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374 results for “Traffic”

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zenodo52/100

CoMobility project data: Warsaw road traffic, road traffic emissions, and air concentrations for greater Warsaw area

Introduction 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. Data production Roads are from the macroscopic traffic model MTAW (Warsaw Municipality, 2016) (Model Transportowy Aglomeracji Warszawskiej 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.  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) 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. Data files 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: “DISTANCE” -length of road segment in kilometers. “CAPACITY” -Hourly capacity of the road. “SLOPE” -Vertical gradientor slope of the road (in %) “SPEEDLIM” -Signed speed on the road (kilometers per hour) In addition there are traffic volume parameters; “ADT_LIGHT” – Annual Daily Traffic, light vehicles (personal cars + light duty vans) average derived from morning and evening peak hours 2019. “ADT_HEAVY” – Annual Daily Traffic, heavy duty vehicles average derived from morning and evening peak hours 2019. “ADT_BUSES” – Annual Daily Traffic, public transport buses average 2019. “MRN_delay” – delay during morning rush hour peak (%) “EVE_delay” – delay during evening rush hour peak (%) The files also contain the annual emissions: “EM_NOx” – 2019 annual emissions of NOx (gram). “EM_ NO2” – 2019 annual emissions of NOx (gram). “EM_PM” – 2019 annual emissions of NOx (gram). 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. 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 “RDU” -Road dust (PM only) “EXT” – Exhaust  (PM only) “TRA” - Exhaust  (NOx only) “IND” – Industry “RES” – Residential “OTH” – Other (all other sources within the domain combined ) “BGC” – Background (all sources outside the domain combined )

opencc-by-4.0Apr 2024View details →

edi52/100

North Temperate Lakes LTER: Boat Traffic Through Yahara Locks 1976 - 2011

One of the dominant uses of the Madison area lakes is for boating. In order to develop a long term data set on the temporal fluctuations and trends in such activity, the LTER project has obtained records of boat traffic that passes through the locks at the head of the Yahara River on its stretch between Lake Mendota and Lake Monona. This data was gathered by the Dane County Department of Public Works as part of the County''s ongoing monitoring of its own facilities and their use. This data set will be augmented as the Department of Public Works makes updates available. Data is currently only available through 2011. Sampling Frequency: tallied daily April through October; exact starting dates vary each year Number of sites: 1

openCC (other)Nov 2022View details →

zenodo48/100

ADS-C Air Traffic Data Collected by the OpenSky Network

ADS-C data collected by the OpenSky Network since 7th July 2023.  Data underlying (Version 1.1) <h1>A First Look at Exploiting the Automatic Dependent Surveillance-Contract Protocol for Open Aviation Research</h1> https://journals.open.tudelft.nl/joas/article/view/7229

opencc-by-4.0Oct 2023View details →

zenodo48/100

Open-source traffic and CO2 emission dataset for commercial aviation

This record is a global open-source passenger air traffic dataset primarily dedicated to the research community.  It gives a seating capacity available on each origin-destination route for a given year, 2019, and the associated aircraft and airline when this information is available.  Context on the original work is given in the related articles (<a href="https://doi.org/10.59490/joas.2024.7365">https://doi.org/10.59490/joas.2024.7365,</a> <a href="https://doi.org/10.59490/joas.2023.7201">https://doi.org/10.59490/joas.2023.7201)</a> and on the associated GitHub page (<a href="https://github.com/AeroMAPS/AeroSCOPE/">https://github.com/AeroMAPS/AeroSCOPE/</a>). A simple data exploration interface will be available at <a href="www.aeromaps.eu/aeroscope">www.aeromaps.eu/aeroscope.</a> The dataset was created by aggregating various available open-source databases with limited geographical coverage. It was then completed using a route database created by parsing Wikipedia and Wikidata, on which the traffic volume was estimated using a machine learning algorithm (XGBoost) trained using traffic and socio-economical data.   <h4> 1- DISCLAIMER</h4> The dataset was gathered to allow highly aggregated analyses of the air traffic, at the continental or country levels. At the route level, the accuracy is limited as mentioned in the associated article and improper usage could lead to erroneous analyses.  Although all sources used are open to everyone, the Eurocontrol database is only freely available to academic researchers. It is used in this dataset in a very aggregated way and under several levels of abstraction. As a result, it is not distributed in its original format as specified in the contract of use. As a general rule, we decline any responsibility for any use that is contrary to the terms and conditions of the various sources that are used. In case of commercial use of the database, please contact us in advance. <h4> 2- DESCRIPTION</h4> Each data entry represents an (Origin-Destination-Operator-Aircraft type) tuple. Please refer to the support article for more details (see above). The dataset contains the following columns: <ul> <li>"First column" : index</li> <li>airline_iata : IATA code of the operator in nominal cases. An ICAO -> IATA code conversion was performed for some sources, and the ICAO code was kept if no match was found.</li> <li>acft_icao : ICAO code of the aircraft type</li> <li>acft_class : Aircraft class identifier, own classification. <ul> <li>WB: Wide Body</li> <li>NB: Narrow Body</li> <li>RJ: Regional Jet</li> <li>PJ: Private Jet</li> <li>TP: Turbo Propeller</li> <li>PP: Piston Propeller</li> <li>HE: Helicopter</li> <li>OTHER</li> </ul> </li> <li>seymour_proxy: Aircraft code for Seymour Surrogate (https://doi.org/10.1016/j.trd.2020.102528), own classification to derive proxy aircraft when nominal aircraft type unavailable in the aircraft performance model.</li> <li>source: Original data source for the record, before compilation and enrichment. <ul> <li>ANAC: Brasilian Civil Aviation Authorities</li> <li>AUS Stats: Australian Civil Aviation Authorities</li> <li>BTS: US Bureau of Transportation Statistics T100</li> <li>Estimation: Own model, estimation on Wikipedia-parsed route database</li> <li>Eurocontrol: Aggregation and enrichment of R&D database</li> <li>OpenSky</li> <li>World Bank</li> </ul> </li> <li>seats: Number of seats available for the data entry, AFTER airport residual scaling</li> <li>n_flights: Number of flights of the data entry, when available</li> <li>iata_departure, iata_arrival : IATA code of the origin and destination airports. Some BTS inhouse identifiers could remain but it is marginal.</li> <li>departure_lon, departure_lat, arrival_lon, arrival_lat : Origin and destination coordinates, could be NaN if the IATA identifier is erroneous</li> <li>departure_country, arrival_country: Origin and destination country ISO2 code. WARNING: disable NA (Namibia) as default NaN at import</li> <li>departure_continent, arrival_continent: Origin and destination continent code. WARNING: disable NA (North America) as default NaN at import</li> <li>seats_no_est_scaling: Number of seats available for the data entry, BEFORE airport residual scaling</li> <li>distance_km: Flight distance (km)</li> <li>ask: Available Seat Kilometres</li> <li>rpk: Revenue Passenger Kilometres (simple calculation from ASK using IATA average load factor)</li> <li>fuel_burn_seymour: Fuel burn per flight (kg) when seymour proxy available</li> <li>fuel_burn: Total fuel burn of the data entry (kg)</li> <li>co2: Total CO2 emissions of the data entry (kg)</li> <li>domestic: Domestic/international boolean (Domestic=1, International=0)</li> </ul>   <h4>3- Citation</h4> Please cite the support paper instead of the dataset itself.  <blockquote> Salgas, A., Sun, J., Delbecq, S., Planès, T., & Lafforgue, G. (2024). Compilation and Applications of an Open-Source Dataset on Global Air Traffic Flows and Carbon Emissions. Journal of Open Aviation Science. <a href="https://doi.org/10.59490/joas.2024.7365">https://doi.org/10.59490/joas.2023.7201</a> </blockquote>

opengpl-3.0-or-laterOct 2023View details →

zenodo48/100

Short-term traffic flow prediction based on secondary hybrid decomposition and deep echo state networks

The publication titled "Short-term traffic flow prediction based on secondary hybrid decomposition and deep echo state networks" is supported by the STRIDE K3 project. The dataset used in the publication is uploaded here.

opencc-by-4.0Dec 2023View details →

zenodo48/100

Air Traffic Management hotspots in Europe with airline cost functions

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]. The data, anonymised in terms of airports and airlines, is a dictionary which is structured as follows: - the top level key is the id of the airport, the value is list a of all regulations available for this airport. - each item of the list is a dictionary, with keys:  -- 'slot_times': list of all slots available to flights for this hotspot/regulation, in minutes since midnight.  -- 'etas': list of initial estimated arrival times of flights involved in the regulation, in minutes since midnight.  -- 'flight_ids': list of flight ids (in the same order than etas)  -- '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].  -- 'airlines_flights': dictionary whose keys are airline ids and values are lists of ids of flights owned by the airline. [1] https://www.sciencedirect.com/science/article/abs/pii/S0968090X21003600  [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. 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> [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

opencc-by-4.0Sep 2023View details →

zenodo48/100

The data that support the findings of a review paper "From urban data to city-scale models: A review of traffic simulation case studies"

This dataset contains the data that were used in a review paper "From urban data to city-scale models: A review of traffic simulation case studies". It contains the following files: <ul> <li>keywords with counts.txt -  list of keywords and their counts in the considered corpus of traffic simulation case studies. The data were used to produce Figure 2 and Figure 3 in the paper.</li> <li>Papers analysis.xlsx - Excel file containing the data on the reviewed studies. The document has the following sheets: <ul> <li> Appendix A - contains a table short reference, location, simulation period, spatial scale, simulated units and marked categories for a paper;</li> <li>Geography - contains data on geographical distribution of simulated areas between world regions and countries, these data were used to produce Figure 4 in the paper;</li> <li>Software tools - contains data on simulation tools used in the studies. </li> <li>Journals and conferences - contains data on where the reviewed papers were published.</li> </ul> </li> </ul>

opencc-zeroAug 2024View details →

zenodo48/100

COMPAIR traffic and air quality sensor data

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. 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. 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>.

opencc-by-4.0Oct 2024View details →

zenodo48/100

IDLAB-UA Dataset for Traffic Classification using Spectrum Data

This dataset contains IQ values of physical layer (L1) packets associated with WLAN transmission and the set of labels that associated each of the packets to properties/features at different radio stack layer (from L1 to L7).

opencc-by-4.0Aug 2021View details →

zenodo44/100

Host Network Traffic 2019

Dataset Summary <ul> <li>Timespan: 2019-01-01 : 2019-12-31</li> <li>Granularity: 1-hour disjoint time windows</li> <li># of characteristics observed: 9</li> <li>Hosts observed: 65536</li> <li>Labels: included</li> <li>Unzipped volume: approx. 10 GB</li> </ul> Dataset Origins Dataset was collected over the whole year  2019. The observation points for the collection of IP flows were located at the borders of the university campus network. The campus university network has /16 CIDR IPv4 network range at disposal and contains various network segments from segments connecting dormitories, over server segments, to a segment containing working stations of university administrative workers. A host in our dataset is identified by its source IPv4 address.   Variables The dataset contains the following variables: <ul> <li>Aggregations - created sums of the individual variables over a one-hour interval: <ul> </ul> <ul> <li># of flows  - number of flows for a given source IP </li> <li># of packets  - number of packets for a given source IP</li> <li># of bytes  - number of packets for a given source IP</li> <li>flow duration  - average flow duration in seconds</li> </ul> </li> <li>Distinct Counts - count of distinct values for each variable over a one-hour window <ul> <li># of peers  - number of distinct communication peers for a given source IP</li> <li># of ports  - number of distinct destination ports for a given source IP</li> <li># of protocols - number of distinct communication protocols for a given source IP</li> <li># of AS numbers - number of distinct destination AS numbers for a given source IP</li> <li># of countries  - number of distinct destination countries for a given source </li> </ul> </li> </ul> Dataset Structure <ul> <li>Dataset Files - each variable is contained in one Comma-Separated File (.csv) file <ul> <li>Row index -  timestamp of the observation window (8760 rows)</li> <li>Columns index -  anonymized IP addresses (65536 columns)</li> </ul> </li> <li>Label File - contains labels of the individual IP addresses from the Dataset Files <ul> <li>Row index - anonymized IP addresses (65536 rows)</li> <li>Columns index - labels for the IP addresses <ul> <li>Subnet - ID of a subnet - hosts belonging to the same subnet have the same Id.</li> <li>Subnet_range - CIDR range of a subnet</li> <li>Unit - an ID of  administrative unit owning the network range</li> <li>Sub-unit  - an ID of  administrative sub-unit owning the network range</li> <li>Subnet_label -  subnet label <ul> <li>Servers - selected subnets containing mostly servers (133.250.178.0/24, 133.250.163.0/24)</li> <li>Workstations - selected subnets containing mostly workstations (133.250.146.0/24, 133.250.157.128/25)</li> </ul> </li> </ul> </li> </ul> </li> </ul> Further notes <ul> <li>N/A values <ul> <li>Variables - means that in a given observation window, the host did not communicate</li> <li>Labels - no additional information on this IP is available</li> </ul> </li> <li>Dataset load  <ul> <li> <pre><code class="language-python">df = pd.read_csv(<filename>,header=[0], index_col=[0])</code></pre> </li> </ul> </li> </ul>

opencc-by-4.0Apr 2020View details →

zenodo44/100

Traffic Detection Datasets

Public (anonymized) road traffic detection datasets from Huawei Munich Research Center. Traffic detection datasets from a variety of traffic sensors (i.e. induction loops). The data is useful for traffic indexing, dominant flows detection, forecasting traffic patterns, and adjusting stop-light control parameters, i.e. cycle length, offset and split times. There are three datasets: <ul> <li>RM is a real-life dataset collected in the period of March 2020 from traffic detectors in an area of a Chinese city. The detectors were located in 6 intersections monitoring the traffic on 44 road edges. Each detector was collecting data every 1 second for all the road edges in its radius. In total, 2894174 detections were collected, from 337089 different vehicles.</li> <li>COM is a synthetic dataset that generated in the same road network as the real-life data RM and RD, with the difference that we included detectors in the 2 intersections where the real scenario didn’t have. Then, we generated equally random trips over the road network. In total we generated 18910 detections from 7500 vehicles.</li> <li>GRID was created using the SUMO Simulation of Urban Mobility [2]. We randomly generated trips on a 10𝑥10 intersections grid road network using a utility from SUMO that equally generates trips over the road network. Then, running the simulation and using TraCI Traffic Control Interface library [3] we read the simulation data and collect the detections. The simulation collected data include 1806141 detections from 135618 different vehicles.</li> </ul> The datasets were used in the Querying Top-k Dominant Traffic Flows on Large Urban Road Networks [1] paper. [1] Stella Maropaki, Paolo Sottovia, and Stefano Bortoli. 2021. Querying Top-k Dominant Traffic Flows on Large Urban Road Networks. In 2021 24th International Conference on Extending Database Technology (EDBT).

openother-openJan 2021View details →

zenodo44/100

Synthetic mobile service traffic time series

This dataset contains synthetic mobile service traffic time series used in the paper titled "kaNSaaS: Combining Deep Learning and Optimization for Practical Overbooking of Network Slices", presented at ACM MobiHoc 2023 in Washington, USA. It is composed of 20 time series representing the fluctuations of demands for diverse services categorized under 5G types, including enhanced Mobile Broadband (eMBB), ultra-Reliable Low Latency Communication (uRLLC), and massive Machine Type Communication (mMTC). The time series cover a period of XXX days, and were shown to yield similar properties as those observed in real-world traffic collected in a production mobile network.

opencc-by-4.0Oct 2023View details →

zenodo44/100

The price of safety: Order picking in warehouses with in-house traffic regulations (Supplementary material)

In what follows, you will find the code and results of the paper: "The price of safety: Order picking in warehouses with in-house traffic regulations" published in IISE Transactions.   List of files: - Zip file: "Order Picking Problem with in-house traffic regulations" containing C# Code used to generate solutions for all safety policies - Result.csv containing all generated results - createPlots.py containing code to generate figures and tables from the paper   The C# code is object-oriented and contains a Main function in the Program.cs file that converts the Example.OPP file with the InstanceReaders to an OPPInstance and uses the Solve function from either the DynamicProgrammic.cs or RuralPostman.cs file to solve the OPPInstance with all the TrafficRegulations as described in the paper.   The Example.OPP defines the Depot location (0: decentral, 1: central), AisleLength, i.e. the number of pick positions within each aisle, and other dimensions of the warehouse. Finally, the items are defined by their picking aisle, shelf, position in the shelf, and region.   The dynamic program (DP) described in the paper is implemented in DynamicProgrammic.cs.  A HashSet of DPNode represents each layer of the DP. A DPNode basically consists of components, nodeDegrees, and a value. Depending on the TrafficRegulation the nodeDegrees are either NodeDegreeClassic, i.e. Null, Uneven, or Even, or NodeDegreeInAndOutDifference, i.e. the difference of the in- and out-degree. To construct the solution at the end, the inEdge is also saved for each DPNode and the additional member depotIsConnected ensures that the depot is visited. The DPNodes in the next layer of the DP are created by the functions MakeNextLayerVertical and MakeNextLayerHorizontal by determining all possibleTransitions per node in the current layer and combining them into a newNode. Products are stored with their position on the shelves in the item list within a PickingAisle. All vertical possibleTransitions are determined in a preprocessing step depending on the TrafficRegulations and are saved within the respective PickingAisle. All horizontal possibleTransitions are determined during the DP with specific functions depending on the TrafficRegulation in HorizontalTransition.cs. When the layers are created, the best feasible DPNode per layer is saved and the best one, i.e. the one with the lowest value, is returned at the end.   The paper describes that certain safety policies cannot be solved with the DP. These OPPInstances are solved as a RuralPostman problem (RPP) by generating a Graph that adopts the rectangular structure of the warehouse. Within the Graph, requiredEdges are determined that correspond to PickingAisles containing items. The resulting RPP can be transformed into a traveling salesman problem (TSP) as described by applying an arc-oriented Dijkstra or, in certain cases, to a generalized TSP (GTSP) where one of the two directed edges must be visited. If necessary, the GTSP is transformed to an asymmetric TSP in GTSPInstance and then solved with TSPSolver using LKH-3.exe (Helsgaun 2017, http://webhotel4.ruc.dk/~keld/research/LKH-3/). To use LKH-3.exe, the TSP instance is saved in a TSPLIB format and a parameter file (.par) for LKH and a solution file (.sol) are created in the bin folder. These files are named according to the name specified in the instance.Solve function, where one can also choose to save or delete these files afterward.   For more information on LKH-3 see: Keld Helsgaun: An Extension of the Lin-Kernighan-Helsgaun TSP Solver for Constrained Traveling Salesman and Vehicle Routing Problems (Technical Report, Roskilde University, 2017)   Evaluation.py A Python script that generates figures 8, 9, and 10 and tables 6, 7 and 8 (in csv-format) of the paper by processing data from Results.csv. It requires Results.csv to be in the same directory as the code. It also requires the following Python packages: - matplotlib - pandas - seaborn

opencc-by-4.0Nov 2024View details →

zenodo44/100

Fatal traffic accidents in Catalonia

This dataset contains 1024 fatal traffic accidents ocurred in Catalonia between June 13, 2014 and October 20, 2021. Each record has data about the date and time of the accident, it's localization and a description. The dataset has been obtained from applying web scrapping techniques on the Catalonia's Government (Generalitat de Catalunya) website: http://transit.gencat.cat/ca/el_servei/premsa_i_comunicacio/comunicats_d_accidents_mortals/

opencc-by-4.0Nov 2021View details →

zenodo44/100

Crowdsourced air traffic data from The OpenSky Network 2020 [CC-BY]

WARNING! 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. Motivation 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. License Creative Commons CC-BY The only difference with the <a href="https://doi.org/10.5281/zenodo.3737101">original dataset</a> comes from anonymised aircraft information. WARNING:This dataset is now longer updated after January 2022. The original dataset is still updated. Disclaimer The data provided in the files is provided as is. Despite our best efforts at filtering out potential issues, some information could be erroneous. <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. Fields origin or destination are empty when no airport could be found.</li> <li>Aircraft information come from the OpenSky aircraft database. Fields typecode and registration are empty when the aircraft is not present in the database.</li> </ul> Description of the dataset One file per month is provided as a csv file with the following features: <ul> <li>callsign: 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>number: the commercial number of the flight, when available (the matching with the callsign comes from public open API)</li> <li>aircraft_uid: a unique anonymised identifier for aircraft;</li> <li>typecode: the aircraft model type (when available);</li> <li>origin: a four letter code for the origin airport of the flight (when available);</li> <li>destination: a four letter code for the destination airport of the flight (when available);</li> <li>firstseen: the UTC timestamp of the first message received by the OpenSky Network;</li> <li>lastseen: the UTC timestamp of the last message received by the OpenSky Network;</li> <li>day: the UTC day of the last message received by the OpenSky Network;</li> <li>latitude_1, longitude_1, altitude_1: the first detected position of the aircraft;</li> <li>latitude_2, longitude_2, altitude_2: the last detected position of the aircraft.</li> </ul> Examples Possible visualisations and a more detailed description of the data are available at the following page: <<a href="https://traffic-viz.github.io/scenarios/covid19.html">https://traffic-viz.github.io/scenarios/covid19.html</a>> Credit Martin Strohmeier, Xavier Olive, Jannis Lübbe, Matthias Schäfer, and Vincent Lenders "Crowdsourced air traffic data from the OpenSky Network 2019–2020" Earth System Science Data 13(2), 2021 <a href="https://doi.org/10.5194/essd-13-357-2021">https://doi.org/10.5194/essd-13-357-2021</a>

opencc-by-4.0Jul 2020View details →

zenodo44/100

HIKARI-2021: Generating Network Intrusion Detection Dataset Based on Real and Encrypted Synthetic Attack Traffic

Available datasets from the paper Generating Encrypted Network Traffic for Intrusion Detection Datasets. To produce the dataset follow the technical detail in <a href="https://github.com/andreysfc/generating-encrypted-network">github</a>

opencc-by-4.0May 2021View details →

zenodo44/100

On-road traffic emission over megacity Delhi

This dataset presents an estimate of hourly gridded on-road traffic exhaust emission of PME, BC, OM, CO, NOx, VOC, NH3, N2O and CH4, for the megacity Delhi (National Capital Territory of Delhi) for 2018 at a spatial resolution of 100m×100m. This dataset is presented as a netDCF covering the rectangular domain around National Capital Territory (NCT) of Delhi.

opencc-by-4.0May 2022View details →

zenodo44/100

Benchmark for deterministic traffic simulator - parameter space exploration (Prague, Jun 6 2021)

The benchmark is meant for deterministic traffic simulator for optimising traffic flow within a city. The simulator is one part of a traffic modeling framework for intelligent transportation in smart cities. In contrast to standard navigation systems where the navigation is optimised for drivers, we aim to optimise a distribution of the global traffic flow. We utilise HPC resources for the simulator’s parameters exploration for which EVEREST SDK is used. The traffic simulator is available at: <a href="https://github.com/It4innovations/ruth">github.com/It4innovations/ruth</a>. The benchmark contains input data, routing map, and skript to run it with HyperQueue. Simulator v1.0 was used.

opencc-by-4.0Aug 2022View details →

zenodo44/100

A consolidated database of police-reported motor vehicle traffic accidents in the United States for actuarial applications

This database is related to "A CONSOLIDATED DATABASE OF POLICE-REPORTED MOTOR VEHICLE TRAFFIC ACCIDENTS IN THE UNITED STATES FOR ACTUARIAL APPLICATIONS" (Araiza Iturria C.A., Hardy M., Marriott P.). Author Information     A. Author         Name: Carlos Andrés Araiza Iturria         Email: caraizai@uwaterloo.ca            B. Co-author         Name: Mary Hardy         Email: mary.hardy@uwaterloo.ca       C. Co-author         Name: Paul Marriott         Email: pmarriott@uwaterloo.ca          Institution: University of Waterloo         Address: 200 University Ave W, Waterloo, ON N2L 3G1 Funding granted by the Natural Sciences and Engineering Research Council of Canada. Hardy: RGPIN-2018-03754, Marriott: RGPIN-2020-04015. The Python scripts to create the database can be directly accessed through related identifiers in this page. Parameter estimates along with their 90% confidence intervals from the 20 multinomial logistic regressions can be seen through related identifiers in this page.

opencc-by-4.0Dec 2021View details →

zenodo44/100

Classification of Artificial Intelligence and eXplainable Artificial Intelligence publications in Air Traffic Management

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

opencc-by-4.0Jan 2022View details →

ScienceDex guides

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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.

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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.

allen-brain-atlas

neuroscienceopenDocumentation, web resources, and API references are available online.

Last verified 2026-04-30Open record

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.

abode-home-cage

behavioral-neuroscienceopenThe DataShare record exposes download links for annotations, documentation, license text, and the zipped per-snippet data directory.

Last verified 2026-04-30Open record

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.

dandi-nwb

electrophysiologyopenPublished Dandiset metadata and archive endpoints are available through the production DANDI API.

Last verified 2026-04-30Open record

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.

ibl

behavioral-neuroscienceopenPublic sessions can be searched and loaded from the IBL public data server through ONE.

Last verified 2026-04-29Open record

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.

openneuro

neuroscienceopenPublished datasets are available on demand over the internet.

Last verified 2026-04-29Open record