As of February 2025, video apps accounted for around 76 percent of global mobile data usage every month. Second-ranked social networking accounted for eight percent of global mobile data volume. The two categories, though, can easily overlap, as users can watch videos via video applications, as well as on social networking applications. Most popular social media platforms with video content Facebook, YouTube, and Instagram were among the most popular social networks in the world, as of October 2021. Each of these platforms allow to post, share, and watch video content on a mobile device. One of the fastest growing global brands, Tiktok, is also a social media platform where users can share video content. In September 2021, the platform reached 1 billion monthly active users. Leading types of mobile video content in the U.S. The United States was the third country in the world based on the number of smartphone users as of May 2021, with around 270 million users. Therefore, mobile content usage in the country was one of the highest in the world, and a big part of it was video content. As of the third quarter of 2021, more than 80 percent of survey respondents in the United States reported watching YouTube on their mobile devices. Social media videos were the second most popular type of content for mobile audiences, with almost six in 10 respondents watching videos on social media platforms like TikTok and Twitter.

Traffic analytics, rankings, and competitive metrics for foundation.app as of June 2025

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.

The real-time traffic data market, currently valued at $36.9 billion in 2025, is experiencing robust growth, projected to expand at a Compound Annual Growth Rate (CAGR) of 12.5% from 2025 to 2033. This significant expansion is fueled by several key factors. The increasing adoption of connected vehicles and the rise of smart city initiatives are driving demand for accurate and timely traffic information. Furthermore, the logistics and transportation sectors heavily rely on real-time data for efficient route optimization, delivery scheduling, and fleet management, contributing substantially to market growth. Government agencies are also significant consumers, leveraging this data for urban planning, traffic management, and emergency response systems. The market is segmented by application (Government, Logistics, Infrastructure Construction, Automobile, and Other) and data type (Traffic Data, Mobility Data, Car Traffic Data), with the Government and Logistics segments exhibiting particularly strong growth potential due to their increasing reliance on data-driven decision-making. Technological advancements such as improved sensor technologies and the development of sophisticated analytical tools are further enhancing the capabilities and accuracy of real-time traffic data solutions. Competitive dynamics within the real-time traffic data market are characterized by a mix of established players and emerging technology companies. Key players like TomTom, HERE Technologies, and INRIX are leveraging their existing mapping and navigation expertise to provide comprehensive real-time traffic data solutions. However, newer companies are entering the market with innovative data aggregation and analysis techniques, leading to increased competition and potentially lower prices. The geographic distribution of market share is expected to be dominated by North America and Europe initially, given the higher adoption rates of smart city technologies and connected vehicle infrastructure in these regions. However, rapid infrastructure development and increasing urbanization in Asia-Pacific are projected to drive substantial market growth in this region over the forecast period. The market's continued growth hinges on continued investment in smart city infrastructure, the expanding adoption of connected car technology, and the continuous development of more sophisticated data analytics.

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.

This app provides the latest traffic volume data for every roadway in the Commonwealth. Traffic volumes are the most recent available and have been approximated in some cases. Click on any roadway to display the traffic volume information. Higher traffic volumes are not necessarily an indication of congestion. Real time mapping on www.511virginia.org provides indications of congestion. Please note that traffic volume data here are bidirectional volume. The latest directional and bidirectional traffic volume data are also available in TMPD’s Pathways for Planning (P4P), https://vdotp4p.com.

Traffic analytics, rankings, and competitive metrics for linear.app as of June 2025

Data Dictionary for District Traffic Web Map used in TPP Statewide Traffic Count App

This statistic gives information on the consumer internet data traffic worldwide from 2016 to 2022, by application category. In 2017, the global consumer data traffic from internet video amounted to ** exabytes per month.

The Nebraska Department of Transporation (NDOT) collects traffic data including traffic flow, traffic counts, and truck traffic flow. The data is averaged out on a yearly basis, and is reported bi-annually, with the first year in the application being 2016. The application includes a filter widget that allows the user to filter the data based on the year that the traffic count was performed. The default filter is set for the most recent iteration of traffic count data, and if there is no filter applied, users can view data for all years by clicking on the features and reading the popup.Maps using this layer (as of 3/20/2023)AADTFlow_MapAADT Flow Map over 6,000UAS Request Webmap - UAS TeamThis item is shared with the following groups:PublicDepartment of TransportationDOT AD 100 HigherDOT DevelopersOther associated items:App -Annual Average Daily Traffic FlowMap -AADTFlow_MapMap Image Layer -AADTFlow_DOT_NE

Context

The data presented here was obtained in a Kali Machine from University of Cincinnati,Cincinnati,OHIO by carrying out packet captures for 1 hour during the evening on Oct 9th,2023 using Wireshark.This dataset consists of 394137 instances were obtained and stored in a CSV (Comma Separated Values) file.This large dataset could be used utilised for different machine learning applications for instance classification of Network traffic,Network performance monitoring,Network Security Management , Network Traffic Management ,network intrusion detection and anomaly detection.

The dataset can be used for a variety of machine learning tasks, such as network intrusion detection, traffic classification, and anomaly detection.

Content :

This network traffic dataset consists of 7 features.Each instance contains the information of source and destination IP addresses, The majority of the properties are numeric in nature, however there are also nominal and date kinds due to the Timestamp.

The network traffic flow statistics (No. Time Source Destination Protocol Length Info) were obtained using Wireshark (https://www.wireshark.org/).

Dataset Columns:

No : Number of Instance. Timestamp : Timestamp of instance of network traffic Source IP: IP address of Source Destination IP: IP address of Destination Portocol: Protocol used by the instance Length: Length of Instance Info: Information of Traffic Instance

Acknowledgements :

I would like thank University of Cincinnati for giving the infrastructure for generation of network traffic data set.

Ravikumar Gattu , Susmitha Choppadandi

Inspiration : This dataset goes beyond the majority of network traffic classification datasets, which only identify the type of application (WWW, DNS, ICMP,ARP,RARP) that an IP flow contains. Instead, it generates machine learning models that can identify specific applications (like Tiktok,Wikipedia,Instagram,Youtube,Websites,Blogs etc.) from IP flow statistics (there are currently 25 applications in total).

**Dataset License: ** CC0: Public Domain

Dataset Usages : This dataset can be used for different machine learning applications in the field of cybersecurity such as classification of Network traffic,Network performance monitoring,Network Security Management , Network Traffic Management ,network intrusion detection and anomaly detection.

ML techniques benefits from this Dataset :

This dataset is highly useful because it consists of 394137 instances of network traffic data obtained by using the 25 applications on a public,private and Enterprise networks.Also,the dataset consists of very important features that can be used for most of the applications of Machine learning in cybersecurity.Here are few of the potential machine learning applications that could be benefited from this dataset are :

1. Network Performance Monitoring : This large network traffic data set can be utilised for analysing the network traffic to identifying the network patterns in the network .This help in designing the network security algorithms for minimise the network probelms.

2. Anamoly Detection : Large network traffic dataset can be utilised training the machine learning models for finding the irregularitues in the traffic which could help identify the cyber attacks.

3.Network Intrusion Detection : This large dataset could be utilised for machine algorithms training and designing the models for detection of the traffic issues,Malicious traffic network attacks and DOS attacks as well.

Leverage the most reliable and compliant mobile device location/foot traffic dataset on the market!

Veraset Movement (GPS Mobility Data) offers unparalleled insights into foot traffic patterns for dozens of countries across the Middle East.

Covering 14+ countries for the Middle East alone, Veraset's foot traffic Data draws on raw GPS data from tier-1 apps, SDKs, and aggregators of mobile devices to provide customers with accurate, up-to-the-minute information on human movement. Ideal for ad tech, planning, retail, and transportation logistics, Veraset's Movement data (footfall) helps shape strategy and make impactful data-driven decisions.

Veraset’s Africa Footfall Panel includes the following countries: - bahrain-BH - iran-IR - iraq-IQ - israel-IL - jordan-JO - kuwait-KW - lebanon-LB - oman-OM - palestinian territories-PS - qatar-QA - saudi arabia-SA - syria-SY - united arab emirates-AE - yemen-YE

Common Use Cases of Veraset's Foot Traffic Data: - Advertising - Ad Placement, Attribution, and Segmentation - Audience Creation/Building - Dynamic Ad Targeting - Infrastructure Plans - Route Optimization - Public Transit Optimization - Credit Card Loyalty - Competitive Analysis - Risk assessment, Underwriting, and Policy Personalization - Enrichment of Existing Datasets - Trade Area Analysis - Predictive Analytics and Trend Forecasting

This application provides an interactive experience to look up traffic count reports across the City of Cleveland. Traffic count reports are conducted using unmanned vehicle counter devices that detect the volume and speed of vehicular traffic.InstructionsEach point represents a single traffic count observation that was conducted since 2019.Zoom into a point, click on it to generate a pop-up that presents summary statistics and a PDF link for each report.Use Filter or Search to narrow down to your area or time of interest.Data GlossarySee: Cleveland Traffic Count Reports - Overview (arcgis.com)Update FrequencyMonthly, at the end of each monthThis application uses the following dataset(s):Cleveland Traffic Count ReportsContactsCity Planning Commission

MIRAGE-2019 is a human-generated dataset for mobile traffic analysis, designed to advance the state-of-the-art in mobile app traffic analysis. It includes traffic generated by over 280 experimenters using 40 mobile apps across 3 devices.

Download MIRAGE-2019: Get the latest downloadable release here.

APP LIST: Details on the apps included in the dataset are available in the downloadable version.

Creative Commons License: MIRAGE-2019 is released under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Cite MIRAGE-2019: If you use MIRAGE-2019 in scientific papers, academic lectures, project reports, or technical documents, please cite:

Giuseppe Aceto, Domenico Ciuonzo, Antonio Montieri, Valerio Persico and Antonio Pescapè, "MIRAGE: Mobile-app Traffic Capture and Ground-truth Creation", 4th IEEE International Conference on Computing, Communications and Security (ICCCS 2019), October 2019, Rome (Italy).

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.

Our Location Intelligence Data connects people's movements to over 14M physical locations globally. These are aggregated and anonymized data that are only used to offer context for the volume and patterns of visits to certain locations. This data feed is compiled from different data sources around the world. Location Intelligence Data Reach: Location Intelligence data brings the POI/Place/OOH level insights calculated on the basis of Factori’s Mobility & People Graph data aggregated from multiple data sources globally. In order to achieve the desired foot-traffic attribution, specific attributes are combined to bring forward the desired reach data.For instance, in order to calculate the foot-traffic for a specific location, a combination of location ID, day of the week and part of the day can be combined to give specific location intelligence data. There can be a maximum of 40 data records possible for one POI based on the combination of these attributes.

cara.app is ranked #4920 in RU with 909.31K Traffic. Categories: . Learn more about website traffic, market share, and more!

Traffic analytics, rankings, and competitive metrics for height.app as of June 2025