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.

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

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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.'

Be ready for a cookieless internet while capturing anonymous website traffic data!

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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.

Between July and September 2022, BYJU's emerged as the top Ed Tech platform for K12 and test preparation In India. It recorded approximately *** million website visits. Following closely behind was Toppr.com, with around *** million visits during the same period.

This is a dataset of Tor cell file extracted from browsing simulation using Tor Browser. The simulations cover both desktop and mobile webpages. The data collection process was using WFP-Collector tool (https://github.com/irsyadpage/WFP-Collector). All the neccessary configuration to perform the simulation as detailed in the tool repository.The webpage URL is selected by using the first 100 website based on: https://dataforseo.com/free-seo-stats/top-1000-websites.Each webpage URL is visited 90 times for each deskop and mobile browsing mode.

The global website traffic analysis tool market is experiencing robust growth, driven by the increasing reliance on digital marketing and the need for businesses of all sizes to understand their online audience. The market, estimated at $15 billion in 2025, is projected to grow at a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033, reaching approximately $45 billion by 2033. This expansion is fueled by several key factors. The rising adoption of cloud-based solutions provides scalability and cost-effectiveness for businesses, particularly SMEs seeking affordable analytics. Moreover, the evolution of sophisticated analytics features, including advanced user behavior tracking and predictive analytics, enhances the value proposition for both SMEs and large enterprises. The market is segmented by application (SMEs and large enterprises) and by type (cloud-based and web-based), with cloud-based solutions dominating due to their accessibility and flexibility. Competitive pressures among numerous vendors, including established players like Google Analytics, Semrush, and Ahrefs, as well as emerging niche players, drive innovation and affordability, benefiting users. Geographic distribution shows strong growth across North America and Europe, with Asia-Pacific emerging as a high-growth region. However, factors such as data privacy concerns and the increasing complexity of website analytics can act as potential restraints. Despite these challenges, the continued expansion of e-commerce and digital marketing strategies across various industries will solidify the demand for robust website traffic analysis tools. The market is expected to witness further consolidation through mergers and acquisitions, with leading players investing heavily in research and development to enhance their offerings. The increasing need for real-time data analysis and integration with other marketing automation platforms will further shape market evolution. The emergence of AI-powered analytics, providing predictive insights and automated reporting, is transforming the industry and will continue to drive market expansion in the coming years. This makes this market an attractive landscape for investors and technology providers looking for strong future growth.

In November 2024, Google.com was the most popular website worldwide with 136 billion average monthly visits. The online platform has held the top spot as the most popular website since June 2010, when it pulled ahead of Yahoo into first place. Second-ranked YouTube generated more than 72.8 billion monthly visits in the measured period. 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.

In November 2021, mobile devices accounted for nearly ** percent of the web traffic to Google.com in Kenya. The website had the highest number of total visits in the country. Among the leading websites, most of them had a higher share of traffic from mobile. Youtube.com was an exception, with only ********* of its traffic originating from mobile devices.

In 2022, notion.so held the leading position as the most popular startup website in the United States and Europe based on global traffic. The workspace platform, which launched in 2013, saw over ***** million monthly website visits on average in the examined year. The U.S.-based visual tool Miro.com came in second, generating almost ** million average monthly visits from users worldwide. Additionally, the website of the startup accelerator Y Combinator ranked third with approximately ** million monthly visits.

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

Introduction

edX Statistics: In this digital era, online learning has developed into a powerful tool for education and career enhancement. Among many platforms in this regard, edX is a key provider of Massive Open Online Courses (MOOCs). Founded by Harvard University and MIT, edX grew in 2012 and has continued to do so by offering thousands of courses in various fields. In 2024, edX has achieved new milestones in terms of its users, revenue, partnerships, and courses.

This article shall look at the latest edX statistics concerning its influence on learners and the future of education.

The global website analytics tool market is experiencing robust growth, driven by the increasing need for businesses of all sizes to understand their online audience and optimize their digital strategies. The market, valued at approximately $15 billion in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 12% from 2025 to 2033. This substantial growth is fueled by several key factors. The rising adoption of cloud-based solutions offers scalability, cost-effectiveness, and accessibility for businesses, regardless of their technical expertise. Furthermore, the expanding use of mobile devices and the proliferation of data are creating a surge in demand for sophisticated analytics tools capable of providing real-time insights into user behavior, campaign performance, and website effectiveness. The market is segmented by deployment (cloud-based and on-premises) and user type (SMEs and large enterprises), with the cloud-based segment dominating due to its inherent advantages. Competitive pressures are intensifying, with established players like Google Analytics and Adobe Analytics facing competition from agile startups offering innovative features and pricing models. While data privacy concerns and the complexity of implementing and interpreting analytics data pose challenges, the overall market trajectory remains overwhelmingly positive, underpinned by continued digital transformation across industries and a growing awareness of the importance of data-driven decision-making. The regional distribution of the market reflects the global landscape of digital adoption, with North America and Europe currently holding significant market shares. However, the Asia-Pacific region is expected to witness the fastest growth in the coming years, driven by rapid economic development and increasing internet penetration. This presents substantial opportunities for both established players and new entrants in the market. The future will likely see further integration of website analytics with other marketing technologies, the rise of AI-powered analytics capabilities offering predictive insights, and a greater emphasis on data security and compliance. This dynamic environment necessitates continuous innovation and adaptation for businesses operating within the website analytics tool market.

In the month of January 2024, the beauty and personal care retailer Nykaa had about **** million website visits. In comparison, the month of December in 2023 clocked over ten million monthly website visits.

In today's digital landscape, the Website Traffic Analysis Tool market has emerged as an essential component for businesses aiming to enhance their online presence and optimize their digital strategies. These tools empower organizations to monitor their website performance, analyze visitor behavior, and derive actio

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.

The website speed test market has witnessed significant growth, reaching a market size of XXX million in 2025. This growth is primarily attributed to the increasing demand for faster and more reliable internet speeds, driven by the widespread adoption of streaming video, online gaming, and cloud-based applications. The CAGR of the market is projected to remain strong over the forecast period from 2023 to 2033, reaching a value of XXX million by 2033. Key market trends include the growing adoption of 5G networks and the increasing popularity of fiber optic internet, both of which offer significantly faster speeds compared to traditional copper-based connections. In terms of segmentation, the market for website speed test can be divided into two main types: cable internet and fiber optic internet. Cable internet is currently the most widely used type of broadband internet connection, but fiber optic internet is rapidly gaining popularity due to its superior speed and reliability. Other types of broadband internet connections include fixed wireless internet, satellite internet, and DSL internet. The market can also be segmented based on its application, with individuals and businesses being the two primary user groups. Businesses typically require faster and more reliable internet speeds than individuals, and are therefore more likely to invest in higher-end solutions such as fiber optic internet. Major companies in the website speed test market include Fusion Connect, Bandwidth Place, Ookla, Netflix, and Measurement Lab, among others.

The global website speed and performance test tool market size was valued at USD 1.84 billion in 2022 and is projected to reach USD 5.52 billion by 2033, exhibiting a CAGR of 12.0% during the forecast period. The escalating demand for website performance optimization services, the surge in website traffic, and the proliferation of mobile devices drive market growth. Moreover, the growing adoption of cloud-based solutions and the increasing preference for online shopping fuel market expansion. Key players in the website speed and performance test tool market include Pingdom, Yellow Lab Tools, Alerta, Sematext, Domsignal, Dareboost, New Relic, Google PageSpeed Insights, KeyCDN Website Speed Test, Yslow, Uptrends, GTmetrix, Site24x7, Datadog, Catchpoint WebPageTest, Dotcom-Monitor, Lighthouse, WebPagetest, and Load Impact. These companies are focusing on offering advanced features and enhancing the capabilities of their tools to gain a competitive edge. The market is fragmented, with several players offering a wide range of solutions catering to different customer needs and industries.

Introduction

Tor Statistics: Tor is a free network that helps people stay anonymous online. It works using open-source software and depends on more than 7,000 volunteer-run relays across the world. Tor, known as “The Onion Router,†is a free tool that helps protect your identity and activity online.

It helps in hiding location and internet use by passing your data through many different servers, called relays, run by volunteers around the globe. Tor is built on open-source software and is widely used by journalists, activists, and everyday users who value their privacy. It was developed by the Tor Project and initially released on September 20, 2002.

This article includes several statistical analyses from different sources covering the overall market trend, features, types, user bases, demographics, countries, traffic shares, and many other factors.

Introduction

Jasper AI Statistics:Â Jasper AI has emerged as a leading generative AI platform, significantly transforming content creation and marketing workflows. By 2024, the company reported over 100,000 active users and more than 850 enterprise clients. Its revenue reached approximately USD 142.9 million, reflecting substantial growth from previous years.

To enhance productivity, Jasper AI introduced over 80 AI applications and launched Marketing Workflow Automation tools. With a total funding of USD 131 million and a valuation of USD 1.5 billion as of early 2024, Jasper AI continues to be a pivotal tool for businesses aiming to optimize their content strategies and achieve better marketing outcomes.

On this account, the article looks at some key Jasper AI statistics and trends for 2024, depicting the evolution and influence.