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Dataset results
22 results for “vehicular”
Federation of Vehicular Data in Smart Villages with Socioeconomic Information
<p>We present a dataset tailored for monitoring vehicle activity in a rural environment, specifically the Barranco de Poqueira region, covering the municipalities of Pampaneira, Bubión, and Capileira within the Sierra Nevada National Park, Granada, Spain. The dataset is generated by four Hikvision License Plate Recognition (LPR) cameras, capturing vehicle entries and exits in each village. To enrich the dataset, we include additional contextual details such as vacation calendars, vehicle origins, and socio-demographic information. Spanning from February 2022 to August 2023, the dataset is organized into three files: one with raw data directly from the cameras, another aggregated at the visit level with contextual information, and a third aggregated by vehicles with context details. With potential applications in mobility studies, urban planning, tourism, and socio-demographic analysis, the dataset is structured into three distinct files, encompassing a total of 43 different variables.</p> <p>The <strong>RAW_SMART_POQUEIRA.csv</strong> file contains information about 4 variables: num_plate_ID, camera_ID, date, and direction.</p> <p>The file <strong>VEHICLES_SMART_POQUEIRA.csv</strong> contains information about 33 variables: num_plate_ID, visit_time, distance, num_holiday, num_workday, num_high_season, num_low_season, entry_in_high_season, entry_in_holiday, nights, visits_dif_weeks, visits_dif_months, total_entries, avg_visit, std_visit, avg_nights, std_nights, avg_holiday, std_holiday, avg_workday, std_workday, avg_high_season, std_high_season, avg_low_season, std_low_season, route, country, km_to_dest, population, avg_gross_income, avg_disposable_income, autonomous_community, and province.</p> <p>The file <strong>VISITS_SMART_POQUEIRA.csv</strong> contains information about 26 variables: num_plate_ID, entry_cam, entry_date, entry_time, exit_cam, exit_date, exit_time, visit_time, route, distance, num_holiday, num_workday, num_high_season, num_low_season, nights, visits_dif_weeks, visits_dif_months, entry_in_holiday, entry_in_high_season, country, km_to_dest, population, avg_gross_income, avg_disposable_income, autonomous_community, and province.</p>
Microscopic vehicular mobility trace of Europarc roundabout, Creteil, France (vehicular-mobility-trace.github.io: v1.0)
<p>First release of the Europarc roundabout micro mobility dataset, Creteil, France.</p> <p>http://vehicular-mobility-trace.github.io/</p>
BRAVES database Version 1.1 (REVISED): multispecies and high spatiotemporal resolution database of vehicular emissions in Brazil.
<p>The BRAzilian Vehicular Emissions inventory Software (BRAVES) database is a multispecies and high spatiotemporal resolution database of vehicular emissions in Brazil. We provide this database using a spatial disaggregation based on road density, temporal disaggregation using vehicular flow profiles, and chemical speciation based on SPECIATE database from the United States Environmental Protection Agency. We provide netCDF files with spatial resolution of 0.05x0.05 and annual emissions from 2013 to 2019. Files are divided by vehicle type (light, commercial-light, motorcycles, and heavy). We also provide the total vehicular emissions (sum of emissions from all vehicle types). The database contains emissions of 41 chemical species, such as ACET, ACROLEIN, ALD2, BENZ, BUTADIENE13, CH4, CO, CO2, ETH, ETHA, ETHY, ETOH, FORM, ISO, N2O, NAPH, NO, NO2, PAL, PCA, PCL, PEC, PFE, PK, coarse mode primary PM (PMC), PMG, PMN, unspeciated PM2.5 (PMOTHR), PNA, PNH4, PNO3, POC, PRPA, PSI, PSO4, PTI, SO2, TERP, TOL, VOC, and XYLMN. Codes from BRAVES are available by registering at <a href="https://hoinaski.prof.ufsc.br/BRAVES/">https://hoinaski.prof.ufsc.br/BRAVES/</a> and <a href="https://github.com/leohoinaski/BRAVES">https://github.com/leohoinaski/BRAVES</a>, where users can access instructions to run the database and download the input files.</p> <p>In this updated version from the first BRAVES database version, we have preserved estimates of ETOH and RCHO from CETESB. Brazil has a unique chemical signature of the chemical composition due to the biofuels (27% of gasoline is ethanol and 7% of diesel is bio-diesel). The emissions of C2H4O (ALD2), CH2O (FORM), and C3H6O (ACET) have been derived from RCHO emissions. We have used US-EPA Speciate to speciate compounds only when local emission factors of RCHO and ETOH are not available, such as in the case of motorcycles and heavy vehicles.</p> <p>We have also included emissions of PM2.5 (PMFINE), speciating coarse PM emissions from brake and tires (40%), road wear (53%), road dust resuspension (17%), and exhaust emissions (100%). Pixel center coordinates (longitude, latitude), pixel area (AREA), and pixel local time zone (LTZ) shift from UTC has been added to the netCDF files.</p> <p>This database has been currently part of the preprint currently under review for the journal ESSD (https://doi.org/10.5194/essd-2022-74).</p> <p> </p> <p>Files description:</p> <p>BRAVESdatabaseAnnual_BR_(typeEmiss)_(Vehicle Type)_(resolution)_(year).nc - Annual emissions in Brazil by vehicle type and 0.05x0.05 degree of resolution.</p> <p>Domain:</p> <p>lati = -36 #(Brazil) #lati = int(round(bound.miny)) # Initial latitude</p> <p>latf = 8 #(Brazil) #latf = int(round(bound.maxy)) # Final latitude</p> <p>loni = -76 #(Brazil) #loni = int(round(bound.minx)) # Initial longitude</p> <p>lonf = -32 #(Brazil) #lonf = int(round(bound.maxx)) # Final longitude</p> <p>deltaX = 0.05 # Grid resolution/spacing in x direction</p> <p>deltaY = 0.05 # Grig resolution/spacing in y direction</p> <p> </p> <p>typeEmiss:</p> <p>'TOTAL' = Total emissions/sum of emissions types<br> 'Exhaust' = Only exhaust emissions<br> 'non-exaust' = Only non-exhaust emissions<br> 'non-exaustMP' = Only Particulate Matter non-exhaust emissions<br> 'non-exaustMP_no_resusp'= Only Particulate Matter non-exhaust emission excluding road resuspension </p>
Generation of synthetic, realistic vehicular traces for three access highways of Quito using SUMO
<p>These files present the maps of three access highways of Quito simulated in SUMO. The contributions are.</p> <ul> <li>Careful validation of the imported maps from OpenStreetMaps (imported in July 2019) including time intervals in traffic lights, location of traffic lights, suppression on non-existing junctions, edges, etc.</li> <li>Simulation of realistic number of vehicles for each road considerings the statistics from traffic authority of Quito</li> <li>Configuration of the 5 generation tools provided in the SUMO package. We used all the meaningful configuration for each tool to obtain synthetic realistic vehicular traces</li> </ul>
VEDSIB: VEhicular trajectory Dataset at Signalised1 Intersection in Bremen Dataset
<p> VEDSIB dataset contains 26,007 trajectories recorded over 32 hours in a signalised intersection in Bremen, Germany.</p> <p>VEDSIB contains:</p> <ul> <li>Date: the date of the collected information</li> <li>Time: Time when the information was collected</li> <li>Direction: at the intersection, which direction the vehicle came from</li> <li>Number: the number of vehicles par minutes</li> <li>Type: The type of vehicles; either a car, a bus or a tram.</li> <li>Temperature in °F: the temperature observed at the time of collecting the information</li> <li>Humidity: humidity level</li> <li>Wind information (speed, Gust): the wind information</li> <li>Pressure, Precipitation, Condition: the general condition of the weather</li> <li>Rainy information: if there is rain or not at the time of collecting the data.</li> </ul>
Synthetic, realistic vehicular traces for financial district of Quito using SUMO
<p>These files present the map of the financial district of Quito simulated in SUMO. The contributions are.</p> <ul> <li>Careful validation of the imported maps from OpenStreetMaps (imported in November 2019) including time intervals in traffic lights, location of traffic lights, suppression on non-existing junctions, edges, etc.</li> <li>Simulation of realistic number of vehicles considering the statistics from traffic authority of Quito</li> <li>Configuration of the generation tools provided in the SUMO package. We used all the meaningful configuration for each tool to obtain synthetic realistic vehicular trace</li> </ul> <p>In addition, we include the scripts and program to analyze vehicular traces from the point of view of vehicular communications. The scripts were developed using awk, bash and R. The software to compute connectivity matrix was developed with C++</p>
Vehicular traffic count measurements at Tampere City, Finland, Updated version
<p>Inductive loop vehicular traffic count measurements from Tampere City, Finland</p> <p>15-minute resolution, 2 locations, 2011-2014</p> <p>Data utilized in "Origin-destination matrix estimation with a conditionally binomial model" by P.Kuusela, I. Norros, J. Kilpi and T. Räty</p> <p>See VehicularTrafficCountMeasurements_TampereFinland_2011-2014.rtf</p>
WiPerf Vehicular Wi-Fi Performance Monitoring Dataset
<p>WiPerf is a Wi-Fi performance monitoring system that can measure quality-of-service (i.e., throughput) and collect channel state and GPS information from vehicles using Wi-Fi for Vehicle-to-Infrastructure (V2I) and Vehicle-to-Everything (V2X) communications.</p> <p>The dataset contains data collected on June 14<sup>th</sup>, 2022, from a real-world vehicular setup. For the setup we mounted two TP-Link Talon AD7200 routers on top of two vehicles, i.e., a 2006 Renault Megane Break and a 2002 Honda Jazz. One of the cars remained static while the other was driven around the stationary node for 10 laps at Rua D. Frei Vicente da Soledade e Castro in Porto,<br> Portugal. We collected 802.11n, 802.11ac, and 802.11ad data simultaneously using the three radio interfaces of the Talon AD7200 routers.</p> <p>The dataset also includes throughput and CPU load data collected using <em>iperf3 </em>and the <em>sar </em>application from the <em>sysstat </em>Linux utility package. These measurements were taken in a lab environment using the same Talon AD7200 routers. </p>
Intersection Vehicular Traffic Flow Scheduling Datasets
<p><strong>Background</strong></p> <p>Vehicular traffic congestion remains a major problem in most modern cities of the world. Therefore, efforts to minimize these congestions and their accompanying effects continuously receive much attention from researchers, traffic engineers, policymakers, etc.</p> <p>One important element required in the efforts of traffic engineering for the minimization of traffic congestion is data. Data influence the modeling and deployment of traffic scheduling systems. Most importantly, the objectives of modeling and deploying the traffic scheduling system influence the nature of the data to be used.</p> <p><strong>Aim</strong></p> <p>The aim of this study was to generate and provide a dataset that may be used in the training of computationally intelligent systems having basic objects of minimizing waiting time, travel time, etc. at various intersections (isolated intersections or roundabouts).</p> <p><strong>Methodology</strong></p> <p>The dataset (<strong><em>.csv</em></strong>) consisted of waiting time (W), queue length (Q), and phase duration (P). The waiting time is the time duration vehicles have waited at an intersection/roundabout before being scheduled to utilize the intersection/roundabout. The queue length refers to the number of vehicles (vehicular count) waiting at an intersection/roundabout. Phase duration is the time period a given vehicular flow (lane) is assigned the green wave to utilize the intersection/roundabout. The dataset was obtained through repeated training, testing, and modification of phase duration and the Adaptive Neuro-Fuzzy Inference System (ANFIS) model.</p> <p>The dataset considered bounded conditions on the three parameters (W, Q, P). The W and Q were bounded between zero and ninety – [0, 90] and the P is [13, 50]. That is, when W and Q are greater than or equal to the upper bound, the upper bound is used. Every flow may be assigned a minimum P of 13s and a maximum of 50s. The P-bounds assumed that the lower bound is large enough for vehicles on the assigned traffic flow to move to the safe region of the intersection before the scheduling system switches assignment to another traffic flow. </p> <p><strong>Conclusion</strong></p> <p>The dataset may be used as a benchmark dataset for the improvement of traffic flow controllers as well as other datasets.</p>
Vehicular scenario near a building
<p>Measurements were performed in an urban environment with low building density while traveling in a vehicle at an average speed of 30 km/h. This context allows to evaluate how the 5G network responds in suburban or industrial areas where buildings are scarce. Aspects such as coverage, data rate and connection stability are examined to ensure reliable connectivity while traveling in a vehicle in less dense urban environments.</p>
Conteo Vehicular - Multas
<p>Publicación final </p> <p>Integración de Datos 2022</p>
Privacy metrics suites for genomic privacy, vehicular communications privacy, and graph privacy
<p>This dataset contains optimized suites of privacy metrics for three application domains: genomic privacy, vehicular communications privacy, and graph privacy.</p> <p>The dataset also contains the data we have used as input for the evolutionary optimization algorithms.</p>
CN+: Vehicular Dataset at Traffic Light Regulated Intersection in Bremen, Germany
<p>Vehicular ad-Hoc Networks (VANETs) were created to reduce traffic accidents and enhance the safety of both passengers and other road users. In VANETs, vehicles communicate with nearby vehicles using V2V (vehicle-to-vehicle) communication links to improve awareness of their surroundings. Since their inception, considerable research has focused on developing cutting-edge algorithms to boost safety. However, testing these algorithms in real-world conditions is difficult due to high costs and various logistical challenges. Therefore, simulation-based testing is frequently used for VANET applications. Utilizing real datasets in simulations can significantly improve accuracy and yield realistic results. In this study, we introduce a dataset called 'CN+', which comprises data from over 25,000 vehicles collected over 32 hours at a signalized intersection in Bremen, Germany. The dataset is presented in two different formats: 1-CSV format and 2-SUMO converted format.</p>
Grey model analysis of vehicle population, road transport energy consumption, and vehicular emissions
<p>The files provide additional information to the paper “Grey model analysis of vehicle population, road transport energy consumption, and vehicular emissions". The supporting data file contains excel sheets of data used in the analysis, and the supporting information file provides some assumptions, background information and other results not included in the paper</p>
The impact of vehicular noise on the effectiveness of acoustic indices
Open the record for dataset details and reuse information.
Real-world vehicular source indicators for exhaust and non-exhaust contribution to PM2.5 during peak and off-peak hours using tunnel measurement
<p>The data are the species concentrations, traffic and meteorological information during the sampling periods in WJL tunnel, and the estimated function of E<sub>N</sub> curve under vehicle electrification.</p>
Impacts of on-road vehicular emissions on U.S. air quality: A comparison of two mobile emission models (MOVES and FIVE)
<p>Here presented CMAQ outputs used for paper of "MImpacts of on-road vehicular emissions on U.S. air quality: A comparison of two mobile emission models (MOVES and FIVE)"</p>
DRIIVE (Data and Research on Interventions to Improve the Vehicular Environment)
ClinicalTrials.gov study NCT02381626. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Extra-vehicular Activity (EVA) - US and Russia
Activities done by an astronaut or cosmonaut outside a spacecraft beyond the Earth's appreciable atmosphere.
Extra-vehicular Activity (EVA)-US and Russia API
Activities done by an astronaut or cosmonaut outside a spacecraft beyond the Earth's appreciable atmosphere.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.