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.

In April 2025, over ** million unique global visitors visited groupon.com. The average time spent on the website was two minutes and ** seconds.

TrueCar is a marketplace and e-commerce site which provides new and used car buyers with pricing and information related to the car buying experience. In the second quarter of 2021, the company recorded *** million average monthly unique visitors to its site, the most of any quarter listed. Since then, quarterly website traffic has been declining, reaching *** million unique visitors in the most recent quarter.

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: 07/12/2025.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

General data recollected for the studio " Analysis of the Quantitative Impact of Social Networks on Web Traffic of Cybermedia in the 27 Countries of the European Union". Four research questions are posed: what percentage of the total web traffic generated by cybermedia in the European Union comes from social networks? Is said percentage higher or lower than that provided through direct traffic and through the use of search engines via SEO positioning? Which social networks have a greater impact? And is there any degree of relationship between the specific weight of social networks in the web traffic of a cybermedia and circumstances such as the average duration of the user's visit, the number of page views or the bounce rate understood in its formal aspect of not performing any kind of interaction on the visited page beyond reading its content? To answer these questions, we have first proceeded to a selection of the cybermedia with the highest web traffic of the 27 countries that are currently part of the European Union after the United Kingdom left on December 31, 2020. In each nation we have selected five media using a combination of the global web traffic metrics provided by the tools Alexa (https://www.alexa.com/), which ceased to be operational on May 1, 2022, and SimilarWeb (https:// www.similarweb.com/). We have not used local metrics by country since the results obtained with these first two tools were sufficiently significant and our objective is not to establish a ranking of cybermedia by nation but to examine the relevance of social networks in their web traffic. In all cases, cybermedia whose property corresponds to a journalistic company have been selected, ruling out those belonging to telecommunications portals or service providers; in some cases they correspond to classic information companies (both newspapers and televisions) while in others they refer to digital natives, without this circumstance affecting the nature of the research proposed.
Below we have proceeded to examine the web traffic data of said cybermedia. The period corresponding to the months of October, November and December 2021 and January, February and March 2022 has been selected. We believe that this six-month stretch allows possible one-time variations to be overcome for a month, reinforcing the precision of the data obtained. To secure this data, we have used the SimilarWeb tool, currently the most precise tool that exists when examining the web traffic of a portal, although it is limited to that coming from desktops and laptops, without taking into account those that come from mobile devices, currently impossible to determine with existing measurement tools on the market. It includes:

Web traffic general data: average visit duration, pages per visit and bounce rate Web traffic origin by country Percentage of traffic generated from social media over total web traffic Distribution of web traffic generated from social networks Comparison of web traffic generated from social netwoks with direct and search procedures

Context

The Google Merchandise Store sells Google branded merchandise. The data is typical of what you would see for an ecommerce website.

Content

The sample dataset contains Google Analytics 360 data from the Google Merchandise Store, a real ecommerce store. The Google Merchandise Store sells Google branded merchandise. The data is typical of what you would see for an ecommerce website. It includes the following kinds of information:

Traffic source data: information about where website visitors originate. This includes data about organic traffic, paid search traffic, display traffic, etc. Content data: information about the behavior of users on the site. This includes the URLs of pages that visitors look at, how they interact with content, etc. Transactional data: information about the transactions that occur on the Google Merchandise Store website.

Fork this kernel to get started.

Acknowledgements

Data from: https://bigquery.cloud.google.com/table/bigquery-public-data:google_analytics_sample.ga_sessions_20170801

Banner Photo by Edho Pratama from Unsplash.

Inspiration

What is the total number of transactions generated per device browser in July 2017?

The real bounce rate is defined as the percentage of visits with a single pageview. What was the real bounce rate per traffic source?

What was the average number of product pageviews for users who made a purchase in July 2017?

What was the average number of product pageviews for users who did not make a purchase in July 2017?

What was the average total transactions per user that made a purchase in July 2017?

What is the average amount of money spent per session in July 2017?

What is the sequence of pages viewed?

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.

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 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 data set features a hyperlink to the New York State Department of Transportation’s (NYSDOT) Traffic Data (TD) Viewer web page, which includes a link to the Traffic Data interactive map. The Traffic Data Viewer is a geospatially based Geographic Information System (GIS) application for displaying data contained in the roadway inventory database. The interactive map has five viewable data categories or ‘layers’. The five layers include: Average Daily Traffic (ADT); Continuous Counts; Short Counts; Bridges; and Grade Crossings throughout New York State.

Annual average daily traffic is the total volume for the year divided by 365 days. The traffic count year is from October 1st through September 30th. Very few locations in California are actually counted continuously. Traffic Counting is generally performed by electronic counting instruments moved from location throughout the State in a program of continuous traffic count sampling. The resulting counts are adjusted to an estimate of annual average daily traffic by compensating for seasonal influence, weekly variation and other variables which may be present. Annual ADT is necessary for presenting a statewide picture of traffic flow, evaluating traffic trends, computing accident rates. planning and designing highways and other purposes.Traffic Census Program Page

Google.com recorded an average monthly traffic of **** billion visits in Japan from September to November 2024, which made it the most visited website. It was followed by Yahoo.co.jp and Youtube.com.

Difference uses Google Analytics as the Baseline. Results based on Paired t-Test for Hypotheses Supported.

Host country of organization for 86 websites in study.

A collection of historic traffic count data and guidelines for how to collect new data for Massachusetts Department of Transportation (MassDOT) projects.

Website type for the 86 websites in study.

Introduction

Fitbit statistics: In today's health-conscious society, monitoring personal wellness metrics has become increasingly important. Fitbit, a leader in wearable technology, offers users detailed insights into their daily activities, sleep patterns, and heart health. On average, Fitbit users take between 10,000 to 18,000 steps per day, aligning with general fitness recommendations.

Sleep tracking data reveals that users typically achieve about 6.5 hours of sleep each night, accompanied by an average Sleep Score of 77. Regarding cardiovascular health, the average resting heart rate among Fitbit users is approximately 65 beats per minute, with variations influenced by factors such as age and gender. These statistics underscore Fitbit's role in providing users with actionable data to support their health and wellness goals.

Let's delve into the fascinating insights through Fitbit statistics and explore what they can tell us about the brand’s performance in 2025.

With more than 44,000 Portable Traffic Count (PTC) Stations located throughout North Carolina, Traffic Survey has adopted a collection schedule. Please see our website: https://www.ncdot.gov/projects/trafficsurvey/for further details. The data in this file was digitized referencing the available NCDOT Linear Referencing System (LRS) and is not the result of using GPS equipment in the field, nor latitude and longitude coordinates. The referencing provided is based on the 2015 Quarter 1 publication of the NCDOT Linear Referencing System (LRS). Some differences will be found when using different quarterly publications with this data set. The data provided is seasonally factored to an estimate of an annual average of daily traffic. The statistics provided are: CVRG_VLM_I: Traffic Survey's seven digit unique station identifier COUNTY: County NameROUTE: Numbered route identifier, or local name if not State maintainedLOCATION: Description of the Annual Average Daily Traffic station location AADT_2015: Estimated Annual Average Daily Traffic in vehicles per day for 2015AADT_2014: Estimated Annual Average Daily Traffic in vehicles per day for 2014AADT_2013: Estimated Annual Average Daily Traffic in vehicles per day for 2013 AADT_2012: Estimated Annual Average Daily Traffic in vehicles per day for 2012 AADT_2011: Estimated Annual Average Daily Traffic in vehicles per day for 2011 AADT_2010: Estimated Annual Average Daily Traffic in vehicles per day for 2010 AADT_2009: Estimated Annual Average Daily Traffic in vehicles per day for 2009 AADT_2008: Estimated Annual Average Daily Traffic in vehicles per day for 2008 AADT_2007: Estimated Annual Average Daily Traffic in vehicles per day for 2007 AADT_2006: Estimated Annual Average Daily Traffic in vehicles per day for 2006 AADT_2005: Estimated Annual Average Daily Traffic in vehicles per day for 2005 AADT_2004: Estimated Annual Average Daily Traffic in vehicles per day for 2004 AADT_2003: Estimated Annual Average Daily Traffic in vehicles per day for 2003 AADT_2002: Estimated Annual Average Daily Traffic in vehicles per day for 2002 Note: A value of zero in the AADT field indicates no available AADT data for that year. Please note the following: Not ALL roads have PTC stations located on them. With the exception of Interstate, NC and US routes, NCDOT County Maps refer to roads using a four digit Secondary Road Number, not a road’s local name. If additional information is needed, or an issue with the data is identified, please contact the Traffic Survey Group at 919 814-5116. Disclaimer related to the spatial accuracy of this file: Data in this file was digitized referencing the available NCDOT GIS Data Layer, LRS Arcs Shapefile Format from Quarter 1 release and is not the result of using GPS equipment in the field.North Carolina Department of Transportation shall not be held liable for any errors in this data. This includes errors of omission, commission, errors concerning the content of data, and relative positional accuracy of the data. This data cannot be construed to be a legal document.

AADT represents current (most recent) Annual Average Daily Traffic on sampled road systems. This information is displayed using the Traffic Count Locations Active feature class as of the annual HPMS freeze in January. Historical AADT is found in another table. Please note that updates to this dataset are on an annual basis, therefore the data may not match ground conditions or may not be available for new roadways. Resource Contact: Christy Prentice, Traffic Forecasting & Analysis (TFA), http://www.dot.state.mn.us/tda/contacts.html#TFA

Check other metadata records in this package for more information on Annual Average Daily Traffic Locations Information.


Link to ESRI Feature Service:

Annual Average Daily Traffic Locations in Minnesota: Annual Average Daily Traffic Locations

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.