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374 results for “traffic”
FiTraM: Field Traffic Model - First Version
<p>This is the first version of the python application FiTraM.</p> <p>FiTraM is the name for 'Field Traffic Model'. It models and reconstructs the tire tracks, wheel passes, wheel load and soil stress of agricultural machinery on the field based on route points either from GPS tracking, route planning or manual route construction.</p> <p>The model is described in Detail in:<br> Augustin, K., Kuhwald, M., Brunotte, J., Duttmann, R. (2019): FiTraM: A model for automated spatial analyses of wheel load, soil stress and wheel pass frequency at field scale. In: Biosystems Engineering 180, S. 108–120.</p> <p>This is part of the Ph.D. Thesis:<br> "Spatial Modeling of Field Traffic Intensity" from Katja Augustin, Kiel University</p>
Data of "High concentration of atmospheric sub-3 nm particles in polluted environment of eastern China: new particle formation and traffic emission"
<p>The attached data is the measurement data at SORPES station in Yangtze Rive Delta of China. The data is for analysis and figures in the study of "High concentration of atmospheric sub-3 nm particles in polluted environment of eastern China: new particle formation and traffic emission". Currently the manuscript is submitted to JGR-A.</p>
Required data for simulating a typical large-scale urban traffic network
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Consistent traffic noise impacts few fitness-related traits in a field cricket
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Growing up with chronic traffic noise exposure leads to transient but not long-term noise tolerance in a songbird
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A Comparison between Background Modelling Methods for Vehicle Segmentation in Highway Traffic Videos
<p>This dataset was used on the paper "A Comparison between Background Modelling Methods for Vehicle Segmentation in Highway Traffic Videos" for the comparison of three of the most common background modelling methods. The objective was to determine which of the models would be a better fit for the videos we had available at the time.</p> <p>Images are separated into folders, each corresponding to one of the videos used. To understand the naming convention, you can check <a href="https://arxiv.org/abs/1810.02835">the paper</a>, available at arXiv.</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>
Project O2 - A Cooperative Bypassing Algorithm for Connected and Autonomous Vehicles in Mixed Traffic
<p>The dataset includes Python code and VISSIM file for the research paper "A Cooperative Bypassing Algorithm for Connected and Autonomous Vehicles in Mixed Traffic".</p> <p> </p> <p> </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>
Kenyalang Traffic Garden Fountain, Kuching #OSS
Quick photogrammetry of a fountain in a local park using Mavic Mini and 3DF Zephyr. #opensourcesarawak #oss #borneo Source: Objaverse 1.0 / Sketchfab
HTTP(S) Traffic Data
<p>This dataset contains HTTP/HTTPS raw traffic data collected by crawling top 2,165 websites using VPN vantage points from 52 countries. The objective of crawling each website from different countries is to discover various geographic-specific hostnames used by the websites to serve content across geography. </p> <p>The capitalized alphabet in the filename, e.g., A in dataA.tar.gz, represent the country names starting with that alphabet from were the data was collected.</p> <p>This dataset was collected in January, 2013.</p>
ExaNeSt eXactLab Data Traffic produced by LAMMPS application
<p>LAMMPS is a classical molecular dynamics code that models an ensemble of particles in a liquid, solid, or gaseous state. It can model atomic, polymeric, biological, metallic, granular, and coarse-grained systems using a variety of force fields and boundary conditions This dataset is comprised of traces obtained from application LAMMPS by processing the otf2 output produced by the SCALASCA utility once the LAMMPS code has been instrumented. Specifically, five variants of LAMMPS have been analysed where the node count varies from 24 to 192.</p>
ΕxaNeSt INFN Data Traffic produced by proprietary DPSNN application
<p>DPSNN-STDP is a natively distributed mini-application benchmark representative of plastic spiking neural network simulators. Processes describe synapses in input to cluster of neurons with an irregular interconnection topology, with complex inter-process traffic patterns broadly varying in time and per process. This dataset is comprised of textual data set describing all the details of the inter-processor communication for five different neural network configurations. Each file contains a 3D numerical matrix describing, for each millisecond time-step of simulated neural activity (and for a total duration of 3 seconds of simulation) the size of the payload to be exchanged between all pairs of processes.</p>
ExaNeSt eXactLab Data Traffic produced by REGCM application
<p>RegCM4 is a regional climate model based on the concept of one-way nesting, in which large scale meteorological fields from a Global Climatic Model (GCM) run provide initial and time-dependent meteorological boundary conditions for high resolution simulations without any active feedback. This dataset is comprised of traces obtained from application REGCM by processing the otf2 output produced by the SCALASCA utility once the REGCM code has been instrumented. The otf2 data have been processed by a script provided by WP3 to extract valuable information written in ASCII. (See pdf included in the data set for more information).</p>
TRAFAIR Traffic Dashboard
<p>The two videos show the TRAFAIR traffic dashboard that was a web application developed within the TRAFAIR European project and that was active between the second half of 2020 and the beginning of 2023. Now the web application is no more available.</p>
Data files for Sheehan et al. 2023 'City Scale Traffic Monitoring Using WorldView Satellite Imagery and Deep Learning: A Case Study of Barcelona' DOI: https://doi.org/10.3390/rs15245709
<p>Data files for Sheehan et al. (2023) City Scale Traffic Monitoring Using WorldView Satellite Imagery and Deep Learning: A Case Study of Barcelona. Remote Sensing. 15(24) DOI: <a href="https://doi.org/10.3390/rs15245709">https://doi.org/10.3390/rs15245709</a></p> <p>Description of contents: </p> <p>xView-YOLOv3_Model6_Barcelona_weights.pt</p> <p>This file contains the pre-trained weights for the xView-YOLOv3 model (model code available here: https://github.com/ultralytics/xview-yolov3). These weights were trained on a manually created training data set of vehicles present in WorldView 2/3 imagery covering the city of Barcelona. The weights relate to Model 6 set up: a single vehicle class (parked, static and moving), RGB imagery, Barcelona training data set derived anchor boxes, 1500 x 1500 pixel sized images and to 1000 epochs. </p> <p> </p>
Data from: neglected puzzle pieces of urban green infrastructure: richness, cover, and composition of insect-pollinated plants in traffic-related green spaces
<p>Insect-pollinated vascular plants in spontaneous vegetation provide essential ecosystem services and benefit wildlife. However, floral communities associated with traffic-related green spaces are rarely considered valuable elements of urban green infrastructure (UGI). The dataset contains information on species-based floral communities of vascular insect-pollinated plants in traffic-related green spaces in three highly populated Finnish cities. Those are Helsinki (665 558 inhabitants), Tampere (244 029 inhabitants), and Turku (175 645 inhabitants). Data were collected during the mean flowering phenophase of vascular plants in July-August 2022 from two types of locations: (i) urban (city centers) and (ii) suburban (city outskirts), and from three types of traffic-related green spaces: (i) traffic islands, (ii) parking lots, (iii) road verges. The dataset contains information for the 93 vascular insect-pollinated plant species flowering during the survey. Sampling campaign was conducted in 90 sampling sites, and the dataset contains information on the location coordinates. In addition, the dataset possesses information on the amount of garbage pieces (cigarette filters, plastic boxes, or scraps) revealed for each sampling point in traffic-related green spaces.</p>
Cyber4OT: ICS network traces containing normal activity and full attack traffic
<p>The <em><strong>Cyber4OT</strong></em> dataset contains prepared in the test-bed environment packet traces from normal activity of OT network, as well as, full network attack. During recorded activity, the attacker performs full network reconnaissance, later disconnects legal Modbus TCP connection and performs PLC device hijacking.</p> <p>The dataset contains 96 files with more than 4,25 millions of packets.</p> <p><em><strong>ReadMe.txt</strong></em> file contains short description of each trace file content.</p> <p>Detailed description of the test bed, where data was prepared, is provided in the <em><strong>Cyber4OT_testbed_description.pdf</strong></em> file.</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>
PACs concentrations in traffic-related environments
<p>The data are the PACs concentrations during the sampling periods in three traffic-related receptor environments.</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)
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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.