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46 results for “road 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>
On-road traffic emission over megacity Delhi
<p>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. </p>
Queensland Road Traffic Crashes Fatalities and Hospitalisations 2011-2021
<p>Data sets that have been prepared from the Open Data Portal of the Queensland Government (2022) available from <a href="https://www.data.qld.gov.au/dataset/crash-data-from-queensland-roads">https://www.data.qld.gov.au/dataset/crash-data-from-queensland-roads</a>.</p> <p>Provides information from 2011 - 2021 on all police reported fatalities and hospitalisations that have occurred during this time period, showing road conditions and driver demographics.</p>
Road traffic prediction dataset.
<p><em>Public (anonymized) road traffic prediction datasets from Huawei Munich Research Center.</em></p> <p>Datasets from a variety of traffic sensors (i.e. induction loops) for traffic prediction. The data is useful for forecasting traffic patterns and adjusting stop-light control parameters, i.e. cycle length, offset and split times.</p> <p>The dataset contains recorded data from 6 crosses in the urban area for the last 56 days, in the form of flow timeseries, depicted the number of vehicles passing every 5 minutes for a whole day (i.e. 12 readings/h, 288 readings/day, 16128 readings / 56 days).</p>
Supplementary materials for article focused on traffic information enrichment: Animation and complete overview of all road segments on which basis summary test results were calculated
<p><strong>Supplementary materials </strong>(Appendix A and B) for the article:</p> <p>Traffic Information Enrichment: Creating Long-Term Traffic Speed Prediction Ensemble Model for Better Navigation through Waypoints</p> <p><em>Abstract: </em>Traffic speed prediction for a selected road segment from a short-term and long-term perspective is among the fundamental issues of intelligent transportation systems (ITS). During the course of the past two decades, many artefacts (e.g., models) have been designed dealing with traffic speed prediction. However, no satisfactory solution has been found for the issue of a long-term prediction for days and weeks using the vast spatial and temporal data. This article aims to introduce a long-term traffic speed prediction ensemble model using country-scale historic traffic data from 37,002 km of roads, which constitutes 66% of all roads in the Czech Republic. The designed model comprises three submodels and combines parametric and nonparametric approaches in order to acquire a good-quality prediction that can enrich available real-time traffic information. Furthermore, the model is set into a conceptual design which expects its usage for the improvement of navigation through waypoints (e.g., delivery service, goods distribution, police patrol) and the estimated arrival time. The model validation is carried out using the same network of roads, and the model predicts traffic speed in the period of 1 week. According to the performed validation of average speed prediction at a given hour, it can be stated that the designed model achieves good results, with mean absolute error of 4.67 km/h. The achieved results indicate that the designed solution can effectively predict the long-term speed information using large-scale spatial and temporal data, and that this solution is suitable for use in ITS.</p> <p>Simunek, M., & Smutny, Z. (2021). Traffic Information Enrichment: Creating Long-Term Traffic Speed Prediction Ensemble Model for Better Navigation through Waypoints. <em>Applied Sciences</em>, 11(1), 315. <a href="https://doi.org/10.3390/app11010315">https://doi.org/10.3390/app11010315</a></p> <p> </p> <p><strong>Appendix A</strong><br> Examples of the deviation between the average speed and the FreeFlowSpeed for selected hours.</p> <p> </p> <p><strong>Appendix B</strong><br> The text file provides a complete overview of all road segments on which basis summary test results were calculated in Section 6 of the article.</p>
NDVI, nocturnal road traffic noise and traffic flow over the canton of Geneva
<p>This dataset relates the spatial distribution of several environmental variables (including NDVI and nocturnal road traffic noise) over the territory of the canton of Geneva (Switzerland) with the aim of studying the potential attenuation effect of vegetation on road traffic noise. Traffic flow measurement points are in this regard also included.</p> <p>The dataset contains two distinct group of files. The first one (hec_grid_ge) contains the hectometric vector grid discretization of the canton of Geneva excluding Lake Geneva. Each cell is characterized by the mean, median and standard deviation of several environmental variables (NDVI, nocturnal road traffic noise, daily road traffic noise and land surface temperature). In addition, BiLISA local Moran's I, p-value and spatial association type for the spatial correlation of median NDVI and spatial lag of median nocturnal road traffic noise are defined for each cell. The second group of files (traffic_ge) contains the traffic flow measurements points which are characterized with "hec_grid_ge" layer variables of the hectometric cell to which they belong to.</p>
Exploring traffic safety problems and challenges of older roads' users in Louisiana: Causes and countermeasures
<p>It is well established that older pedestrians and drivers with 65 years and above are among the most vulnerable road users. As the number and proportion of older road users (as drivers and pedestrians) grows in many countries, as well as their share in pedestrians’ and drivers’ crashes and injuries, it behooves transportation researchers to further investigate the safety and mobility challenges of older road users. This study aims mainly to provide a comprehensive investigation of older pedestrians’ and drivers’ safety challenges. To this end, a three-fold research approach is designed to thoroughly examine older road users’ safety challenges as pedestrians and drivers. First, crash data analysis identified significant risk factors causing/leading older drivers’ to be involved in vehicle crashes. Second, a driving simulator experiment was performed to further investigate the identified risky conditions from the crash data analysis and literature review. Third, a self-reported survey was conducted across the country to address pedestrians’ safety challenges, needs, and attitudes toward different pedestrian crossing facilities (i.e., signalized intersections, unsignalized intersections, midblock cross walks with and without flashing lights, and roundabouts). The results of this study provide a better understanding regarding older drivers’ and pedestrian’ needs and challenges that should be accommodated to improve their safety and mobility.</p>
Magnitude and determinants of road traffic accidents in North Gondar Zone, Amhara Region, Ethiopia
<p>Number and types of a road traffic accidents in relation to road and road user, environmental and time related and vehicle related factors</p>
Relationship between Road Network Characteristics and Traffic Safety
<p>Corresponding data set for Tran-SET Project No. 17ITSTSA01. Abstract of the final report is stated below for reference:</p> <p>"The Transportation and Capital Improvement of the City of San Antonio, Texas Department of Transportation (TxDOT) and other related agencies often make several efforts based on traffic data to improve safety at intersections, but the number of intersection crashes is still on the high side. There is no one size fits all solution for intersections and the City is often usually confronted with doing best value option analysis on different solutions to choose the least expensive yet more advancements. The goal of this project was to obtain the relationship between road network characteristics and public safety with a focus on intersections; perform a thorough analysis of critical intersections with high crash incidents and crash rates within the city of San Antonio, Texas, and analyze key factors that lead to crashes and recommend effective safety countermeasures. Researchers conducted the following tasks: literature review, crash data analysis, factors affecting crashes at intersections, and the development of possible solutions to some of the identified challenges. Several variables and factors were analyzed, including driver characteristics, like age and gender, road-related factors and environmental factors such as weather conditions and time of day ArcGIS was used to analyze crash frequency at different intersections, and hotspot analysis was carried out to identify high-risk intersections. The crash rates were also calculated for some intersections. The research outcome shows that there are more male drivers than female drivers involved in crashes, even though we have more licensed female drivers than male drivers. The highest number of crashes involved drivers within the age range of 15 – 34 years; this is an indication that intersection crash is one of the top threats to the young generation. The study also shows that the most common crash type is the angle crash which represents over 23% of the intersection crashes. Driver’s inattention ranked first among all the contributing factors recorded. The highrisk intersections based on crash frequency and crash rate show that the intersection along the Bandera Road and Loop 1604 is the worst in the city, with 399 crashes and 8.5 crashes per million entering vehicles. The research concluded with some suggested countermeasures, which include public enlightenment and road safety audit as a proactive means of identifying high-risk intersections."</p>
A Modeling Framework for Near-Road Population Exposure to Traffic-Related PM2.5 and Environmental Equity Analysis: A Case Study in Atlanta, Georgia
<p>This is the dataset for the NCST project <em>"A Modeling Framework for Near-Road Population Exposure to Traffic-Related PM2.5 and Environmental Equity Analysis: A Case Study in Atlanta, Georgia"</em> by the Georgia Tech research team.</p> <p> </p> <p>Here is the abstract of the research: </p> <p>In this study, a modeling framework for population exposure to traffic-related PM2.5 with high spatiotemporal resolution is proposed and applied to the I-575/I-75 Northwest Corridor (NWC) in Atlanta, GA, for environmental equity analysis. The analyses retrieved trip data from the Atlanta Regional Commission’s (ARC) Activity-Based Model 2020 (ABM2020), after implementing path retention algorithms (Zhao, et al., 2019) to generate individual travel paths for more than 20 million predicted vehicle trips. Emission rates for each link were retrieved from MOVES-Matrix given the ABM link speed and facility type, the ARC’s county-level fleet composition data, and regional fuel properties and I&M program parameters. High-resolution downwind concentration profiles were predicted using EPA’s AERMOD microscale dispersion model with AERMET meteorology profiles for a huge array of receptors. Trip-end locations were derived from the ABM trip data, and the on-road trajectories for each person-trip (vehicle trace data) were derived from the travel paths through network. ABM synthetic household and person data were used in demographic assessment, and linked to representative household latitude and longitude locations in the Epsilon 2019 household demographic dataset. Individual exposure to traffic-related PM2.5 in time and space (average hourly concentration) was assessed by overlaying the second-by-second person location profiles (for 24 hours) against the hourly predicted PM2.5 concentration profiles. The analyses summarize the results across 16 demographic groups and the aggregate population exposure are compared to assess potential impact differences across demographics. High-income households in the corridor were exposed to less traffic-related air pollution as they tended to live further from the freeways. The analyses did not reveal large disproportionate negative impacts on low income groups along this specific corridor, but lager disproportionate negative impacts are expected elsewhere in the metro area due to the spatial clustering of income groups along other corridors. Overall, the research demonstrates the applicability of the modeling framework and describes how the various elements (e.g., link screening, dispersion modeling, path tracing, etc.) are optimized on the supercomputing cluster.</p>
Psychoacoustic indicators of pass-by road traffic noise
<p>This dataset contains files related to a measurement campaign of pass-by road traffic noise using the Statistical Pass-By (SPB) method according to ISO 11819-1 (2023) on road surfaces in hot mix asphalt. </p> <p>The compressed folder "Audio files, timestamps and calibration file" is divided into subfolders per measurement day, named according to the measurement date and location. Each subfolder contains continuous recordings as .wav audio files and Excel files with timestamps at 2 seconds before (t0) and 2 s after (tf) the moment a vehicle passage produced the peak noise level in that audio file, besides the air temperature (Tair) and vehicle category (Category). The pass-bys are classified into four categories (Passenger cars, Vans, Dual-axle heavy vehicle - HD and Multiple-axle heavy vehicle - HM).</p> <p>The file "Calibration file 1Hz 113.7 dB.wav" is a pure-tone 113.7 dB calibration file recorded from the sound level meter and microphone used in the measurements.</p> <p>The Excel file "Psychoacoustic indicators of pass-by road traffic noise.xlsx" presents nine acoustic and psychoacoustic indicators calculated from the 4s audio excerpts of 2199 pass-by vehicle noise obtained from this measurement campaign. Calculations were conducted using the algorithms implemented on PsySound3. The columns in this Excel file mean the following:</p> <ol> <li>L<sub>A,max</sub>: average maximum A-weighted sound level, in dB</li> <li>ΔL: the difference between the peak L<sub>A,max </sub>and the L<sub>A,max</sub> at 2 s before the peak was reached, in dB</li> <li>L<sub>A,eq</sub>: A-weighted equivalent continuous sound level, in dB</li> <li>L10: 10% percentile statistical noise level, in dB</li> <li>L90: 90% percentile statistical noise level, in dB</li> <li>N50: 50% percentile loudness, in sone</li> <li>S50: 50% percentile sharpness, in acum</li> <li>R50: 50% percentile roughness, in asper</li> <li>FS50: 50% percentile fluctuation strength, in vacil</li> </ol>
A Feasibility Trial of Eye Movement Desensitization and Reprocessing Therapy- Integrative Treatment Group Protocol for Ongoing Traumatic Stress In Road Traffic Accident Survivors for Reduction of Post
ClinicalTrials.gov study NCT07027930. IPD Sharing: NO. Countries: 1. Publications: 1.
Data for: Effect of low-traffic roads on abundance of ground-nesting birds in sub-Arctic habitats
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Data from: The journey from traffic offender to severe road trauma victim: destiny or preventive opportunity?
Background: Road trauma is a leading cause of death and injury in young people. Traffic offences are common, but their importance as a risk indicator for subsequent road trauma is unknown. This cohort study assessed whether severe road trauma could be predicted by a history of prior traffic offences. Methodology and Principal Findings: Clinical data of all adult road trauma patients admitted to the Western Australia (WA) State Trauma Centre between 1998 and 2013 were linked to traffic offences records at the WA Department of Transport. The primary outcomes were alcohol exposure prior to road trauma, severe trauma (defined by Injury Severity Score >15), and intensive care admission (ICU) or death, analyzed by logistic regression. Traffic offences directly leading to the road trauma admissions were excluded. Of the 10,330 patients included (median age 34 years-old, 78% male), 1955 (18.9%) had alcohol-exposure before road trauma, 2415 (23.4%) had severe trauma, 1360 (13.2%) required ICU admission, and 267 (2.6%) died. Prior traffic offences were recorded in 6269 (60.7%) patients. The number of prior traffic offences was significantly associated with alcohol-related road trauma (odds ratio [OR] per offence 1.03, 95% confidence interval [CI] 1.02–1.05), severe trauma (OR 1.13, 95%CI 1.14–1.15), and ICU admission or death (OR 1.10, 95%CI 1.08–1.11). Drink-drinking, seat-belt, and use of handheld electronic device offences were specific offences strongly associated with road trauma leading to ICU admission or death—all in a 'dose-related' fashion. For those who recovered from road trauma after an ICU admission, there was a significant reduction in subsequent traffic offences (mean difference 1.8, 95%CI 1.5 to 2.0) and demerit points (mean difference 7.0, 95%CI 6.5 to 7.6) compared to before the trauma event. Significance: Previous traffic offences were a significant risk factor for alcohol-related road trauma and severe road trauma leading to ICU admission or death.
Data from: How traffic facilitates population expansion of invasive species along roads: the case of common ragweed in Germany
1. Because common ragweed (Ambrosia artemisiifolia L., henceforth Ambrosia) has negative effects on human health, it is a common focus for management, which would benefit from a better understanding of the underlying mechanisms by which the species spreads. Road systems are known to be invasion corridors, but the conduit function of vehicles for the rapid spread of Ambrosia along roads and for population extension along roadside verges has not yet been demonstrated convincingly. 2. To quantify the effect of different traffic volumes on the dispersal and population extension of Ambrosia we used two approaches: First, by combining field experiments along roads with records of the seed rain around single plants, we simulated a combined dispersal kernel that revealed the interactions between primary dispersal and traffic-mediated secondary dispersal. Second, we recorded seedling recruitment around isolated roadside populations over two years to determine how traffic-related parameters affect population extension. 3. The longest traffic-mediated dispersal distances exceeded those of primary dispersal by about one order of magnitude. Traffic volume had a significant positive effect on dispersal distances and on the lateral deposition of seeds on the road verge. 4. Seedling recruitment around isolated roadside populations was significantly higher in the driving direction than against, but only at the distance where the major seed rain of traffic-mediated dispersal is to be expected according to the combined dispersal kernel (3-15 m). 5. Synthesis and applications: This study isolates effects of road traffic from confounding mechanisms (e.g. mowing machinery, propagule pressure from infested fields) on common ragweed (Ambrosia artemsiifolia L.) invasions. Results demonstrate traffic-mediated dispersal in Ambrosia invasions as a routine and predictable process that facilitates population extension in the direction of traffic along roadsides, depending on traffic volume. This highlights the importance of prioritizing mowing along high use roads and mowing of isolated populations to prevent seed abscission and further spread of common ragweed Ambrosia.
Artifacts for the paper "Automated and Complete Generation of Traffic Scenarios at Road Junctions Using a Multi-level Danger Definition"
<div> <div> <div>This deposit contains measurement data and additional artifacts pertaining to the "<em>Automated and Complete Generation of Traffic Scenarios at Road Junctions Using a Multi-level Danger Definition</em>" paper. The deposit is structured as follows:</div> <br> <div>Data pertaining to <strong>RQ1</strong> is found in the <em>baseline-comparison/</em> directory, which contains (1) statistics measured during scenario generation and (2) data analysis results for both our proposed approach (which we name <em>complete</em>) and the baseline <em>Scenic</em> approach, including statistical significance data.</div> <br> <div>Data pertaining to <strong>RQ2-4</strong> and to the following <strong>Discussion</strong> is found in the following three directories.</div> </div> </div> <div> </div> <ul> <li><em>0-generated-scenarios/ </em>contains (1) the generated abstract scenario specifications (i.e. maneuver instance and path region assignments) and (2) concrete scenarios represented in an `xml` format that are executable through the <a href="https://github.com/carla-simulator/scenario_runner">CARLA Scenario Runner</a> framework.</li> <li><em>1-simulation-results/ </em>contains the simulation results for our measurement runs. We include simulation traces, which show the exact position of each actor at each frame, in a human-readable, textual format. We also include result analysis (i.e. pertaining to simulation runtime, outcome, preventive maneuvers, etc.) within `measurements.json` files.</li> <li><em>2-generated-figures/ </em>contains figures derived from the contents of <em>1-simulation-results/</em> (including additional figures not included in the publication).</li> </ul> <div>All the code of the proposed scenario generation approach is implemented as extensions to the <a href="https://github.com/ArenBabikian/concretize">Concretize</a> framework (for scenario generation and analysis), and to the <a href="https://github.com/ArenBabikian/transfuser/tree/complete-gen">Transfuser</a> repository (for simulation). <a href="https://github.com/ArenBabikian/concretize">Concretize</a> is available under the <a href="https://www.eclipse.org/legal/epl-2.0/">Eclipse Public License - v 2.0</a>, while <a href="https://github.com/autonomousvision/transfuser">Transfuser</a> is available under the <a href="https://opensource.org/license/mit">MIT License</a>.</div>
Translucent Event Logs based on the Road Traffic Fine Management Event Log and the Inductive Miner - infrequent
<p>The translucent events logs in this file are based on the Road Traffic Fine Management Event Log (https://data.4tu.nl/articles/dataset/Road_Traffic_Fine_Management_Process/12683249) published by Massimiliano de Leoni and Felix Mannhardt. The folders' name specify the threshold setting of the Inductive Miner - infrequent (0.4, 0.6, and 0.8). Details on the generation can be found in https://doi.org/10.1007/978-3-031-70396-6_9.</p>
The Bangladesh Road Traffic Sign Dataset in Real-World Images for Traffic Sign Recognition
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Estimation of the road traffic sound levels based on Non-Negative Matrix Factorization dataset
<p>Sound database to compute the NonnegMatrixFact experience (download the .m files here: https://github.com/jean-remyGloaguen/article2017EstimationAmbiance) dedicated to the estimation of the traffic sound level on urban sound mixtures.</p> <p>This sound database includes two subfolders: <em>ambiance</em> and <em>dictionary</em></p> <p><em>- ambiance </em>folder includes 6 sub-corpus (<em>alert</em>, <em>animals, climate, human, mechanics</em> and <em>transportation</em>). In each sub-corpus, 50 sound mixtures are available and divided into two files: one dedicated to the traffic signal, the other to the interfering signal.</p> <p><em>- dictionary </em>folder includes the isolated sounds dedicated to the dictionary learning for NMF</p>
Weekly road traffic collision (AXA Mexico) & weekly road traffic deaths
<p><span>Dataset 1 (AXA collisions 2015–2019) was curated and used to evaluate the effect of two road traffic regulations implemented in Mexico City in 2015 and 2019 on collisions using an interrupted time series analysis. </span>Collisions data came from insurance collision claims (January 2015 to December 2019). The dataset contains 8 variables: year (anio_n), week (semana), count of total collisions per week (c_total), count of collisions resulting in injury per week (c_p_lesion), binary variable to identify the 2015 intervention (limit), binary variable to identify the 2019 intervention (limit1), the number of weeks from baseline (time), an estimate of the number of insured vehicles per week (veh_a_cdmx).</p> <p><span>Dataset 2 (Road traffic deaths 2013–2019) was curated and used to evaluate the effect of two road traffic regulations implemented in Mexico City in 2015 and 2019 on mortality using an interrupted time series analysis. </span>Mortality data came from vital registries collated by the Mexican Institute for Geography and Statistics, INEGI, (January 2013 to December 2019). The dataset contains 7 variables: year (anio_ocur), week (semana), count of traffic-related deaths per week (def_trans), binary variable to identify the 2015 intervention (limit), binary variable to identify the 2019 intervention (limit1), the number of weeks from baseline (time) and an estimate of the Mexico City population per week (pob_tot_p).</p>
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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.