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374 results for “traffic”
Supplementary files for Collection of Datasets with DNS over HTTPS Traffic
<p>The DNS over HTTPS (DoH) is becoming a default option for domain resolution in modern privacy-aware software. Therefore, research has already focused on various aspects; however, a comprehensive dataset from an actual production network is still missing. In this paper, we present a novel dataset, which comprises multiple PCAP files of DoH traffic. The captured traffic is generated towards various DoH providers to cover differences of various DoH server implementations and configurations. In addition to generated traffic, we also provide real network traffic captured on high-speed backbone lines of a large Internet Service Provider with around half a million users. Network identifiers (excluding network identifiers of DoH resolvers) in the real network traffic (e.g., IP addresses and transmitted content) were anonymized, but still, the important characteristics of the traffic can still be obtained from the data that can be used, e.g., for network traffic classification research. The real network traffic dataset contains DoH and also non-DoH HTTPS traffic as observed at the collection points in the network.</p> <p> </p> <p>This repository provides supplementary files for the "Collection of Datasets with DNS over HTTPS Traffic" :</p> <pre><code>─── supplementary_files | - Directory with supplementary files (scripts, DoH resolver list) used for dataset creation ├── chrome | - Generation scripts for Chrome browser and visited websites during generation ├── doh_resolvers | - The list of DoH resolvers used for filter creation during ISP backbone capture ├── firefox | - Generation scripts for Firefox browser and visited websites during generation └── pcap-anonymizer | - Anonymization script of real backbone captures</code></pre> <p>Collection of datasets:</p> <ul> <li><strong>DoH-Gen-F-AABBC</strong> --- <a href="https://doi.org/10.5281/zenodo.5957277">https://doi.org/10.5281/zenodo.5957277</a></li> <li><strong>DoH-Gen-F-FGHOQS</strong> --- <a href="https://doi.org/10.5281/zenodo.5957121">https://doi.org/10.5281/zenodo.5957121</a></li> <li><strong>DoH-Gen-F-CCDDD</strong> --- <a href="https://doi.org/10.5281/zenodo.5957420">https://doi.org/10.5281/zenodo.5957420</a></li> <li><strong>DoH-Gen-C-AABBCC </strong>--- <a href="https://doi.org/10.5281/zenodo.5957465">https://doi.org/10.5281/zenodo.5957465</a></li> <li><strong>DoH-Gen-C-DDD</strong> --<a href="https://doi.org/10.5281/zenodo.5957676"> https://doi.org/10.5281/zenodo.5957676</a></li> <li><strong>DoH-Gen-C-CFGHOQS</strong> --- <a href="https://doi.org/10.5281/zenodo.5957659">https://doi.org/10.5281/zenodo.5957659</a></li> <li><strong>DoH-Real-world</strong> --- <a href="https://doi.org/10.5281/zenodo.5956043">https://doi.org/10.5281/zenodo.5956043</a></li> </ul> <p> </p> <p> </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>
Madrid datasets (air quality, meteorological and traffic data)
<p>The datasets consist of air quality, meteorological, and traffic data from January to June 2019 and from January to June 2020. The following are the features of the datasets: Nitrogen dioxide, Wind speed, Wind direction (u component, v component, north, northeast, east, southeast, south, southwest, west, northwest), Pressure, Temperature, Humidity, Solar irradiance, Intensity, Occupancy time, Load, Average traffic speed.</p>
Digital Twin Technologies Towards Understanding the Interactions between Transportation and other Civil Infrastructure Systems: Traffic Sign and Day 1 Video
<p>This dataset contains three files. The first is raw video files collected from a GoPro camera that was dash mounted and driven around the UTEP campus. The telemetry from these files was extracted using the process outlined here (https://lucaselbert.medium.com/extracting-gopro-gps-and-other-telemetry-data-fadf97ed1834). The videos were manual evaluated to record the time in the video where a sign appeared, and the time stamp was noted. The Python file compared the timestamps from the manual file and the GoPro telemetry to create a combined data set for each route driven that includes the type of sign and the location. This data is in the Microsoft Excel file.</p> <p> </p> <p>Note that this data set is split into two because of the size of the videos. This is the video data from day 1 of 2 of data collection.</p>
Digital Twin Technologies Towards Understanding the Interactions between Transportation and other Civil Infrastructure Systems: Traffic Sign and Day 2 Video
<p>This dataset contains three files. The first is raw video files collected from a GoPro camera that was dash mounted and driven around the UTEP campus. The telemetry from these files was extracted using the process outlined here (https://lucaselbert.medium.com/extracting-gopro-gps-and-other-telemetry-data-fadf97ed1834). The videos were manual evaluated to record the time in the video where a sign appeared, and the time stamp was noted. The Python file compared the timestamps from the manual file and the GoPro telemetry to create a combined data set for each route driven that includes the type of sign and the location. This data is in the Microsoft Excel file.</p> <p> </p> <p>Note that this data set is split into two because of the size of the videos. This is the video data from day 2 of 2 of data collection.</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>
The dataset for DATA 2022 paper "Dataset: An Indoor Smart Traffic Dataset and Data Collection System"
<p>The dataset for DATA 2022 paper "Dataset: An Indoor Smart Traffic Dataset and Data Collection System"</p> <p>This archive contains a traffic light dataset that can be used for traffic light detection/classification. The dataset is collected from an indoor smart traffic testbed. In this testbed, we use fences to simulate the road's boundaries and use movable toy traffic signs and traffic lights to simulate those in real-world traffic scenes. An F1TENTH vehicle drives along the fence autonomously. Two cameras are mounted on both sides of the vehicle, which capture images of traffic lights and traffic signs on both sides of the track.</p> <p>This dataset contains 3507 images captured by the F1TENTH vehicle. Each image comes with ground truth bounding boxes that enclose the traffic lights and a label indicating the current state of the traffic light, 0 for a green light and 1 for a red light.</p> <p>Please cite our paper if you are using this dataset.</p>
Dataset: 2023 Aircraft traffic and GPS anomalies aggregated per hexbins
<p>We divided the globe into hexbins, each with an average area of 385 square kilometers. Once the hexbin grid was established, data from the GPS gaps, GPS deviations, and Traffic Density datasets were used to populate these hexbins with relevant information. On average, each hexbin has around 23,478 flights passing through it.</p> <ul> <li><strong>Total Records</strong>: 14,117 - total number of hexbin on a map, where number of flights > 0</li> <li><strong>Columns</strong>:</li> <ul> <li><strong>id:</strong></li> <li><strong>WKT</strong>: Well-Known Text representation of a POINT (senter of each hexbin) in the CSV file, or a geometry field in the DPKG file.</li> <li><strong>flights</strong>: number of flights traveled trough that hexbin in 2023</li> <li><strong>gaps</strong>: number of GPS gap incidents registered in that hexbin in 2023</li> <li><strong>deviations</strong>: number of GPS deviation incidents started in that hexbin in 2023</li> </ul> </ul>
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>
High-resolution harvester data for estimating rolling resistance and forest trafficability
<p>This study describes an extensive, high-resolution data on rolling resistance coefficient collected in a boreal forest landscape in Southern Finland during the non-frost period of 2021, covering roughly 50~km of harvester routes and used in the following study:</p> <p>Salmivaara, A., Holmström, E., Kulju, S., Ala-Ilomäki, J., Virjonen, P., Nevalainen, P., Heikkonen, J., and Launiainen, S. 2024.High-resolution harvester data for estimating rolling resistance and forest trafficability. European Journal of Forest Reseach. doi: 10.1007/s10342-024-01717-6</p> <p>The coordinates are in metric system, and the relative position between sites are maintained. However, they have been converted and do not refer to actual locations.</p> <p>Two datasets collected by a Ponsse Ergo harvester are available: </p> <p>1) All non-frost period of 2021, with 8m*8m grid means of rolling resistance ('grid mean rr'), along with grid mean longitudinal inclination of the machine ('grid mean pitch'), time, topographic wetness index ('twi') and binary indication whether a road is within 4m radius from the grid centerpoint ('roads'). </p> <p>2) Data including all 11 test sites with all first pass harvester machine data ('time', 'E_conv', 'N_conv', 'dtorq', 'drpm', 'peng', 'cooling', 'phydr', 'pwheels', 'v_ms', 'fmotive', 'slopeforce', 'normalforce', 'pitch', 'rr') and grid means of spatial data ('grid', 'grid mean rr', 'grid mean pitch', 'roads', 'site', 'slope_rad_terrain', 'bmroot_all'. 'c_bmroot_ratio', 'broadlv_rootbm_ratio', 'p_bmroot_ratio','s_bmroot_ratio', 'age', 'diameter', 'standheight', 'b_vol', 'b_pulp_vol', 'b_timber_vol', 's_vol', 's_pulp_vol', 's_timber_vol', 'p_vol', 'p_pulp_vol', 'p_timber_vol', 'broadlv_vol','broadlv_pulp_vol', 'broadlv_timber_vol', 'canopy_cov', 'broadlv_canopy_cov', 'ba', 'volume', 'dem', 'devMeanElev16', 'devMeanElev8', 'diffMeanElev8', 'erosion risk', 'tri', 'roughness_mag', 'roughness_scale', 'dtw00_5ha', 'dtw_01_0ha', 'dtw_04_0ha', 'dtw_10_0ha', 'twi', 'sat_deficit_doy', vol_moisture_doy, 'bare_rock_areas', 'lakes', 'streams', 'peat', 'topsoil', 'aspect', 'aspect_sin', 'aspect_cos', 'main_class_fertility_class_comb')</p> <p> </p>
Supplementary materials. Live traffic. Exploring Tehran with excitable medium
<p>These are videos of traffic in Tehran, used to analyse patterns of excitation on street network of Tehran. These are supplementary materials to the paper </p> <p>Exploring Tehran with excitable medium </p> <p>An excitable chemical medium --- Belousov-Zhabotinsky (BZ) reaction --- is proven to be a fruitful substrate for prototyping unconventional computing devices ~\cite{adamatzky2017east}. These include image processors, logical circuits, and robot controllers. We study a BZ potential for characterising a geometry of street networks on a fragment of Tehran street map. The city was chosen because it is one of the most populated cities in the World with nearly uncontrollable urban growth. In numerical experiments with Oregonator model of BZ reaction, we demonstrate that excitability of the medium allows acts as a selector between omnidirectional waves and soliton-like localised excitations. We uncover a phase-transition like dynamics, controlled by the excitability, of coverage of the street network by excitation wave-fronts. In the cluster analysis, we show how the network geometry, when it meets propagation of BZ wave-front, relates to the traffic flow of Tehran</p>
Population estimation from mobile network traffic metadata
<p><em><strong>Please cite our paper if you publish material based on those datasets</strong></em></p> <blockquote> <p>G. Khodabandelou, V. Gauthier, M. El-Yacoubi, M. Fiore, "Estimation of Static and Dynamic Urban Populations with Mobile Network Metadata", in IEEE Trans. on Mobile Computing, 2018 (in Press). <a href="http://dx.doi.org/10.1109/TMC.2018.2871156">10.1109/TMC.2018.2871156</a></p> </blockquote> <p> </p> <p><strong>Abstract</strong></p> <p>Communication-enabled devices that are physically carried by individuals are today pervasive,<br> which opens unprecedented opportunities for collecting digital metadata about the mobility of large populations. In this paper, we propose a novel methodology for the estimation of people density at metropolitan scales, using subscriber presence metadata collected by a mobile operator. We show that our approach suits the estimation of static population densities, i.e., of the distribution of dwelling units per urban area contained in traditional censuses. Specifically, it achieves higher accuracy than that granted by previous equivalent solutions. In addition, our approach enables the estimation of dynamic population densities, i.e., the time-varying distributions of people in a conurbation. Our results build on significant real-world mobile network metadata and relevant ground-truth information in multiple urban scenarios.</p> <p><strong>Dataset Columns</strong></p> <p>This dataset cover one month of data taken during the month of April 2015 for three Italian cities: Rome, Milan, Turin. The raw data has been provided during the Telecom Italia Big Data Challenge (http://www.telecomitalia.com/tit/en/innovazione/archivio/big-data-challenge-2015.html)</p> <p>1. <strong>grid_id</strong>: the coordinate of the grid can be retrieved with the shapefile of a given city<br> 2. <strong>date</strong>: format Y-M-D H:M:S<br> 4. <strong>landuse_label</strong>: the land use label has been computed by through method described in [2]<br> 5. <strong>population</strong>: Census population of a given grid block as defined by the Istituto nazionale di statistica (ISTAT https://www.istat.it/en/censuses) in 2011<br> 6. <strong>estimation</strong>: Dynamics density population estimation (in person) as the result of the method described in [1]<br> 7. <strong>area</strong>: surface of the "grid id" considered in km^2<br> 8. <strong>geometry</strong>: the shape of the area considered with the EPSG:3003 coordinate system (only with quilt)</p> <p><strong>Note</strong></p> <p>Due to legal constraints, we cannot share directly the original data from the Telecom Italia Big Data Challenge we used to build this dataset.</p> <p><strong>Easy access to this dataset with quilt</strong></p> <p>Install the dataset repository:</p> <p>$ quilt install vgauthier/DynamicPopEstimate</p> <p>Use the dataset with a Panda Dataframe</p> <p>>>> from quilt.data.vgauthier import DynamicPopEstimate<br> >>> import pandas as pd<br> >>> df = pd.DataFrame(DynamicPopEstimate.rome())<br> <br> Use the dataset with a GeoPanda Dataframe<br> <br> >>> from quilt.data.vgauthier import DynamicPopEstimate<br> >>> import geopandas as gpd<br> >>> df = gpd.DataFrame(DynamicPopEstimate.rome())</p> <p><strong>References</strong></p> <p>[1] G. Khodabandelou, V. Gauthier, M. El-Yacoubi, M. Fiore, "Population estimation from mobile network traffic metadata", in proc of the 17th International Symposium on A World of Wireless, Mobile and Multimedia Networks (WoWMoM), pp. 1 - 9, 2016. </p> <p>[2] A. Furno, M. Fiore, R. Stanica, C. Ziemlicki, and Z. Smoreda, "A tale of ten cities: Characterizing signatures of mobile traffic in urban areas," IEEE Transactions on Mobile Computing, Volume: 16, Issue: 10, 2017.<br> </p>
Traffic flow formation in termites
<p>The material shared here is part of the research project on the mechanism of traffic flow formation. We use termites as a biological model and their natural foraging activity as a method. Here, we present some video fragments extracted from the recordings of the full foraging process of the termites <em>Constrictotermes </em><em>cyphergaster</em> at laboratory conditions. This research project is supported by the Brazilian Government through the Minas Gerais State Agency for Research (FAPEMIG), the Brazilian Council for Research (CNPq) and the Coordination for the Improvement of Higher Education Personnel (CAPES).</p> <p>Details of the videos: Each file recording is named with the ID of the nest used for each test. A test consisted of the passage of the termites from the box containing the entire nest to the foraging box through a bridge. The width and the form of the bridge were modified between tests. In the fragment, 2018-IX-07-RCB-N09 we tested a bridge with a constant width of 7.5 cm, while in the fragment 2018-IX-07-RCB-N18 we tested a "bottleneck" bridge with a hybrid width of 2.5 cm - 1.5 cm - 2.5cm.</p> <p>For aditional information, please contact us: Julieth Castiblanco (castiblancoq.j@gmail.com), Og DeSouza (og.souza@ufv.br).</p>
Automated Truck Lanes in Urban Area for Through and Cross Border Traffic
<p>Autonomous trucks will soon be operational across our nation’s roadways. Yet, it is unclear how the design of our highway infrastructure should be modified to accommodate autonomous trucks, to enable them to operate in such a way to maximize the economic, capacity and safety benefits. The University of Texas at El Paso (UTEP) is proposing to develop and demonstrate, through microscopic traffic simulations, the concept of operations of autonomous truck lanes along the interstate freeways. Using the I-10 Freeway in the El Paso, TX region as the testbed, the research team will: (i) assess the existing structural, geometric and traffic designs in handling fully automated trucks, including the entrances, exits, and connectors; (ii) perform microscopic traffic simulations at critical locations to demonstrate design issues and the recommended design improvements; (iii) conduct a preliminary cost estimation on such infrastructure improvements.</p>
Mitigating Traffic Congestion on I-10 in Baton Rouge, LA: Supply- and Demand-Oriented Strategies & Treatments
<p>Corresponding data set for Tran-SET Project No. 17ITSLSU09. Abstract of the final report is stated below for reference:</p> <p>"The aim of this study is to develop a better understanding of the causes of traffic congestion on I-10 in the Baton Rouge, LA area, particularly at the I-10 Mississippi River Bridge, and to identify treatments and strategies to mitigate congestion at the bridge site. This study developed and calibrated a microsimulation model of I-10 (from Lobdell Highway in Port Allen to Highland Road, I-110 to Florida Street, and I-12 to Walker Road) and investigated several supply- and demand-oriented strategies. This includes: rehabilitation and utilization of the old Mississippi River Bridge on US-190 and the existing US-190/US-61 corridor, overall demand management of I-10 EB traffic, reduction in percent trucks traveling eastbound on I-10 during the A.M. peak period, and ramp metering at the on-ramp west of the I-10 Mississippi River Bridge. The majority of the tested strategies appear to be feasible and effective solutions; however, a combination of supply- and demand-oriented treatments must be implemented to fully relieve congestion on I-10 in Baton Rouge."</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>
Dataset Using TLS Fingerprints for OS Identification in Encrypted Traffic
<p>The dataset consists of data from three different sources; flow records collected from the university backbone network, log entries from the two university DHCP (Dynamic Host Configuration Protocol) servers and a single RADIUS (Remote Authentication Dial In User Service) accounting server. The data was collected from 2019-07-12 00:00 to 2019-07-16 23:59 with a few hours overhead on both sides of the interval for the log entries to cover long connection sessions overlapping to and from the time frame.</p> <p>We measured the flow data from the university uplink to the Internet. In the dataset, we kept only flows with source IP addresses from university wireless networks (Eduroam). The flow data was then enriched with information from DHCP and RADIUS servers to contain ID of the RADIUS session and operating system od the transmitting device as derived from DHCP logs.</p> <p>The dataset is in the form of CSV file with the following information fields important for OS identification:</p> <ul> <li>Basic flow features <ul> <li>Date flow start - timestamp of flow start</li> <li>Date flow end - timestamp of flow end</li> <li>Src IPv4 - source IPv4 address</li> <li>sPort - source L4 port</li> <li>Dst IPv4 - destination IPv4 address</li> <li>dPort - destination L4 port</li> </ul> </li> <li>Extended TCP/IP parameters <ul> <li>SYN size - the size of the initial SYN packet of a TCP connection (in bytes)</li> <li>TCP win - value of TCP Window size parameter</li> <li>TCP SYN TTL - observed TTL value</li> </ul> </li> <li>HTTP parameters <ul> <li>HTTP Host - hostname from the HTTP request</li> <li>HTTP UA OS - OS identification based on user-agent</li> <li>HTTP UA OS MAJ - OS identification based on user-agent</li> <li>HTTP UA OS MIN - OS identification based on user-agent</li> <li>HTTP UA OS BLD - OS identification based on user-agent</li> </ul> </li> <li>TLS parameters <ul> <li>TLS SNI - Server Name Indication field</li> <li>TLS SNI length - length of SNI in bytes</li> <li>TLS Client Version - TLS client hello Version field</li> <li>Client Cipher Suites - list of supported cipher suites</li> <li>TLS Extension Types - list of extension IDs</li> <li>TLS Extension Lengths - list of extension lengths</li> <li>TLS Elliptic Curves - list of supported curves (or supported groups in TLS1.3)</li> <li>TLS EC Point Formats - list of EC formats</li> </ul> </li> <li>Log based extensions <ul> <li>Session ID - ID of the session to match flows from one device</li> <li>Ground Truth OS - OS name derived from log data</li> </ul> </li> </ul> <p>The observed network traffic contains privacy-sensitive information. Hereby, we declare that the monitored data used for our research were processed in accordance with the EU General Data Protection Regulation 2016/679. The published dataset was anonymized with cryptographic means using Crypto-PAn algorithm to preserve both the scientific value and user privacy.</p> <p>When using this dataset, please cite the original work as follows:</p> <pre><code>@inproceedings{lastovicka2020using, title={Using TLS Fingerprints for OS Identification in Encrypted Traffic}, author={La{\v{s}}tovi{\v{c}}ka, Martin and {\v{S}}pa{\v{c}}ek, Stanislav and Velan, Petr and {\v{C}}eleda, Pavel}, booktitle = {2020 IEEE/IFIP Network Operations and Management Symposium (NOMS 2020)}, doi = {http://dx.doi.org/10.1109/NOMS47738.2020.9110319}, keywords = {OS fingerprinting;passive monitoring;IPFIX;TLS}, isbn = {978-1-7281-4973-8}, pages = {1-6}, publisher = {IEEE Xplore Digital Library}, year = {2020} }</code></pre> <p> </p>
Speed Prediction in Large and Dynamic Traffic Sensor Networks
<p>Aggregated traffic sensor data from Fortaleza (Brazil) in 2014.</p> <p>Please cite the following paper when using the dataset:</p> <p>R.P. Magalhaes, F. Lettich, J.A. Macedo, F.M. Nardini, R. Perego, C. Renso, R. Trani., <strong>Speed prediction in large and dynamic traffic sensor networks</strong>, Information Systems (2019) 101444, <a href="https://doi.org/10.1016/j.is.2019.101444">https://doi.org/10.1016/j.is.2019.101444</a></p> <p>You can also check details regarding the dataset in the paper.</p>
Ruth Traffic Simulator - Input for Prague, Boston
<p>Input data for simulations of 10 000 and 60 000 vehicles in Prague and 300 000 vehicles in Boston.<br>The map file should be included in "./data" folder, in working directory.</p> <p>Prague:<br>vehicles - prague-10K.parquet or prague-60K.parquet<br>map - 50_200432299999996-14_1755692-49_9151047-14_7526108_2024-02-13T00-00-00.graphml</p> <p>Boston:<br>vehicles - boston-300K.parquet<br>map - 42_47641909231966--71_27465474058792-42_14946390799415--70_90361558027409_2024-02-13T00-00-00.graphml</p> <p> </p>
Support data for conference paper "Service-Oriented Model for Handling mMTC Subscribers' Traffic in a 5G Cluster"
<p>Support data for conference paper<br>V. Kovtun, and O. Kovtun, “Service-Oriented Model for Handling mMTC Subscribers’ Traffic in a 5G Cluster.” In Proc. 5th 5th International Workshop on Intelligent Information Technologies & Systems of Information Security, CEUR-WS, vol. 3675, 2024; pp. 236-246.<br>This research is part of the project No. 2022/45/P/ST7/03450 co-funded by the National Science Centre and the European Union Framework Programme for Research and Innovation Horizon 2020 under the Marie Skłodowska-Curie grant agreement No. 945339.</p>
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.