Explore our detailed website traffic dataset featuring key metrics like page views, session duration, bounce rate, traffic source, and conversion rates.

The dataset contains information about web requests to a single website. It's a time series dataset, which means it tracks data over time, making it great for machine learning analysis.

In the second quarter of 2025, mobile devices (excluding tablets) accounted for 62.54 percent of global website traffic. Since consistently maintaining a share of around 50 percent beginning in 2017, mobile usage surpassed this threshold in 2020 and has demonstrated steady growth in its dominance of global web access. 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.

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Global network traffic analytics Industry Overview

Technavio’s analysts have identified the increasing use of network traffic analytics solutions to be one of major factors driving market growth. With the rapidly changing IT infrastructure, security hackers can steal valuable information through various modes. With the increasing dependence on web applications and websites for day-to-day activities and financial transactions, the instances of theft have increased globally. Also, the emergence of social networking websites has aided the malicious attackers to extract valuable information from vulnerable users. The increasing consumer dependence on web applications and websites for day-to-day activities and financial transactions are further increasing the risks of theft. This encourages the organizations to adopt network traffic analytics solutions.

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See the complete table of contents and list of exhibits, as well as selected illustrations and example pages from this report.

Companies covered

The network traffic analytics market is fairly concentrated due to the presence of few established companies offering innovative and differentiated software and services. By offering a complete analysis of the competitiveness of the players in the network monitoring tools market offering varied software and services, this network traffic analytics industry analysis report will aid clients identify new growth opportunities and design new growth strategies.

The report offers a complete analysis of a number of companies including:

Allot
Cisco Systems
IBM
Juniper Networks
Microsoft
Symantec

Network traffic analytics market growth based on geographic regions

Americas
APAC
EMEA

With a complete study of the growth opportunities for the companies across regions such as the Americas, APAC, and EMEA, our industry research analysts have estimated that countries in the Americas will contribute significantly to the growth of the network monitoring tools market throughout the predicted period.

Network traffic analytics market growth based on end-user

Telecom
BFSI
Healthcare
Media and entertainment

According to our market research experts, the telecom end-user industry will be the major end-user of the network monitoring tools market throughout the forecast period. Factors such as increasing use of network traffic analytics solutions and increasing use of mobile devices at workplaces will contribute to the growth of the market shares of the telecom industry in the network traffic analytics market.

Key highlights of the global network traffic analytics market for the forecast years 2018-2022:

CAGR of the market during the forecast period 2018-2022
Detailed information on factors that will accelerate the growth of the network traffic analytics market during the next five years
Precise estimation of the global network traffic analytics market size and its contribution to the parent market
Accurate predictions on upcoming trends and changes in consumer behavior
Growth of the network traffic analytics industry across various geographies such as the Americas, APAC, and EMEA
A thorough analysis of the market’s competitive landscape and detailed information on several vendors
Comprehensive information about factors that will challenge the growth of network traffic analytics companies

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This market research report analyzes the market outlook and provides a list of key trends, drivers, and challenges that are anticipated to impact the global network traffic analytics market and its stakeholders over the forecast years.

The global network traffic analytics market analysts at Technavio have also considered how the performance of other related markets in the vertical will impact the size of this market till 2022. Some of the markets most likely to influence the growth of the network traffic analytics market over the coming years are the Global Network as a Service Market and the Global Data Analytics Outsourcing Market.

Technavio’s collection of market research reports offer insights into the growth of markets across various industries. Additionally, we also provide customized reports based on the specific requirement of our clients.

The dataset provides 12 months (August 2016 to August 2017) of obfuscated Google Analytics 360 data from the Google Merchandise Store , a real ecommerce store that sells Google-branded merchandise, in BigQuery. It’s a great way analyze business data and learn the benefits of using BigQuery to analyze Analytics 360 data Learn more about the data The data includes The data is typical of what an ecommerce website would see and includes the following information:Traffic source data: information about where website visitors originate, including data about organic traffic, paid search traffic, and display trafficContent data: information about the behavior of users on the site, such as URLs of pages that visitors look at, how they interact with content, etc. Transactional data: information about the transactions on the Google Merchandise Store website.Limitations: All users have view access to the dataset. This means you can query the dataset and generate reports but you cannot complete administrative tasks. Data for some fields is obfuscated such as fullVisitorId, or removed such as clientId, adWordsClickInfo and geoNetwork. “Not available in demo dataset” will be returned for STRING values and “null” will be returned for INTEGER values when querying the fields containing no data.This public dataset is hosted in Google BigQuery and is included in BigQuery's 1TB/mo of free tier processing. This means that each user receives 1TB of free BigQuery processing every month, which can be used to run queries on this public dataset. Watch this short video to learn how to get started quickly using BigQuery to access public datasets. What is BigQuery

Unlock the Power of Behavioural Data with GDPR-Compliant Clickstream Insights.

Swash clickstream data offers a comprehensive and GDPR-compliant dataset sourced from users worldwide, encompassing both desktop and mobile browsing behaviour. Here's an in-depth look at what sets us apart and how our data can benefit your organisation.

User-Centric Approach: Unlike traditional data collection methods, we take a user-centric approach by rewarding users for the data they willingly provide. This unique methodology ensures transparent data collection practices, encourages user participation, and establishes trust between data providers and consumers.

Wide Coverage and Varied Categories: Our clickstream data covers diverse categories, including search, shopping, and URL visits. Whether you are interested in understanding user preferences in e-commerce, analysing search behaviour across different industries, or tracking website visits, our data provides a rich and multi-dimensional view of user activities.

GDPR Compliance and Privacy: We prioritise data privacy and strictly adhere to GDPR guidelines. Our data collection methods are fully compliant, ensuring the protection of user identities and personal information. You can confidently leverage our clickstream data without compromising privacy or facing regulatory challenges.

Market Intelligence and Consumer Behaviuor: Gain deep insights into market intelligence and consumer behaviour using our clickstream data. Understand trends, preferences, and user behaviour patterns by analysing the comprehensive user-level, time-stamped raw or processed data feed. Uncover valuable information about user journeys, search funnels, and paths to purchase to enhance your marketing strategies and drive business growth.

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Data Quality and Credibility: We take data quality seriously. Our data sourcing practices are designed to ensure responsible and reliable data collection. We implement rigorous data cleaning, validation, and verification processes, guaranteeing the accuracy and reliability of our clickstream data. You can confidently rely on our data to drive your decision-making processes.

Context

This file contains 5 years of daily time series data for several measures of traffic on a statistical forecasting teaching notes website whose alias is statforecasting.com. The variables have complex seasonality that is keyed to the day of the week and to the academic calendar. The patterns you you see here are similar in principle to what you would see in other daily data with day-of-week and time-of-year effects. Some good exercises are to develop a 1-day-ahead forecasting model, a 7-day ahead forecasting model, and an entire-next-week forecasting model (i.e., next 7 days) for unique visitors.

Content

The variables are daily counts of page loads, unique visitors, first-time visitors, and returning visitors to an academic teaching notes website. There are 2167 rows of data spanning the date range from September 14, 2014, to August 19, 2020. A visit is defined as a stream of hits on one or more pages on the site on a given day by the same user, as identified by IP address. Multiple individuals with a shared IP address (e.g., in a computer lab) are considered as a single user, so real users may be undercounted to some extent. A visit is classified as "unique" if a hit from the same IP address has not come within the last 6 hours. Returning visitors are identified by cookies if those are accepted. All others are classified as first-time visitors, so the count of unique visitors is the sum of the counts of returning and first-time visitors by definition. The data was collected through a traffic monitoring service known as StatCounter.

Inspiration

This file and a number of other sample datasets can also be found on the website of RegressIt, a free Excel add-in for linear and logistic regression which I originally developed for use in the course whose website generated the traffic data given here. If you use Excel to some extent as well as Python or R, you might want to try it out on this dataset.

In March 2024, close to 4.4 billion unique global visitors had visited Wikipedia.org, slightly down from 4.4 billion visitors since August of the same year. Wikipedia is a free online encyclopedia with articles generated by volunteers worldwide. The platform is hosted by the Wikimedia Foundation.

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.

Echo’s Catchment Area dataset reveals where your customers live and work, helping you define store trade areas and uncover hidden market potential.

Built from non-PII, real-world mobility data, this EU-focused dataset maps consumer origins around a POI, enabling smarter decisions in expansion, competition analysis, and localized marketing.

Key data points include: - Customer home/work location aggregation - POI-based catchment area boundaries - Competitor and market overlap visibility - Quarterly updates, normalized & clean - GDPR-compliant, non-PII dataset

Ideal for retailers, consultants, and CRE professionals optimizing store networks and market strategies.

IL Coverage of the Gateway camera snapshots. The Gateway provides camera snapshot images throughout its coverage area in the form of camera icons on its maps and images in its camera report. With a free subscription, users can also access the Gateway ftp server which contains the most up to date versions of the images available.ImgPath - this is a link to the travelmidwest.com/lmiga/showCamera.jsp popup window that allows the user to select another direction, if availableCameraLocation - a text description of where the camera is locatedCameraDirection - the direction the camera is facing (NONE, N, E, S, W, NE, NW, SE, or SW)y - latitude in decimal degreesx - longitude in decimal degreesSnapShot - public URL of camera's image file that is suitable for placement in a tag, for instanceWarningAge - "true" if the camera is more than 10 minutes old, false otherwiseTooOld - "true" if more than 30 minutes old, "false" otherwiseAgeInMinutes - integer age of camera image in minutes

Context

• Time Series: Time series is a set of observations recorded over regular interval of time, Time series can be beneficial in many fields like stock market prediction, weather forecasting. - Accounts for the fact that data points taken over time may have an internal structure (such as auto correlation, trend or seasonal variation) that should be accounted for.

• Web traffic: Amount of data sent and received by visitors to a website. - Sites monitor the incoming and outgoing traffic to see which parts or pages of their site are popular and if there are any apparent trends, such as one specific page being viewed mostly by people in a particular country

Content

Contains Page Views for 60k Wikipedia articles in 8 different languages taken on a daily basis for 2 years.

https://i.ibb.co/h1JCgpY/DSLC.png" alt="DSLC">

A Data Science Life Cycle can be used to create a project. Forecasting can be done for any interval provided sufficient dataset is available. Refer the Github link in the tasks to view the forecast done using ARIMA and Prophet. Further feel free to contribute. Several other models can be used including a neural network to improve the results by many folds.

Acknowledgements

Credits :
1. Wikipedia 2. Google

In August 2025, Google.com was the most visited website worldwide, with an average of 98.2 billion monthly visits. The platform has maintained its leading position since June 2010, when it surpassed Yahoo to take first place. YouTube ranked second during the same period, recording over 48 billion monthly visits. The internet leaders: search, social, and e-commerce Social networks, search engines, and e-commerce websites shape the online experience as we know it. While Google leads the global online search market by far, YouTube and Facebook have become the world’s most popular websites for user generated content, solidifying Alphabet’s and Meta’s leadership over the online landscape. Meanwhile, websites such as Amazon and eBay generate millions in profits from the sale and distribution of goods, making the e-market sector an integral part of the global retail scene. What is next for online content? Powering social media and websites like Reddit and Wikipedia, user-generated content keeps moving the internet’s engines. However, the rise of generative artificial intelligence will bring significant changes to how online content is produced and handled. ChatGPT is already transforming how online search is performed, and news of Google's 2024 deal for licensing Reddit content to train large language models (LLMs) signal that the internet is likely to go through a new revolution. While AI's impact on the online market might bring both opportunities and challenges, effective content management will remain crucial for profitability on the web.

Waze maps provide information about specific routes to assist motorists in avoiding traffic jams. Waze provides information about traffic jams and events that affect road conditions, either from drivers using Waze or from external sources. MTO has partnered with Waze through their Connected Citizen Program to publish Waze reported information on the Ontario 511 website. MTO iCorridor leverages such traffic jams to generate summaries of delay, duration, speed, and length by weekly, monthly, time of day, and day of week. The objective is to determine the pattern of congestion on all provincial highway corridors. The delay analysis was comprised of the followings:Estimation of corridor delay;Estimation of duration and length of congestion; andIdentification of true peak period."It must be noted that a “no congestion scenario” does not necessarily imply that there is no traffic on a specific road. Even when congestion is reduced to zero there may still be vehicles driving on the road. Waze creates “jam lines” that indicate continuous portions of streets where speed has slowed. Waze data provides the exact geographic location, length, speed, and time delay for these jam lines compared to the time it would normally take to transverse the jam line by car. A categorization for the severity of the jam is also provided.the jam data is composed of jam lines (which can change over time) measured at different time intervals. Given the crowd-sourced nature of the data, it cannot be determined if fluctuations in jam line activity are due to actual changes in traffic conditions or due to fluctuations in the number of active Wazers. Evidence from on-the-ground measures supports the notion that changes in jam activity are generally due to actual changes in traffic conditions"ElementValueDescriptionpubDateTimePublication date.linqmap:typeStringTRAFFIC_JAM.georss:lineList of longitude and latitude coordinatesTraffic jam line string (supplied when available).linqmap:speedFloatCurrent average speed on jammed segments in meter/second.linqmap:lengthIntegerJam length in meters.linqmap:delayIntegerDelay of jam compared to free flow speed, in seconds (in case of block, 1).linqmap:streetStringStreet name (as is written in database, no canonical form (supplied when available).linqmap:cityStringCity and state name [City, State] in case both are available, [State] if not associated with a city (supplied when available).linqmap:countryStringAvailable on EU (world) server (see two letters codes in https://en.wikipedia.org/wiki/ISO-31661).linqmap:roadTypeIntegerRoad type (see road types table in the appendix).linqmap:startNodeStringNearest Junction/street/city to jam start (supplied when available).linqmap:endNodeStringNearest Junction/street/city to jam end (supplied when available).linqmap:level0-5Traffic congestion level (0 = free flow 5 = blocked).linqmap:uuidStringUnique jam identifier.linqmap:turnLineCoordinatesA set of coordinates of a turn only when the jam is in a turn (supplied when available).linqmap:turnTypeStringWhat kind of turn it is: left, right, exit R or L, continue straight, or NONE (no info) (supplied when available).linqmap:blockingAlertUuidStringIf the jam is connected to a block (see alerts).ElementValueDescriptionpubDateTimePublication date.georss:pointCoordinatesLocation per report (Lat long).linqmap:uuidStringUnique system ID.linqmap:magvarInteger (0359)Event direction (Driver heading at report time. 0 degrees at North, according to the driver's device).linqmap:typeSee alert type tableEvent type.linqmap:subtypeSee alert subtypes tableEvent subtype depends on parameter.linqmap:reportDescriptionStringReport description (supplied when available).linqmap:streetStringStreet name (as is written in database, no canonical form, may be null).linqmap:cityStringCity and state name [City, State] in case both are available, [State] if not associated with a city (supplied when available).linqmap:countryStringSee two letters codes in .linqmap:roadTypeIntegerRoad type (see road types table in the appendix).linqmap:reportRatingIntegerUser rank between 16 (6 = high ranked user).linqmap:jamUuidStringIf the alert is connected to a jam jam ID.linqmap:Reliability (new)0-10How reliable is the report, 10 being most reliable. Based on reporter level and user respon-reference from (https://ops.fhwa.dot.gov/publications/fhwahop18084/ch2.htm)

The complete Social Media Networks websites ranking list: Click here for free access to the top Social Media Networks websites in the world, ranked by traffic and engagement

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

This is intended as a basis for conversion between image annotation formats only. For the complete dataset (including the actual images these files annotate), refer to Mapillary's website.

This Mapillary Traffic Sign Dataset is provided by Mapillary AB under the Creative Commons Attribution NonCommercial Share Alike (CC BY-NC-SA) license.

Picture from: Ertler C., Mislej J., Ollmann T., Porzi L., Neuhold G., Kuang Y. (2020) The Mapillary Traffic Sign Dataset for Detection and Classification on a Global Scale. In: Vedaldi A., Bischof H., Brox T., Frahm JM. (eds) Computer Vision – ECCV 2020. ECCV 2020. Lecture Notes in Computer Science, vol 12368. Springer, Cham. https://doi.org/10.1007/978-3-030-58592-1_5

Dataset

This dataset was created by Merve Afranur ARTAR

Contents

Locations of signalised intersections and signalised pedestrian crossings in Hamilton City

Column_InfoSite_Number, int : SCATS ID - Unique identifierRoad_1, varchar : First road descriptorRoad_2, varchar : Second road descriptor - 'Ped xing' means it is a mid-block pedestrian signalRoad_3, varchar : Third road descriptor if relevantSite_Type, varchar : Pedestrian crossing or intersection, and whether on a state highway or council roadIs_CBD, int : Site is within the CBD boundaryEasting, decimal : Eastward-measured distance in NZTM projectionNorthing, decimal : Northward-measured distance in NZTM projectionLatitude, decimal : North-south geographic coordinatesLongitude, decimal : East-west geographic coordinates

Relationship

This table is referenced by Traffic_Signal_Detector

Analytics

For convenience Hamilton City Council has also built a Quick Analytics Dashboard over this dataset that you can access here.

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.'

Between April 2022 and January 2024, global traffic to Pornhub.com originated mainly from mobile devices. In January 2024, Pornhub saw over 11.4 billion mobile visits from global users. In the same month, the desktop traffic to the popular pornographic website was less than 500 million visits. In October 2023, approximately 97 percent of the traffic to Pornhub.com came from mobile devices.