100+ datasets found
  1. d

    Open Data Website Traffic

    • catalog.data.gov
    • data.lacity.org
    Updated Jun 21, 2025
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    data.lacity.org (2025). Open Data Website Traffic [Dataset]. https://catalog.data.gov/dataset/open-data-website-traffic
    Explore at:
    Dataset updated
    Jun 21, 2025
    Dataset provided by
    data.lacity.org
    Description

    Daily utilization metrics for data.lacity.org and geohub.lacity.org. Updated monthly

  2. d

    Website Analytics

    • catalog.data.gov
    • data.brla.gov
    • +2more
    Updated Jun 29, 2025
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    data.brla.gov (2025). Website Analytics [Dataset]. https://catalog.data.gov/dataset/website-analytics-89ba5
    Explore at:
    Dataset updated
    Jun 29, 2025
    Dataset provided by
    data.brla.gov
    Description

    Web traffic statistics for the several City-Parish websites, brla.gov, city.brla.gov, Red Stick Ready, GIS, Open Data etc. Information provided by Google Analytics.

  3. d

    Web Traffic Data | 500M+ US Web Traffic Data Resolution | B2B and B2C...

    • datarade.ai
    .csv, .xls
    Updated Feb 24, 2025
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Allforce (2025). Web Traffic Data | 500M+ US Web Traffic Data Resolution | B2B and B2C Website Visitor Identity Resolution [Dataset]. https://datarade.ai/data-products/traffic-continuum-from-solution-publishing-500m-us-web-traf-solution-publishing
    Explore at:
    .csv, .xlsAvailable download formats
    Dataset updated
    Feb 24, 2025
    Dataset authored and provided by
    Allforce
    Area covered
    United States
    Description

    Unlock the Potential of Your Web Traffic with Advanced Data Resolution

    In the digital age, understanding and leveraging web traffic data is crucial for businesses aiming to thrive online. Our pioneering solution transforms anonymous website visits into valuable B2B and B2C contact data, offering unprecedented insights into your digital audience. By integrating our unique tag into your website, you unlock the capability to convert 25-50% of your anonymous traffic into actionable contact rows, directly deposited into an S3 bucket for your convenience. This process, known as "Web Traffic Data Resolution," is at the forefront of digital marketing and sales strategies, providing a competitive edge in understanding and engaging with your online visitors.

    Comprehensive Web Traffic Data Resolution Our product stands out by offering a robust solution for "Web Traffic Data Resolution," a process that demystifies the identities behind your website traffic. By deploying a simple tag on your site, our technology goes to work, analyzing visitor behavior and leveraging proprietary data matching techniques to reveal the individuals and businesses behind the clicks. This innovative approach not only enhances your data collection but does so with respect for privacy and compliance standards, ensuring that your business gains insights ethically and responsibly.

    Deep Dive into Web Traffic Data At the core of our solution is the sophisticated analysis of "Web Traffic Data." Our system meticulously collects and processes every interaction on your site, from page views to time spent on each section. This data, once anonymous and perhaps seen as abstract numbers, is transformed into a detailed ledger of potential leads and customer insights. By understanding who visits your site, their interests, and their contact information, your business is equipped to tailor marketing efforts, personalize customer experiences, and streamline sales processes like never before.

    Benefits of Our Web Traffic Data Resolution Service Enhanced Lead Generation: By converting anonymous visitors into identifiable contact data, our service significantly expands your pool of potential leads. This direct enhancement of your lead generation efforts can dramatically increase conversion rates and ROI on marketing campaigns.

    Targeted Marketing Campaigns: Armed with detailed B2B and B2C contact data, your marketing team can create highly targeted and personalized campaigns. This precision in marketing not only improves engagement rates but also ensures that your messaging resonates with the intended audience.

    Improved Customer Insights: Gaining a deeper understanding of your web traffic enables your business to refine customer personas and tailor offerings to meet market demands. These insights are invaluable for product development, customer service improvement, and strategic planning.

    Competitive Advantage: In a digital landscape where understanding your audience can make or break your business, our Web Traffic Data Resolution service provides a significant competitive edge. By accessing detailed contact data that others in your industry may overlook, you position your business as a leader in customer engagement and data-driven strategies.

    Seamless Integration and Accessibility: Our solution is designed for ease of use, requiring only the placement of a tag on your website to start gathering data. The contact rows generated are easily accessible in an S3 bucket, ensuring that you can integrate this data with your existing CRM systems and marketing tools without hassle.

    How It Works: A Closer Look at the Process Our Web Traffic Data Resolution process is streamlined and user-friendly, designed to integrate seamlessly with your existing website infrastructure:

    Tag Deployment: Implement our unique tag on your website with simple instructions. This tag is lightweight and does not impact your site's loading speed or user experience.

    Data Collection and Analysis: As visitors navigate your site, our system collects web traffic data in real-time, analyzing behavior patterns, engagement metrics, and more.

    Resolution and Transformation: Using advanced data matching algorithms, we resolve the collected web traffic data into identifiable B2B and B2C contact information.

    Data Delivery: The resolved contact data is then securely transferred to an S3 bucket, where it is organized and ready for your access. This process occurs daily, ensuring you have the most up-to-date information at your fingertips.

    Integration and Action: With the resolved data now in your possession, your business can take immediate action. From refining marketing strategies to enhancing customer experiences, the possibilities are endless.

    Security and Privacy: Our Commitment Understanding the sensitivity of web traffic data and contact information, our solution is built with security and privacy at its core. We adhere to strict data protection regulat...

  4. Z

    Network Traffic Analysis: Data and Code

    • data.niaid.nih.gov
    • zenodo.org
    Updated Jun 12, 2024
    + more versions
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Honig, Joshua (2024). Network Traffic Analysis: Data and Code [Dataset]. https://data.niaid.nih.gov/resources?id=zenodo_11479410
    Explore at:
    Dataset updated
    Jun 12, 2024
    Dataset provided by
    Homan, Sophia
    Moran, Madeline
    Chan-Tin, Eric
    Ferrell, Nathan
    Soni, Shreena
    Honig, Joshua
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Code:

    Packet_Features_Generator.py & Features.py

    To run this code:

    pkt_features.py [-h] -i TXTFILE [-x X] [-y Y] [-z Z] [-ml] [-s S] -j

    -h, --help show this help message and exit -i TXTFILE input text file -x X Add first X number of total packets as features. -y Y Add first Y number of negative packets as features. -z Z Add first Z number of positive packets as features. -ml Output to text file all websites in the format of websiteNumber1,feature1,feature2,... -s S Generate samples using size s. -j

    Purpose:

    Turns a text file containing lists of incomeing and outgoing network packet sizes into separate website objects with associative features.

    Uses Features.py to calcualte the features.

    startMachineLearning.sh & machineLearning.py

    To run this code:

    bash startMachineLearning.sh

    This code then runs machineLearning.py in a tmux session with the nessisary file paths and flags

    Options (to be edited within this file):

    --evaluate-only to test 5 fold cross validation accuracy

    --test-scaling-normalization to test 6 different combinations of scalers and normalizers

    Note: once the best combination is determined, it should be added to the data_preprocessing function in machineLearning.py for future use

    --grid-search to test the best grid search hyperparameters - note: the possible hyperparameters must be added to train_model under 'if not evaluateOnly:' - once best hyperparameters are determined, add them to train_model under 'if evaluateOnly:'

    Purpose:

    Using the .ml file generated by Packet_Features_Generator.py & Features.py, this program trains a RandomForest Classifier on the provided data and provides results using cross validation. These results include the best scaling and normailzation options for each data set as well as the best grid search hyperparameters based on the provided ranges.

    Data

    Encrypted network traffic was collected on an isolated computer visiting different Wikipedia and New York Times articles, different Google search queres (collected in the form of their autocomplete results and their results page), and different actions taken on a Virtual Reality head set.

    Data for this experiment was stored and analyzed in the form of a txt file for each experiment which contains:

    First number is a classification number to denote what website, query, or vr action is taking place.

    The remaining numbers in each line denote:

    The size of a packet,

    and the direction it is traveling.

    negative numbers denote incoming packets

    positive numbers denote outgoing packets

    Figure 4 Data

    This data uses specific lines from the Virtual Reality.txt file.

    The action 'LongText Search' refers to a user searching for "Saint Basils Cathedral" with text in the Wander app.

    The action 'ShortText Search' refers to a user searching for "Mexico" with text in the Wander app.

    The .xlsx and .csv file are identical

    Each file includes (from right to left):

    The origional packet data,

    each line of data organized from smallest to largest packet size in order to calculate the mean and standard deviation of each packet capture,

    and the final Cumulative Distrubution Function (CDF) caluclation that generated the Figure 4 Graph.

  5. d

    Click Global Data | Web Traffic Data + Transaction Data | Consumer and B2B...

    • datarade.ai
    .csv
    Updated Mar 13, 2025
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Consumer Edge (2025). Click Global Data | Web Traffic Data + Transaction Data | Consumer and B2B Shopper Insights | 59 Countries, 3-Day Lag, Daily Delivery [Dataset]. https://datarade.ai/data-products/click-global-data-web-traffic-data-transaction-data-con-consumer-edge
    Explore at:
    .csvAvailable download formats
    Dataset updated
    Mar 13, 2025
    Dataset authored and provided by
    Consumer Edge
    Area covered
    Bermuda, Congo, El Salvador, South Africa, Marshall Islands, Finland, Sri Lanka, Bosnia and Herzegovina, Montserrat, Nauru
    Description

    Click Web Traffic Combined with Transaction Data: A New Dimension of Shopper Insights

    Consumer Edge is a leader in alternative consumer data for public and private investors and corporate clients. Click enhances the unparalleled accuracy of CE Transact by allowing investors to delve deeper and browse further into global online web traffic for CE Transact companies and more. Leverage the unique fusion of web traffic and transaction datasets to understand the addressable market and understand spending behavior on consumer and B2B websites. See the impact of changes in marketing spend, search engine algorithms, and social media awareness on visits to a merchant’s website, and discover the extent to which product mix and pricing drive or hinder visits and dwell time. Plus, Click uncovers a more global view of traffic trends in geographies not covered by Transact. Doubleclick into better forecasting, with Click.

    Consumer Edge’s Click is available in machine-readable file delivery and enables: • Comprehensive Global Coverage: Insights across 620+ brands and 59 countries, including key markets in the US, Europe, Asia, and Latin America. • Integrated Data Ecosystem: Click seamlessly maps web traffic data to CE entities and stock tickers, enabling a unified view across various business intelligence tools. • Near Real-Time Insights: Daily data delivery with a 5-day lag ensures timely, actionable insights for agile decision-making. • Enhanced Forecasting Capabilities: Combining web traffic indicators with transaction data helps identify patterns and predict revenue performance.

    Use Case: Analyze Year Over Year Growth Rate by Region

    Problem A public investor wants to understand how a company’s year-over-year growth differs by region.

    Solution The firm leveraged Consumer Edge Click data to: • Gain visibility into key metrics like views, bounce rate, visits, and addressable spend • Analyze year-over-year growth rates for a time period • Breakout data by geographic region to see growth trends

    Metrics Include: • Spend • Items • Volume • Transactions • Price Per Volume

    Inquire about a Click subscription to perform more complex, near real-time analyses on public tickers and private brands as well as for industries beyond CPG like: • Monitor web traffic as a leading indicator of stock performance and consumer demand • Analyze customer interest and sentiment at the brand and sub-brand levels

    Consumer Edge offers a variety of datasets covering the US, Europe (UK, Austria, France, Germany, Italy, Spain), and across the globe, with subscription options serving a wide range of business needs.

    Consumer Edge is the Leader in Data-Driven Insights Focused on the Global Consumer

  6. World Traffic Web Map

    • walmart-event-collaboration-portal-walmarttech.hub.arcgis.com
    Updated Jun 18, 2021
    + more versions
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Walmart Emergency Management (2021). World Traffic Web Map [Dataset]. https://walmart-event-collaboration-portal-walmarttech.hub.arcgis.com/maps/world-traffic-web-map
    Explore at:
    Dataset updated
    Jun 18, 2021
    Dataset provided by
    Walmarthttp://walmart.com/
    Authors
    Walmart Emergency Management
    Area covered
    Description

    This is a dynamic traffic map service with capabilities for visualizing traffic speeds relative to free-flow speeds as well as traffic incidents which can be visualized and identified. The traffic data is updated every five minutes. Traffic speeds are displayed as a percentage of free-flow speeds, which is frequently the speed limit or how fast cars tend to travel when unencumbered by other vehicles. The streets are color coded as follows:Green (fast): 85 - 100% of free flow speedsYellow (moderate): 65 - 85%Orange (slow); 45 - 65%Red (stop and go): 0 - 45%Esri's historical, live, and predictive traffic feeds come directly from HERE (www.HERE.com). HERE collects billions of GPS and cell phone probe records per month and, where available, uses sensor and toll-tag data to augment the probe data collected. An advanced algorithm compiles the data and computes accurate speeds. Historical traffic is based on the average of observed speeds over the past three years. The live and predictive traffic data is updated every five minutes through traffic feeds. The color coded traffic map layer can be used to represent relative traffic speeds; this is a common type of a map for online services and is used to provide context for routing, navigation and field operations. The traffic map layer contains two sublayers: Traffic and Live Traffic. The Traffic sublayer (shown by default) leverages historical, live and predictive traffic data; while the Live Traffic sublayer is calculated from just the live and predictive traffic data only. A color coded traffic map image can be requested for the current time and any time in the future. A map image for a future request might be used for planning purposes. The map layer also includes dynamic traffic incidents showing the location of accidents, construction, closures and other issues that could potentially impact the flow of traffic. Traffic incidents are commonly used to provide context for routing, navigation and field operations. Incidents are not features; they cannot be exported and stored for later use or additional analysis. The service works globally and can be used to visualize traffic speeds and incidents in many countries. Check the service coverage web map to determine availability in your area of interest. In the coverage map, the countries color coded in dark green support visualizing live traffic. The support for traffic incidents can be determined by identifying a country. For detailed information on this service, including a data coverage map, visit the directions and routing documentation and ArcGIS Help.

  7. Share of U.S. mobile website traffic 2015-2023

    • statista.com
    Updated Mar 25, 2024
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Statista (2024). Share of U.S. mobile website traffic 2015-2023 [Dataset]. https://www.statista.com/statistics/683082/share-of-website-traffic-coming-from-mobile-devices-usa/
    Explore at:
    Dataset updated
    Mar 25, 2024
    Dataset authored and provided by
    Statistahttp://statista.com/
    Area covered
    United States
    Description

    As of the last quarter of 2023, 31.57 percent of web traffic in the United States originated from mobile devices, down from 49.51 percent in the fourth quarter of 2022. In comparison, over half of web traffic worldwide was generated via mobile in the last examined period.

  8. Kaggle Wikipedia Web Traffic Daily Dataset (without Missing Values)

    • zenodo.org
    • explore.openaire.eu
    • +1more
    zip
    Updated Apr 1, 2021
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Rakshitha Godahewa; Rakshitha Godahewa; Christoph Bergmeir; Christoph Bergmeir; Geoff Webb; Geoff Webb; Rob Hyndman; Rob Hyndman; Pablo Montero-Manso; Pablo Montero-Manso (2021). Kaggle Wikipedia Web Traffic Daily Dataset (without Missing Values) [Dataset]. http://doi.org/10.5281/zenodo.4656075
    Explore at:
    zipAvailable download formats
    Dataset updated
    Apr 1, 2021
    Dataset provided by
    Zenodohttp://zenodo.org/
    Authors
    Rakshitha Godahewa; Rakshitha Godahewa; Christoph Bergmeir; Christoph Bergmeir; Geoff Webb; Geoff Webb; Rob Hyndman; Rob Hyndman; Pablo Montero-Manso; Pablo Montero-Manso
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    This dataset was used in the Kaggle Wikipedia Web Traffic forecasting competition. It contains 145063 daily time series representing the number of hits or web traffic for a set of Wikipedia pages from 2015-07-01 to 2017-09-10.

    The original dataset contains missing values. They have been simply replaced by zeros.

  9. G

    Website traffic strategies by industry and size of enterprise

    • open.canada.ca
    • datasets.ai
    • +3more
    csv, html, xml
    Updated Jan 17, 2023
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Statistics Canada (2023). Website traffic strategies by industry and size of enterprise [Dataset]. https://open.canada.ca/data/en/dataset/a7882acc-a647-4fa6-a58b-6dae889de472
    Explore at:
    csv, xml, htmlAvailable download formats
    Dataset updated
    Jan 17, 2023
    Dataset provided by
    Statistics Canada
    License

    Open Government Licence - Canada 2.0https://open.canada.ca/en/open-government-licence-canada
    License information was derived automatically

    Description

    Digital technology and Internet use, website traffic strategies, by North American Industry Classification System (NAICS) and size of enterprise for Canada from 2012 to 2013.

  10. P

    Traffic Dataset

    • paperswithcode.com
    Updated Mar 13, 2024
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    (2024). Traffic Dataset [Dataset]. https://paperswithcode.com/dataset/traffic
    Explore at:
    Dataset updated
    Mar 13, 2024
    Description

    Abstract: The task for this dataset is to forecast the spatio-temporal traffic volume based on the historical traffic volume and other features in neighboring locations.

    Data Set CharacteristicsNumber of InstancesAreaAttribute CharacteristicsNumber of AttributesDate DonatedAssociated TasksMissing Values
    Multivariate2101ComputerReal472020-11-17RegressionN/A

    Source: Liang Zhao, liang.zhao '@' emory.edu, Emory University.

    Data Set Information: The task for this dataset is to forecast the spatio-temporal traffic volume based on the historical traffic volume and other features in neighboring locations. Specifically, the traffic volume is measured every 15 minutes at 36 sensor locations along two major highways in Northern Virginia/Washington D.C. capital region. The 47 features include: 1) the historical sequence of traffic volume sensed during the 10 most recent sample points (10 features), 2) week day (7 features), 3) hour of day (24 features), 4) road direction (4 features), 5) number of lanes (1 feature), and 6) name of the road (1 feature). The goal is to predict the traffic volume 15 minutes into the future for all sensor locations. With a given road network, we know the spatial connectivity between sensor locations. For the detailed data information, please refer to the file README.docx.

    Attribute Information: The 47 features include: (1) the historical sequence of traffic volume sensed during the 10 most recent sample points (10 features), (2) week day (7 features), (3) hour of day (24 features), (4) road direction (4 features), (5) number of lanes (1 feature), and (6) name of the road (1 feature).

    Relevant Papers: Liang Zhao, Olga Gkountouna, and Dieter Pfoser. 2019. Spatial Auto-regressive Dependency Interpretable Learning Based on Spatial Topological Constraints. ACM Trans. Spatial Algorithms Syst. 5, 3, Article 19 (August 2019), 28 pages. DOI:[Web Link]

    Citation Request: To use these datasets, please cite the papers:

    Liang Zhao, Olga Gkountouna, and Dieter Pfoser. 2019. Spatial Auto-regressive Dependency Interpretable Learning Based on Spatial Topological Constraints. ACM Trans. Spatial Algorithms Syst. 5, 3, Article 19 (August 2019), 28 pages. DOI:[Web Link]

  11. Global monthly traffic volume for leading online education platforms...

    • statista.com
    Updated Jun 24, 2025
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Statista (2025). Global monthly traffic volume for leading online education platforms 2022-2024 [Dataset]. https://www.statista.com/statistics/1462755/web-traffic-online-learning-platforms/
    Explore at:
    Dataset updated
    Jun 24, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    Dec 2022 - Jan 2024
    Area covered
    Worldwide
    Description

    Between December 2022 and January 2024, ******** was the online learning platform reporting the highest traffic, with a peak of *** million visits to its websites in December 2023. ******** ranked second, with the platform reaching a peak of ** million visits in the examined period. The website ******* (which stands for technology, entertainment, design) saw a peak of over ** million visits in March 2023.

  12. i

    ASNM Datasets: A Collection of Network Traffic Data for Testing of...

    • ieee-dataport.org
    Updated May 18, 2022
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Ivan Homoliak (2022). ASNM Datasets: A Collection of Network Traffic Data for Testing of Adversarial Classifiers and Network Intrusion Detectors [Dataset]. https://ieee-dataport.org/open-access/asnm-datasets-collection-network-traffic-data-testing-adversarial-classifiers-and
    Explore at:
    Dataset updated
    May 18, 2022
    Authors
    Ivan Homoliak
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    ASNM datasets include records consisting of many features

  13. c

    Click Global Data | Web Traffic Data + Transaction Data | Consumer and B2B...

    • dataproducts.consumeredge.com
    Updated Mar 13, 2025
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Consumer Edge (2025). Click Global Data | Web Traffic Data + Transaction Data | Consumer and B2B Shopper Insights | 59 Countries, 3-Day Lag, Daily Delivery [Dataset]. https://dataproducts.consumeredge.com/products/click-global-data-web-traffic-data-transaction-data-con-consumer-edge
    Explore at:
    Dataset updated
    Mar 13, 2025
    Dataset authored and provided by
    Consumer Edge
    Area covered
    Sao Tome and Principe, Tonga, Svalbard and Jan Mayen, Syrian Arab Republic, Albania, French Polynesia, New Zealand, Trinidad and Tobago, Saint Barthélemy, Vanuatu
    Description

    Click is global web traffic data combined with transaction data to enhance signal capabilities. Click adds a layer to your analysis for richer spending behavior on consumer & B2B websites, addressable market overview, trends and shopping behavior shifts, and effects of pricing on growth.

  14. d

    Traffic Count Segments

    • catalog.data.gov
    • data-academy.tempe.gov
    • +7more
    Updated Sep 20, 2024
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    City of Tempe (2024). Traffic Count Segments [Dataset]. https://catalog.data.gov/dataset/traffic-count-segments-4a2ab
    Explore at:
    Dataset updated
    Sep 20, 2024
    Dataset provided by
    City of Tempe
    Description

    This dataset consists of 24-hour traffic volumes which are collected by the City of Tempe high (arterial) and low (collector) volume streets. Data located in the tabular section shares with its users total volume of vehicles passing through the intersection selected along with the direction of flow.Historical data from this feature layer extends from 2016 to present day.Contact: Sue TaaffeContact E-Mail: sue_taaffe@tempe.govContact Phone: 480-350-8663Link to embedded web map:http://www.tempe.gov/city-hall/public-works/transportation/traffic-countsLink to site containing historical traffic counts by node: https://gis.tempe.gov/trafficcounts/Folders/Data Source: SQL Server/ArcGIS ServerData Source Type: GeospatialPreparation Method: N/APublish Frequency: As information changesPublish Method: AutomaticData Dictionary

  15. Share of global mobile website traffic 2015-2024

    • statista.com
    • ai-chatbox.pro
    Updated Jan 28, 2025
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Statista (2025). Share of global mobile website traffic 2015-2024 [Dataset]. https://www.statista.com/statistics/277125/share-of-website-traffic-coming-from-mobile-devices/
    Explore at:
    Dataset updated
    Jan 28, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Area covered
    Worldwide
    Description

    Mobile accounts for approximately half of web traffic worldwide. In the last quarter of 2024, mobile devices (excluding tablets) generated 62.54 percent of global website traffic. Mobiles and smartphones consistently hoovered around the 50 percent mark since the beginning of 2017, before surpassing it in 2020. Mobile traffic Due to low infrastructure and financial restraints, many emerging digital markets skipped the desktop internet phase entirely and moved straight onto mobile internet via smartphone and tablet devices. India is a prime example of a market with a significant mobile-first online population. Other countries with a significant share of mobile internet traffic include Nigeria, Ghana and Kenya. In most African markets, mobile accounts for more than half of the web traffic. By contrast, mobile only makes up around 45.49 percent of online traffic in the United States. Mobile usage The most popular mobile internet activities worldwide include watching movies or videos online, e-mail usage and accessing social media. Apps are a very popular way to watch video on the go and the most-downloaded entertainment apps in the Apple App Store are Netflix, Tencent Video and Amazon Prime Video.

  16. i

    Real road traffic network data

    • ieee-dataport.org
    Updated Nov 4, 2024
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    zhonghan zhang (2024). Real road traffic network data [Dataset]. https://ieee-dataport.org/documents/real-road-traffic-network-data
    Explore at:
    Dataset updated
    Nov 4, 2024
    Authors
    zhonghan zhang
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    D.C.

  17. a

    Web Traffic Data | 500M+ US Web Traffic Data Resolution | B2B and B2C...

    • data.allforce.io
    Updated Jun 19, 2025
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Allforce (2025). Web Traffic Data | 500M+ US Web Traffic Data Resolution | B2B and B2C Website Visitor Identity Resolution [Dataset]. https://data.allforce.io/
    Explore at:
    Dataset updated
    Jun 19, 2025
    Dataset authored and provided by
    Allforce
    Area covered
    United States
    Description
    • Capture the Identity of Your Anonymous Web Visitors.
    • 500M B2B & B2C Hash Email Resolutions
    • That Day's Contacts Deposited to an FTP Every Night
    • Full Contact Details for US Web Traffic Data Resolution
  18. a

    Traffic Site

    • hub.arcgis.com
    • data-waikatolass.opendata.arcgis.com
    • +1more
    Updated Sep 9, 2021
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Hamilton City Council (2021). Traffic Site [Dataset]. https://hub.arcgis.com/maps/hcc::traffic-site
    Explore at:
    Dataset updated
    Sep 9, 2021
    Dataset authored and provided by
    Hamilton City Council
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Attributes of sites in Hamilton City which collect anonymised data from a sample of vehicles. Note: A Link is the section of the road between two sites

    Column_InfoSite_Id, int : Unique identiferNumber, int : Asset number. Note: If the site is at a signalised intersection, Number will match 'Site_Number' in the table 'Traffic Signal Site Location'Is_Enabled, varchar : Site is currently enabledDisabled_Date, datetime : If currently disabled, the date at which the site was disabledSite_Name, varchar : Description of the site locationLatitude, numeric : North-south geographic coordinatesLongitude, numeric : East-west geographic coordinates

    Relationship
    
    
    
    
    
    
    
    
    
    Disclaimer
    
    Hamilton City Council does not make any representation or give any warranty as to the accuracy or exhaustiveness of the data released for public download. Levels, locations and dimensions of works depicted in the data may not be accurate due to circumstances not notified to Council. A physical check should be made on all levels, locations and dimensions before starting design or works.
    
    Hamilton City Council shall not be liable for any loss, damage, cost or expense (whether direct or indirect) arising from reliance upon or use of any data provided, or Council's failure to provide this data.
    
    While you are free to crop, export and re-purpose the data, we ask that you attribute the Hamilton City Council and clearly state that your work is a derivative and not the authoritative data source. Please include the following statement when distributing any work derived from this data:
    
    ‘This work is derived entirely or in part from Hamilton City Council data; the provided information may be updated at any time, and may at times be out of date, inaccurate, and/or incomplete.'
    
  19. Data from: Web Traffic Dataset

    • zenodo.org
    application/gzip, txt
    Updated Dec 6, 2021
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Mark R. Meiss; Filippo Menczer; Santo Fortunato; Alessandro Flammini; Alessandro Vespignani; José J. Ramasco; Mark R. Meiss; Filippo Menczer; Santo Fortunato; Alessandro Flammini; Alessandro Vespignani; José J. Ramasco (2021). Web Traffic Dataset [Dataset]. http://doi.org/10.5281/zenodo.4487194
    Explore at:
    txt, application/gzipAvailable download formats
    Dataset updated
    Dec 6, 2021
    Dataset provided by
    Zenodohttp://zenodo.org/
    Authors
    Mark R. Meiss; Filippo Menczer; Santo Fortunato; Alessandro Flammini; Alessandro Vespignani; José J. Ramasco; Mark R. Meiss; Filippo Menczer; Santo Fortunato; Alessandro Flammini; Alessandro Vespignani; José J. Ramasco
    Description

    A collection of Web (HTTP) requests for the month of November 2009. This is a small sample of the larger click dataset.

    • Source: Generated by applying a Berkeley Packet Filter to a mirror of the traffic passing through the border router of Indiana University.
    • Date range: Nov. 1, 2009 to November 22. 2009
    • File size: 235M requests; 2.7GB uncompressed
  20. m

    Network traffic and code for machine learning classification

    • data.mendeley.com
    Updated Feb 20, 2020
    + more versions
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Víctor Labayen (2020). Network traffic and code for machine learning classification [Dataset]. http://doi.org/10.17632/5pmnkshffm.2
    Explore at:
    Dataset updated
    Feb 20, 2020
    Authors
    Víctor Labayen
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    The dataset is a set of network traffic traces in pcap/csv format captured from a single user. The traffic is classified in 5 different activities (Video, Bulk, Idle, Web, and Interactive) and the label is shown in the filename. There is also a file (mapping.csv) with the mapping of the host's IP address, the csv/pcap filename and the activity label.

    Activities:

    Interactive: applications that perform real-time interactions in order to provide a suitable user experience, such as editing a file in google docs and remote CLI's sessions by SSH. Bulk data transfer: applications that perform a transfer of large data volume files over the network. Some examples are SCP/FTP applications and direct downloads of large files from web servers like Mediafire, Dropbox or the university repository among others. Web browsing: contains all the generated traffic while searching and consuming different web pages. Examples of those pages are several blogs and new sites and the moodle of the university. Vídeo playback: contains traffic from applications that consume video in streaming or pseudo-streaming. The most known server used are Twitch and Youtube but the university online classroom has also been used. Idle behaviour: is composed by the background traffic generated by the user computer when the user is idle. This traffic has been captured with every application closed and with some opened pages like google docs, YouTube and several web pages, but always without user interaction.

    The capture is performed in a network probe, attached to the router that forwards the user network traffic, using a SPAN port. The traffic is stored in pcap format with all the packet payload. In the csv file, every non TCP/UDP packet is filtered out, as well as every packet with no payload. The fields in the csv files are the following (one line per packet): Timestamp, protocol, payload size, IP address source and destination, UDP/TCP port source and destination. The fields are also included as a header in every csv file.

    The amount of data is stated as follows:

    Bulk : 19 traces, 3599 s of total duration, 8704 MBytes of pcap files Video : 23 traces, 4496 s, 1405 MBytes Web : 23 traces, 4203 s, 148 MBytes Interactive : 42 traces, 8934 s, 30.5 MBytes Idle : 52 traces, 6341 s, 0.69 MBytes

    The code of our machine learning approach is also included. There is a README.txt file with the documentation of how to use the code.

Share
FacebookFacebook
TwitterTwitter
Email
Click to copy link
Link copied
Close
Cite
data.lacity.org (2025). Open Data Website Traffic [Dataset]. https://catalog.data.gov/dataset/open-data-website-traffic

Open Data Website Traffic

Explore at:
Dataset updated
Jun 21, 2025
Dataset provided by
data.lacity.org
Description

Daily utilization metrics for data.lacity.org and geohub.lacity.org. Updated monthly

Search
Clear search
Close search
Google apps
Main menu