By 2030, the average mobile data connection was forecast to generate almost ** gigabytes of traffic per month in the Middle East and North Africa (MENA), increasing from *** gigabytes in 2023. The monthly mobile data traffic per subscriber has experienced a considerable growth from *** gigabytes in 2018.

In March 2024, the video platform YouTube reported around 32.5 billion visits from global users. Meta-owned Facebook.com reported around 16.1 billion visits from global users, as Instagram.com and Twitter.com followed, each with 7 billion and 6.1 billion visits from users worldwide during the examined month. Wikipedia.org, which hosts users-generated encyclopedic entries, recorded around 4.4 billion visits, while news aggregator and community platform Reddit.com saw approximately 2.2 billion visits during the examined period.

google.com is ranked #1 in US with 96.55B Traffic. Categories: Online Services. Learn more about website traffic, market share, and more!

reddit.com is ranked #3 in US with 4.35B Traffic. Categories: Online Services. Learn more about website traffic, market share, and more!

fansly.com is ranked #845 in US with 35.46M Traffic. Categories: Online Services. Learn more about website traffic, market share, and more!

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?

The dataset consists of more than 2300 trajectories of pedestrians and 1000 trajectories of cyclists recorded by a research vehicle of the University of Applied Sciences Aschaffenburg (Kooperative Automatisierte Verkehrssysteme) in urban traffic. In addition to the actual trajectory, the data set contains 3D poses, a representation of the body posture in three-dimensional space, and semantic maps describing the surrounding of the respective vulnerable road user (VRU).

The trajectories were sampled using a sliding window approach and split into a training, validation, and test dataset. Each sample contains the trajectory, 3D poses and semantic maps of the past second, as well as the sought future trajectory and semantic maps for the future 2.52 s. In addition, each pattern is assigned to a current type of motion. The motion types were annotated manually. For a more detailed description of the dataset, please refer to the following publication:

Viktor Kress, Fabian Jeske, Stefan Zernetsch, Konrad Doll, Bernhard Sick: Pose and Semantic Map Based Probabilistic Forecast of Vulnerable Road Users' Trajectories. 2021, arXiv: 2106.02598, https://arxiv.org/abs/2106.02598

We provide files for the training/validation dataset and the test dataset for pedestrians and cyclists, respectively. To read the provided data, unzip the files first. Each file contains a zarr directory. Zarr is a format for the storage of chunked, compressed, N-dimensional arrays (https://zarr.readthedocs.io). To read the data:

import zarr

data = zarr.open(

Each zarr directory contains the following keys:

Key:
  pre_trajectories_and_poses: input trajectories of 13 body joint positions, format: [sample, timestep, x,y,z coordinates (first 13 coordinates: x, 14- 26: y, 27:39: z)]
  pre_smaps: input semantic maps, format: [sample, timestep (-0.96s, -0.48a, 0.00s)], codes: static obstacles: 0, dynamic obstacles: 1, person: 2, sidewalk: 3, road: 4, walkable vegetation: 5, unknown obstacle: 6, unknown free space: 7, unkown: 8
  pos_trajectories: ground truth future trajectories of the head, format: [sample, x,y coordinates (first 63 coordinates: x, 64- 126: y for the timesteps +0.04s, +0.08s, ..., +2.52s))]
  pos_smaps: future semantic maps, format: [sample, timestep (+0.44s, +0.96s, +1.48s, +2.00s, 2.52s)]
  fold: affiliation to training/validation dataset, format: [sample], codes: test set: 0, validation set: 1, training set: 2
  augmentation: affiliation to the augmentation loop (0-2), format: [sample]
  move, start, stop, wait, tl, tr: current motion type as boolean arrays, format: [sample]

This work was supported by “Zentrum Digitalisierung.Bayern”. In addition, the work is backed by the project DeCoInt2 , supported by the German Research Foundation (DFG) within the priority program SPP 1835: “Kooperativ interagierende Automobile”, grant numbers DO 1186/1-2 and SI 674/11-2.

Mobile internet users aged up to 25 years in Russia consumed approximately ** times more data in the second quarter of 2021 than in the first three months of 2016. That was the highest traffic growth rate compared to other age groups. On average across the country, the data traffic growth on mobile devices reached *** percent over the observed period.

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.

DDOT is committed to making District streets safer for all roadway users while providing multimodal mobility and access for residents, visitors, and commercial users. A combination of traffic control devices and traffic calming measures can help meet both goals. DDOT has a full portfolio of proactive, data-based safety efforts, which can be reviewed at https://visionzero.dc.gov/pages/engineering. In addition, our Traffic Safety Input (TSI) program provides a mechanism for DDOT to hear from residents on roadway segments and intersections where users have safety concerns.

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

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.

chatgpt.com is ranked #12 in US with 4.61B Traffic. Categories: AI. Learn more about website traffic, market share, and more!

You can also access an API version of this dataset.

TMS

(traffic monitoring system) daily-updated traffic counts API

Important note: due to the size of this dataset, you won't be able to open it fully in Excel. Use notepad / R / any software package which can open more than a million rows.

Data reuse caveats: as per license.

Data quality

statement: please read the accompanying user manual, explaining:

how

 this data is collected identification 

 of count stations traffic 

 monitoring technology monitoring 

 hierarchy and conventions typical 

 survey specification data 

 calculation TMS 

 operation. 

Traffic

monitoring for state highways: user manual

[PDF 465 KB]

The data is at daily granularity. However, the actual update

frequency of the data depends on the contract the site falls within. For telemetry

sites it's once a week on a Wednesday. Some regional sites are fortnightly, and

some monthly or quarterly. Some are only 4 weeks a year, with timing depending

on contractors’ programme of work.

Data quality caveats: you must use this data in

conjunction with the user manual and the following caveats.

The

 road sensors used in data collection are subject to both technical errors and 

 environmental interference.Data 

 is compiled from a variety of sources. Accuracy may vary and the data 

 should only be used as a guide.As 

 not all road sections are monitored, a direct calculation of Vehicle 

 Kilometres Travelled (VKT) for a region is not possible.Data 

 is sourced from Waka Kotahi New Zealand Transport Agency TMS data.For 

 sites that use dual loops classification is by length. Vehicles with a length of less than 5.5m are 

 classed as light vehicles. Vehicles over 11m long are classed as heavy 

 vehicles. Vehicles between 5.5 and 11m are split 50:50 into light and 

 heavy.In September 2022, the National Telemetry contract was handed to a new contractor. During the handover process, due to some missing documents and aged technology, 40 of the 96 national telemetry traffic count sites went offline. Current contractor has continued to upload data from all active sites and have gradually worked to bring most offline sites back online. Please note and account for possible gaps in data from National Telemetry Sites. 

The NZTA Vehicle

Classification Relationships diagram below shows the length classification (typically dual loops) and axle classification (typically pneumatic tube counts),

and how these map to the Monetised benefits and costs manual, table A37,

page 254.

Monetised benefits and costs manual [PDF 9 MB]

For the full TMS

classification schema see Appendix A of the traffic counting manual vehicle

classification scheme (NZTA 2011), below.

Traffic monitoring for state highways: user manual [PDF 465 KB]

State highway traffic monitoring (map)

State highway traffic monitoring sites

Snapshot img { margin: 10px !important; }

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.

Want a bigger picture? Try a FREE sample of this report now!

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 suc

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

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...

whatnot.com is ranked #1322 in US with 9.1M Traffic. Categories: Retail. Learn more about website traffic, market share, and more!

Historical Annual Average Daily Traffic Volume datasets (2001 -> 2019) provides annual average daily traffic (AADT) volume data for all vehicles and heavy vehicles in each direction on each road segment on the Victoria declared road network. The data is either estimated through data processes or actual volumes from data collection.

User guide for the ArcGIS Online Statewide Traffic Count AppThe guide covers essential aspects, including:Map Functions Overview: This section details the basic interactive functions of the map, including zooming, panning, and identifying features. It will explain how to navigate the map interface effectively, find specific locations, and understand the map's overall layout and controls. Turn Layers On and Off: This portion of the guide will teach users how to control the visibility of different data layers within the map. Users will learn how to toggle layers on and off to customize the map display, focusing on specific traffic count data or related information. This allows for a more focused analysis of the data. Attribute Table and Export Data: This section explains how to access and utilize the attribute table associated with the traffic count data. Users will learn how to view detailed information about each traffic count location, including specific count values, dates, and other relevant attributes. Furthermore, this section will instruct how to export the attribute table data into formats like CSV or Excel for further analysis outside of the online application. Downloading Data: This portion of the guide will explain how to download the traffic count data. It will explain what file types are available for download, and any restrictions that are placed on the data.