This statistic shows the total business internet data traffic in the United States from 2016 to 2023. In 2017, total business internet traffic volume amounted to 56.7 billion gigabytes in the United States.

The statistic shows estimated internet data traffic per month in the United States from 2018 to 2023. In 2018, total internet data traffic was estimated to amount to 33.45 million exabytes per month.

Internet traffic volume measures global IP traffic, or the amount of data being sent and received over the internet globally each month. Data and forecasts are sourced from Cisco Systems Inc.

This statistic shows the data volume development in stationary broadband internet traffic via landline in Germany from 2001 to 2022, with an estimate for 2023. In 2023, the data volume was estimated to have amounted to roughly 142.1 billion gigabytes.

This statistic shows data on consumer internet traffic per month in Latin America in 2016 and 2017 as well as a forecast thereof until 2022. In 2017, the traffic reached six exabytes per month. This is expected to grow to 16 exabytes by 2022.

Hong Kong Internet Traffic Volume, Excludes Lease Line Access data was reported at 14,399.000 min th in Aug 2018. This records an increase from the previous number of 14,238.000 min th for Jul 2018. Hong Kong Internet Traffic Volume, Excludes Lease Line Access data is updated monthly, averaging 27,200.000 min th from Aug 1997 (Median) to Aug 2018, with 253 observations. The data reached an all-time high of 1,441,938.000 min th in Aug 2000 and a record low of 11,244.000 min th in Feb 2018. Hong Kong Internet Traffic Volume, Excludes Lease Line Access data remains active status in CEIC and is reported by Office of the Communications Authority. The data is categorized under Global Database’s Hong Kong SAR – Table HK.TB004: Internet Statistics: Office of the Telecommunication Authority.

This resource includes input data used in the work "Machine-Learning Based Prediction of Multiple Types of Network Traffic" by Aleksandra Knapińska, Piotr Lechowicz, and Krzysztof Walkowiak; published in International Conference on Computational Science (ICCS) 2021, Lecture Notes in Computer Science, vol 12742. pp. 122-136. Springer, Cham. https://doi.org/10.1007/978-3-030-77961-0_12 The work was supported by the National Science Centre, Poland, under Grant 2019/35/B/ST7/04272. Both seattle_november.xml and seattle_december.xml files include internet traffic data from Seattle Internet Exchange Point. The european.xml file includes internet traffic data from one of the European Internet Exchange Points. Each file includes the traffic volume decomposed into specific frame size ranges. Each file starts with a metadata section providing general information. The period covered by a specific file is indicated by its 'start' and 'end' tags. They provide Unix timestamps in the GMT timezone. It should be noted that Seattle lies in the PST time zone, and the European IXP is located in the CET timezone, so the start and end times should be adjusted accordingly. The step parameter is given in seconds, so the samples are stored every 5 minutes in all three files. Each file has multiple columns providing traffic data in bits per second for different frame size ranges. Column names specify the ranges in bytes. The 'total' column stores information about the total aggregate traffic volume, which is a sum of values in all the remaining columns in each row.

In 2017, mobile data and internet traffic per month in Latin America reached 0.75 exabytes per month. This value was expected to grow to 4.44 exabytes by 2022. The installed capacity of interconnection bandwidth in the region is forecasted to amount to 1.43 petabits per second.

Internet Usage: Search Engine Market Share: Mobile: Haosou data was reported at 0.000 % in 24 Dec 2024. This records a decrease from the previous number of 0.030 % for 23 Dec 2024. Internet Usage: Search Engine Market Share: Mobile: Haosou data is updated daily, averaging 0.000 % from Dec 2024 (Median) to 24 Dec 2024, with 6 observations. The data reached an all-time high of 0.050 % in 20 Dec 2024 and a record low of 0.000 % in 24 Dec 2024. Internet Usage: Search Engine Market Share: Mobile: Haosou data remains active status in CEIC and is reported by Statcounter Global Stats. The data is categorized under Global Database’s Syrian Arab Republic – Table SY.SC.IU: Internet Usage: Search Engine Market Share.

TERMS OF USE 1. Restriction on the use of Material on this websiteUsage and/or downloading this data indicates Your acceptance of the terms and conditions below.The data here controlled and operated by the Corporation of the County of Lennox and Addington (referred to the “County” herein) and is protected by copyright. No part of the information herein may be sold, copied, distributed, or transmitted in any form without the prior written consent of the County. All rights reserved. Copyright 2018 by the Corporation of the County of Lennox and Addington.2. DisclaimerThe County makes no representation, warranty or guarantee as to the content, accuracy, currency or completeness of any of the information provided on this website. The County explicitly disclaims any representations, warranties and guarantees, including, without limitation, the implied warranties of merchantability and fitness for a particular purpose.3. Limitation of LiabilityThe County is not responsible for any special, indirect, incidental or consequential damages that may arise from the use of or the inability to use, any web pages and/or the materials contained on the web page whether the materials are provided by the County or by a third party. Without limiting the generality of the foregoing, the County assumes no responsibility whatsoever for: any errors omissions, or inaccuracies in the information provided, regardless of how caused; or any decision made or action taken or not taken by the reader or other third party in reliance upon any information or data furnished on any web page.The Data is provided "as is" without warranty or any representation of accuracy, timeliness or completeness. The burden for determining accuracy, completeness, timeliness, merchantability and fitness for or the appropriateness for use rests solely on the requester. Lennox and Addington County makes no warranties, express or implied, as to the use of the Data. There are no implied warranties of merchantability or fitness for a particular purpose. The requester acknowledges and accepts the limitations of the Data, including the fact that the Data is dynamic and is in a constant state of maintenance, corrections and update.

Web App. Traffic counts in St. Louis County, Missouri. Traffic count locations showing day, time, volume, etc. Link to Metadata.

Internet Usage: Search Engine Market Share: Tablet: Haosou data was reported at 0.000 % in 06 Mar 2025. This stayed constant from the previous number of 0.000 % for 05 Mar 2025. Internet Usage: Search Engine Market Share: Tablet: Haosou data is updated daily, averaging 0.000 % from Feb 2025 (Median) to 06 Mar 2025, with 9 observations. The data reached an all-time high of 0.740 % in 02 Mar 2025 and a record low of 0.000 % in 06 Mar 2025. Internet Usage: Search Engine Market Share: Tablet: Haosou data remains active status in CEIC and is reported by Statcounter Global Stats. The data is categorized under Global Database’s Syrian Arab Republic – Table SY.SC.IU: Internet Usage: Search Engine Market Share.

Average Annual Daily Traffic data for use with GIS mapping software, databases, and web applications are from Caliper Corporation and contain data on the total volume of vehicle traffic on a highway or road for a year divided by 365 days.

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.

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]

The census count of vehicles on city streets is normally reported in the form of Average Daily Traffic (ADT) counts. These counts provide a good estimate for the actual number of vehicles on an average weekday at select street segments. Specific block segments are selected for a count because they are deemed as representative of a larger segment on the same roadway. ADT counts are used by transportation engineers, economists, real estate agents, planners, and others professionals for planning and operational analysis. The frequency for each count varies depending on City staff’s needs for analysis in any given area. This report covers the counts taken in our City during the past 12 years approximately.

This traffic-count data is provided by the City of Pittsburgh's Department of Mobility & Infrastructure (DOMI). Counters were deployed as part of traffic studies, including intersection studies, and studies covering where or whether to install speed humps. In some cases, data may have been collected by the Southwestern Pennsylvania Commission (SPC) or BikePGH.

Data is currently available for only the most-recent count at each location.

Traffic count data is important to the process for deciding where to install speed humps. According to DOMI, they may only be legally installed on streets where traffic counts fall below a minimum threshhold. Residents can request an evaluation of their street as part of DOMI's Neighborhood Traffic Calming Program. The City has also shared data on the impact of the Neighborhood Traffic Calming Program in reducing speeds.

Different studies may collect different data. Speed hump studies capture counts and speeds. SPC and BikePGH conduct counts of cyclists. Intersection studies included in this dataset may not include traffic counts, but reports of individual studies may be requested from the City. Despite the lack of count data, intersection studies are included to facilitate data requests.

Data captured by different types of counting devices are included in this data. StatTrak counters are in use by the City, and capture data on counts and speeds. More information about these devices may be found on the company's website. Data includes traffic counts and average speeds, and may also include separate counts of bicycles.

Tubes are deployed by both SPC and BikePGH and used to count cyclists. SPC may also deploy video counters to collect data.

NOTE: The data in this dataset has not updated since 2021 because of a broken data feed. We're working to fix it.

The global number of internet users in was forecast to continuously increase between 2024 and 2029 by in total 1.3 billion users (+23.66 percent). After the fifteenth consecutive increasing year, the number of users is estimated to reach 7 billion users and therefore a new peak in 2029. Notably, the number of internet users of was continuously increasing over the past years.Depicted is the estimated number of individuals in the country or region at hand, that use the internet. As the datasource clarifies, connection quality and usage frequency are distinct aspects, not taken into account here.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the number of internet users in countries like the Americas and Asia.

This resource includes input data used in the work "Long-term prediction of multiple types of time-varying network traffic using chunk-based ensemble learning" by Aleksandra Knapińska, Piotr Lechowicz, Weronika Węgier, and Krzysztof Walkowiak. The work was supported by the National Science Centre, Poland, under Grants 2019/35/B/ST7/04272, 2018/31/D/ST6/0304, and 2019/35/B/ST6/04442.
The SIX2021.xml file includes internet traffic data from the Seattle Internet Exchange Point collected for one year. The file contains information about the traffic volume decomposed into specific frame size ranges. It starts with a metadata section providing general information. The covered period is indicated by the 'start' and 'end' tags. They provide Unix timestamps in the GMT timezone. It should be noted that Seattle lies in the PST time zone, so the start and end times should be adjusted accordingly. The step parameter is given in seconds, so the samples are stored every 5 minutes. The file has multiple columns providing traffic data in bits per second for different frame size ranges. Column names specify the ranges in bytes. The 'total' column stores information about the total aggregate traffic volume, which is a sum of values in all the remaining columns in each row.

The Annual Average Daily Traffic (AADT) for sections of roads for all vehicle types, including single and combination trucks, reported in the 2023 Highway Performance Monitoring System (HPMS) federal report.Annual Average Daily Traffic (AADT) is used to represent vehicle traffic on a typical day of the year and is important for planning purposes, such as defining the federal functional classification of a roadway. The values are calculated using data collected from traffic counter devices, such as Automatic Traffic Recorders (ATR), Weigh In Motion (WIM) devices, and short term counters using tubes. All available traffic data collected throughout the year are then summed and divided by 365 to calculate the annual average daily traffic.Single unit trucks are any trucks that meets the requirements established for the FHWA Truck Classification Method for Categories 4 through 7. Combination unit trucks are any trucks that meets the requirements established for the FHWA Truck Classification Method for Categories 8 through 13. Refer to the Federal Highway Administration website for more information about truck classifications.Reported Extent: State Highway System (i.e. all ADOT-owned roads), National Highway System (NHS), and all federal aid-eligible roads. Federal aid-eligible roads include urban roads classified as minor collectors or above (functional system 1-6) and rural roads classified as major collectors or above (function system 1-5). Roads where ATRs are available, counts are updated annually. For roads where short term counters must be used, traffic counts are collected every three years for all National Highway System (NHS) roads as well as interstates (functional system 1), principal arterials (functional systems 2-3), and sample panel sections. All other federal aid-eligible roads, including minor arterials and collectors, are collected every six years.For undivided highways, which do not have a physical barrier between the two directions of traffic, values are reported as the sum total for both directions of travel. On divided highways, AADT is reported separately on the cardinal and non-cardinal directions of the roadway. Note, the cardinal direction refers to the direction of increasing mileposts.

The FDOT Annual Average Daily Traffic feature class provides spatial information on Annual Average Daily Traffic section breaks for the state of Florida. In addition, it provides affiliated traffic information like KFCTR, DFCTR and TFCTR among others. This dataset is maintained by the Transportation Data & Analytics office (TDA). The source spatial data for this hosted feature layer was created on: 06/14/2025.Download Data: Enter Guest as Username to download the source shapefile from here: https://ftp.fdot.gov/file/d/FTP/FDOT/co/planning/transtat/gis/shapefiles/aadt.zip