram).</p> <p>“<em>EM_PM</em>” – 2019 annual emissions of NOx (gram).</p> <p>EPISODE output files for atmospheric concentration files are given on NetCDF file format. Concentrations are given as annual average grid concentration for each of the components. In addition, 42 000 spatially spread out receptor points gives the 2 meter concentrations to allow for surface air concentration levels at individual point locations. Furthermore, these allows for downgridding concentrations to higher resolution.</p> <p>The source contribution files are from EPISODE and gives atmospheric concentration fields for NOx, PM10 and PM2.5 from individual sources. The individual sources are</p> <p><em>“RDU” </em>-Road dust (PM only)</p> <p><em>“EXT”</em> – Exhaust (PM only)</p> <p><em>“TRA”</em> - Exhaust (NOx only)</p> <p><em>“IND” </em>– Industry</p> <p><em>“RES”</em> – Residential</p> <p><em>“OTH”</em> – Other (all other sources within the domain combined )</p> <p><em>“BGC”</em> – Background (all sources outside the domain combined )</p> <p> </p>
ADS-C Air Traffic Data Collected by the OpenSky Network
<p>ADS-C data collected by the OpenSky Network since 7th July 2023. </p> <p>Data underlying (Version 1.1)</p> <h1>A First Look at Exploiting the Automatic Dependent Surveillance-Contract Protocol for Open Aviation Research</h1> <p>https://journals.open.tudelft.nl/joas/article/view/7229</p>
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>
COMPAIR traffic and air quality sensor data
<p>Sensor data regarding traffic and air quality was gathered as part of the <a href="https://cordis.europa.eu/project/id/101036563">EU Horizon2020 COMPAIR project</a> in Europe. The pilot cities/regions are Berlin, Athens, Sofia, Plovdiv, and Flanders.<br><br>During the project, the data was published through an <a href="https://sensorthings.wecompair.eu/FROST-Server/v1.1/Things">OGC SensorThings API</a>. To persist after the project, the air quality related are available as CSV exports, with the retention of the API's structure (Location, Thing, Datastream, Sensor, ObservedProperty, and Observation). Observations about air quality contain sensor readings regarding nitrodioxide (NO2), black carbon (BC), particulate matter (PM1.0, PM2.5 and PM10), humidity and temperature. The NO2 observations are calibrated data streams.<br><br>The traffic observations remain available through the <a href="https://app.swaggerhub.com/apis-docs/telraam/Telraam-API/1.2.0">API of the Telraam platform</a>.<br><br><br></p>
Crowdsourced air traffic data from The OpenSky Network 2020 [CC-BY]
<p><strong>WARNING! </strong>This dataset is no longer updated after January 2022. Refer to the <a href="https://doi.org/10.5281/zenodo.3737101">original dataset</a> with different license terms for an up to date version.</p> <p><strong>Motivation</strong></p> <p>The data in this dataset is derived and cleaned from the full OpenSky dataset to illustrate the development of air traffic during the COVID-19 pandemic. It spans all flights seen by the network's more than 2500 members since 1 January 2019. More data will be periodically included in the dataset until the end of the COVID-19 pandemic.</p> <p><strong>License</strong></p> <p>Creative Commons CC-BY</p> <p>The only difference with the <a href="https://doi.org/10.5281/zenodo.3737101">original dataset</a> comes from anonymised aircraft information.</p> <p><strong>WARNING:</strong>This dataset is now longer updated after January 2022. The original dataset is still updated.</p> <p><strong>Disclaimer</strong></p> <p>The data provided in the files is provided as is. Despite our best efforts at filtering out potential issues, some information could be erroneous.</p> <ul> <li>Origin and destination airports are computed online based on the ADS-B trajectories on approach/takeoff: no crosschecking with external sources of data has been conducted.<br> Fields <strong>origin</strong> or <strong>destination</strong> are empty when no airport could be found.</li> <li>Aircraft information come from the OpenSky aircraft database. Fields <strong>typecode</strong> and <strong>registration</strong> are empty when the aircraft is not present in the database.</li> </ul> <p><strong>Description of the dataset</strong></p> <p>One file per month is provided as a csv file with the following features:</p> <ul> <li><strong>callsign</strong>: the identifier of the flight displayed on ATC screens (usually the first three letters are reserved for an airline: AFR for Air France, DLH for Lufthansa, etc.)</li> <li><strong>number</strong>: the commercial number of the flight, when available (the matching with the callsign comes from public open API)</li> <li><strong>aircraft_uid</strong>: a unique anonymised identifier for aircraft;</li> <li><strong>typecode</strong>: the aircraft model type (when available);</li> <li><strong>origin</strong>: a four letter code for the origin airport of the flight (when available);</li> <li><strong>destination</strong>: a four letter code for the destination airport of the flight (when available);</li> <li><strong>firstseen</strong>: the UTC timestamp of the first message received by the OpenSky Network;</li> <li><strong>lastseen</strong>: the UTC timestamp of the last message received by the OpenSky Network;</li> <li><strong>day</strong>: the UTC day of the last message received by the OpenSky Network;</li> <li><strong>latitude_1</strong>, <strong>longitude_1</strong>, <strong>altitude_1</strong>: the first detected position of the aircraft;</li> <li><strong>latitude_2</strong>, <strong>longitude_2</strong>, <strong>altitude_2</strong>: the last detected position of the aircraft.</li> </ul> <p><strong>Examples</strong></p> <p>Possible visualisations and a more detailed description of the data are available at the following page:<br> <<a href="https://traffic-viz.github.io/scenarios/covid19.html">https://traffic-viz.github.io/scenarios/covid19.html</a>></p> <p><strong>Credit</strong></p> <p>Martin Strohmeier, Xavier Olive, Jannis Lübbe, Matthias Schäfer, and Vincent Lenders<br> <strong>"</strong>Crowdsourced air traffic data from the OpenSky Network 2019–2020<strong>"</strong><br> <em>Earth System Science Data</em> 13(2), 2021<br> <a href="https://doi.org/10.5194/essd-13-357-2021">https://doi.org/10.5194/essd-13-357-2021</a></p> <p> </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>
Madrid datasets (air quality, meteorological and traffic data)
<p>The datasets consist of air quality, meteorological, and traffic data from January to June 2019 and from January to June 2020. The following are the features of the datasets: Nitrogen dioxide, Wind speed, Wind direction (u component, v component, north, northeast, east, southeast, south, southwest, west, northwest), Pressure, Temperature, Humidity, Solar irradiance, Intensity, Occupancy time, Load, Average traffic speed.</p>
Madrid datasets (air quality, meteorological and traffic data) of 2019 and 2022
<p>The datasets consist of air quality, meteorological, and traffic data from January to June 2019 and from January to June 2022. The following are the features of the datasets: Nitrogen dioxide, Wind speed, Wind direction (in degrees), Temperature, Humidity, Pressure, Solar irradiance, Intensity, Occupancy time, Load, Average traffic speed, Wind direction (after converting to categorical data (north, northeast, east, southeast, south, southwest, west, northwest) and passing through One Hot Encoder). The following are the names of the above features (the names of the columns in the files): 'NO2', 'windSpeed', 'windDir', 'Temp', 'Humidity', 'Pressure', 'SolarRad', 'intensidad', 'ocupacion', 'carga', 'vmed', 'windDir_Categ_east', 'windDir_Categ_north', 'windDir_Categ_northeast', 'windDir_Categ_northwest', 'windDir_Categ_south', 'windDir_Categ_southeast', 'windDir_Categ_southwest', 'windDir_Categ_west'.</p> <p><a href="https://zenodo.org/api/files/5af044df-ea61-41ee-a43f-4d095f43ca55/Mad_2019_winddir.csv">Mad_2019_winddir.csv</a> and <a href="https://zenodo.org/api/files/5af044df-ea61-41ee-a43f-4d095f43ca55/Mad_2019_winddir.csv">Mad_2022_winddir.csv</a> include the above features for the defined grid in the city of Madrid for January-June 2019 and January-June 2022, respectively, with the following dimension: 4344×340×19: (January-June 2019); 4343 × 340 × 19 (January-June 2022).</p> <p><a href="https://zenodo.org/api/files/5af044df-ea61-41ee-a43f-4d095f43ca55/Mad_Station_2019.csv">Mad_Station_2019.csv</a> and <a href="https://zenodo.org/api/files/5af044df-ea61-41ee-a43f-4d095f43ca55/Mad_Station_2019.csv">Mad_Station_2022.csv</a> include the above features with the following dimension: 4344 × 24 × 19: (January-June 2019); 4343 × 24 × 19 (January-June 2022), extracted from the defined grid for cells only where air quality monitoring stations exist.</p>
Incorporating real-time traffic data into air quality mapping uncovers more severe exposure disparities at the community level
<p>Data and codes for the XGBoost model training in our manuscript titled "Incorporating real-time traffic data into air quality mapping uncovers more severe exposure disparities at the community level"</p>
Crowdsourced air traffic data from The OpenSky Network 2020 [CC-BY]
<p><strong>Motivation</strong></p> <p>The data in this dataset is derived and cleaned from the full OpenSky dataset to illustrate the development of air traffic during the COVID-19 pandemic. It spans all flights seen by the network's more than 2500 members since 1 January 2020. More data will be periodically included in the dataset until the end of the COVID-19 pandemic.</p> <p><strong>License</strong></p> <p>Creative Commons CC-BY</p> <p>The only difference with the <a href="https://zenodo.org/record/3928550">original dataset</a> comes from anonymised aircraft information.</p> <p><strong>Disclaimer</strong></p> <p>The data provided in the files is provided as is. Despite our best efforts at filtering out potential issues, some information could be erroneous.</p> <ul> <li>Origin and destination airports are computed online based on the ADS-B trajectories on approach/takeoff: no crosschecking with external sources of data has been conducted.<br> Fields <strong>origin</strong> or <strong>destination</strong> are empty when no airport could be found.</li> <li>Aircraft information come from the OpenSky aircraft database. Fields <strong>typecode</strong> and <strong>registration</strong> are empty when the aircraft is not present in the database.</li> </ul> <p><strong>Description of the dataset</strong></p> <p>One file per month is provided as a csv file with the following features:</p> <ul> <li><strong>callsign</strong>: the identifier of the flight displayed on ATC screens (usually the first three letters are reserved for an airline: AFR for Air France, DLH for Lufthansa, etc.)</li> <li><strong>number</strong>: the commercial number of the flight, when available (the matching with the callsign comes from public open API)</li> <li><strong>aircraft_uid</strong>: a unique anonymised identifier for aircraft;</li> <li><strong>typecode</strong>: the aircraft model type (when available);</li> <li><strong>origin</strong>: a four letter code for the origin airport of the flight (when available);</li> <li><strong>destination</strong>: a four letter code for the destination airport of the flight (when available);</li> <li><strong>firstseen</strong>: the UTC timestamp of the first message received by the OpenSky Network;</li> <li><strong>lastseen</strong>: the UTC timestamp of the last message received by the OpenSky Network;</li> <li><strong>day</strong>: the UTC day of the last message received by the OpenSky Network;</li> <li><strong>latitude_1</strong>, <strong>longitude_1</strong>, <strong>altitude_1</strong>: the first detected position of the aircraft;</li> <li><strong>latitude_2</strong>, <strong>longitude_2</strong>, <strong>altitude_2</strong>: the last detected position of the aircraft.</li> </ul> <p><strong>Examples</strong></p> <p>Possible visualisations and a more detailed description of the data are available at the following page:<br> <<a href="https://traffic-viz.github.io/scenarios/covid19.html">https://traffic-viz.github.io/scenarios/covid19.html</a>></p> <p><strong>Credit</strong></p> <p>If you use this dataset, please cite the original OpenSky paper:</p> <p>Matthias Schäfer, Martin Strohmeier, Vincent Lenders, Ivan Martinovic and Matthias Wilhelm.<br> "Bringing Up OpenSky: A Large-scale ADS-B Sensor Network for Research".<br> In<em> Proceedings of the 13th IEEE/ACM International Symposium on Information Processing in Sensor Networks (IPSN)</em>, pages 83-94, April 2014.</p> <p>and the traffic library used to derive the data:</p> <p>Xavier Olive.<br> "traffic, a toolbox for processing and analysing air traffic data."<br> <em>Journal of Open Source Software</em> 4(39), July 2019.</p>
Home Air Filtration for Traffic-Related Air Pollution
ClinicalTrials.gov study NCT04279249. IPD Sharing: NO. Countries: 1. Publications: 1.
Effects of air pollution exposure on inflammatory and endurance performance in recreationally trained cyclists adapted to traffic-related air pollution
<p>Supplemental Table 1. Baseline values for blood parameters.</p>
Hyperlocal monitoring of traffic-related air pollution to assess near-term impacts of sustainable transportation interventions
Open the record for dataset details and reuse information.
The human circulating miRNome reflects multiple diseases associated with short-term exposure to traffic-related air pollution
GEO Series GSE106221. Homo sapiens. 48 samples. Type: Non-coding RNA profiling by high throughput sequencing.
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>
Acute Health Effects of Traffic-Related Air Pollution Exposure
ClinicalTrials.gov study NCT04153539. IPD Sharing: NO. Countries: 1. Publications: 0.
Health Effects of Wood Smoke and Traffic-Related Air Pollution Exposures: a Necessary Comparison
ClinicalTrials.gov study NCT06749093. IPD Sharing: YES. Countries: 1. Publications: 0.
The Use of L-arginine to Mitigate the Cardiovascular Effects of Exposure to Traffic-related Air Pollution
ClinicalTrials.gov study NCT03433963. IPD Sharing: NO. Countries: 1. Publications: 0.
Night-time air traffic on the road
<u>Source</u>: Europeana <br><u>4DCity URL</u>: <a href="https://4dcity.org/imgupload/1652523751.9555.jpg">https://4dcity.org/imgupload/1652523751.9555.jpg</a> <br><u>Original Image URL</u>: <a href="https://api.europeana.eu/thumbnail/v2/url.json?uri=https%3A%2F%2Fwww.openbeelden.nl%2Fimages%2F679809%2FNachtelijk_vliegverkeer_op_de_weg_%25280_35%2529.png&type=VIDEO">https://api.europeana.eu/thumbnail/v2/url.json?uri=https%3A%2F%2Fwww.openbeelden.nl%2Fimages%2F679809%2FNachtelijk_vliegverkeer_op_de_weg_%25280_35%2529.png&type=VIDEO</a> <br><br><u>Image-Metadata:</u><br>Filename: 1652523751.9555.jpg<br>Image Dimensions: 360x288<br>Megapixels: 0.10 MP<br>Filesize: 47.80 KB<br>
A lightweight speech recognition method with target-swap knowledge distillation for Mandarin air traffic control communications
<p>Mandarin air traffic control communications (ATCC) dataset for the paper "A lightweight speech recognition method with target-swap knowledge distillation for Mandarin air traffic control communications".</p>
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International Brain Laboratory public data
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OpenNeuro
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