Aurora:GeoStudio® is a premier geospatial analysis platform that excels in supporting foot traffic data through its sophisticated Population Dynamics® analytic. Foot traffic data encompasses information about the number of people visiting specific locations or establishments, providing deep insights into customer behavior, patterns, and trends. This data is crucial for businesses looking to understand their audience and make data-driven decisions.

Core Features:

  1.    Data Collection Methods:
  •    Passive Sensors: Aurora:GeoStudio® integrates data collected from passive sensors deployed at various locations. These devices count the number of visitors, track their movement paths, and record the duration of their visits.
  •    Mobile Devices: The platform also leverages data from mobile devices, providing additional insights into foot traffic patterns through location-based services and applications.
  2.    Population Dynamics® Analytic:
  •    Aurora:GeoStudio®’s Population Dynamics® analytic processes foot traffic data to deliver comprehensive insights. This analytic tool helps visualize and understand visitor behavior, peak visiting times, and movement trends within specific areas.
  3.    Visualization and Mapping:
  •    The platform offers advanced visualization capabilities, displaying foot traffic data on customizable maps from providers like Google, Esri, Open, and Stamen. These visualizations help users understand spatial patterns and relationships, facilitating informed decision-making.

Applications:

  1.    Customer Behavior Analysis:
  •    Businesses can analyze foot traffic data to understand customer behavior, such as the number of visitors, the duration of their visits, and the paths they take within an establishment. This information is crucial for tailoring services and improving customer satisfaction.
  2.    Store Layout Optimization:
  •    Foot traffic data helps businesses optimize store layouts by identifying high-traffic areas and bottlenecks. By understanding how customers move through a space, businesses can rearrange products and displays to enhance flow and maximize sales opportunities.
  3.    Marketing Strategy Enhancement:
  •    Aurora:GeoStudio® enables businesses to refine their marketing strategies by providing insights into peak visiting times and customer demographics. This data supports targeted marketing campaigns, ensuring promotions reach the right audience at the right time.
  4.    Operational Efficiency:
  •    Understanding foot traffic patterns allows businesses to optimize staffing levels, manage inventory more effectively, and improve overall operational efficiency. By aligning resources with actual customer demand, businesses can enhance service delivery and reduce costs.
  5.    Urban Planning and Public Spaces:
  •    Foot traffic data is invaluable for urban planners and managers of public spaces. It helps in designing public areas that accommodate pedestrian flow efficiently and ensures that amenities are accessible and well-placed.

Aurora:GeoStudio®’s support for foot traffic data through the Population Dynamics® analytic offers businesses and urban planners a powerful tool for understanding and optimizing visitor behavior. By leveraging data from sensors, cameras, and mobile devices, the platform provides detailed insights into customer movements and trends. These insights enable businesses to enhance their marketing strategies, optimize store layouts, and improve operational efficiency. For urban planners, foot traffic data facilitates the design of more effective and accessible public spaces. Aurora:GeoStudio®’s advanced features empower users to make informed decisions and achieve a comprehensive understanding of foot traffic dynamics, leading to better strategic outcomes.

Traffic-related delays are a major drain on the US economy, costing around $100 billion annually, rivaling the price tag of NASA's Artemis moon rocket. Therefore, real-time traffic information providers, expected to earn $5.6 billion in 2024, provide valuable solutions that help keep the economy moving. These systems source manifold data from phones, cameras, car manufacturers, event planners, and civil engineers. This vital information is then processed and sold to navigation apps, governments, or even back to the companies that provided it. Growth in this industry has surged at a compound annual growth rate (CAGR) of 10/2% over the last five years, largely driven by smarter systems becoming commonplace in smartphones and vehicles. Technological innovations have dramatically streamlined traffic management and monitoring while keeping profit margins healthy. Due to a rise in GPS-enabled phones and vehicle devices, traffic information is increasingly accurate and up-to-date. Smartphones have enhanced the spread of traffic data and enabled the extensive collection of live location information, aiding event and weather-related transport planning. Alongside this, roadside camera tech has evolved to identify cars and their speeds and count and categorize vehicles on the road, all without human help. Over the next five years, the industry will continue its upward trajectory, with a predicted CAGR of 2.0%, generating $6.2 billion by 2029. The easing of restrictive monetary policy will likely boost sales of cars equipped with navigation and traffic tools. Nevertheless, experts predict a slowdown if issues linked to the adverse effects of private vehicles take precedence over traditional car culture. In these changing times, it is clear that traffic systems hold vital importance, offering crucial guidance to communities at the planning level and for drivers.

According to data collected in the first half of 2021, five leading tech firms accounted for more than half of global data traffic. Google accounted for around a fifth of global data traffic, with its share including traffic driven by its subsidiary video platform YouTube. Google is followed by Facebook with a share of around 15 percent, while Netflix accounts for around 9 percent of traffic.

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

Veraset Movement (GPS Mobility Data) offers unparalleled insights into footfall traffic patterns across nearly four dozen countries in Africa.

Covering 46+ countries, Veraset's Mobility 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 (Mobility data) helps shape strategy and make impactful data-driven decisions.

Veraset’s Africa Movement Panel includes the following countries: - algeria-DZ - angola-AO - benin-BJ - botswana-BW - burkina faso-BF - burundi-BI - cameroon-CM - central african republic-CF - chad-TD - comoros-KM - congo-brazzaville-CG - congo-kinshasa-CD - djibouti-DJ - egypt-EG - eritrea-ER - ethiopia-ET - gabon-GA - gambia-GM - ghana-GH - guinea-bissau-GW - kenya-KE - lesotho-LS - liberia-LR - libya-LY - madagascar-MG - malawi-MW - mali-ML - mauritius-MU - morocco-MA - mozambique-MZ - namibia-NA - nigeria-NG - rwanda-RW - senegal-SN - seychelles-SC - sierra leone-SL - somalia-SO - south africa-ZA - south sudan-SS - tanzania-TZ - togo-TG - tunisia-TN - uganda-UG - zambia-ZM - zimbabwe-ZW

Companies use Veraset's Mobility Data for: - 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 map contains 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 TomTom (www.tomtom.com). Historical traffic is based on the average of observed speeds over the past year. 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 can be requested for the current time and any time in the future. A map for a future request might be used for planning purposes. The map 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.

Aurora:GeoStudio® stands out in geospatial analytics by effectively utilizing passive sensor data to provide detailed mobile location metadata. This passive data collection method captures a wealth of information about mobile devices without requiring active user engagement, offering an extensive overview of device interactions and movements. Here’s a breakdown of the specific data collected and how it enhances various applications:

Data Collected by Passive Sensors:

  1.    Identification and Metadata:
  •    Wireless ID: A unique detection ID associated with each data value.
  •    Device ID: The name of the sensor collecting the data.
  •    Sensor ID: An ID generated for each sensor upon creation.
  •    Department ID: An ID generated for each department upon creation.
  2.    Temporal Data:
  •    Date and Time: The precise date and time when the detection occurred.
  3.    Detection Details:
  •    Value: Sensitive data or data value collected.
  •    Signal Type: Type of detection signal (e.g., WiFi, BLE, 4G LTE).
  •    Brand and Model: The brand (e.g., Samsung, Apple) and model (e.g., Samsung A22) of the detected device.
  •    Status: Indicates if the detection occurred within the sensor’s working time (“Was Working” or “Was Not Working”).
  •    Provider: The network provider associated with the detection (e.g., Vodafone), represented by the MNC.
  •    Country: The country where the detection occurred, represented by the MCC.
  •    Role Color ID: Assigned role color IDs for devices (e.g., 6 for unauthorized, 1 for trusted coworkers).
  •    Type: Type of detection data (e.g., TMSI, IMSI, MAC).
  •    Trusted: Indicates if the detection is trusted (default is “No” for new detections).
  •    Signal Strength: Indicates the range within which the device was detected.
  •    Company: The company name determined based on the sensitive data.
  4.    Database and Location Information:
  •    Created At and Updated At: Timestamps for record creation and updates in the database.
  •    Location Details: Name, city, state, country, street, and zip code of the location tagged with the sensor.
  •    Coordinates: Latitude and longitude of the tagged location.

Applications:

  1.    Customer Behavior Analysis:
  •    By analyzing foot traffic data, businesses can understand customer behavior, including visit frequency, duration, and movement paths. This information is crucial for tailoring services and improving customer satisfaction.
  2.    Store Layout Optimization:
  •    Passive sensor data helps identify high-traffic areas and bottlenecks within stores, allowing businesses to optimize product placement and store layouts to enhance customer flow and maximize sales opportunities.
  3.    Marketing Strategy Enhancement:
  •    Businesses can refine their marketing strategies using insights from peak visiting times and customer demographics, enabling targeted campaigns that reach the right audience at optimal times.
  4.    Operational Efficiency:
  •    Understanding foot traffic patterns allows businesses to optimize staffing levels, manage inventory more effectively, and improve overall operational efficiency. Aligning resources with actual customer demand enhances service delivery and reduces costs.
  5.    Urban Planning and Public Spaces:
  •    Urban planners can use foot traffic data to design public areas that accommodate pedestrian flow efficiently and ensure that amenities are accessible and well-placed.

Aurora:GeoStudio®’s integration of passive sensor data provides a comprehensive view of mobile location metadata, offering detailed insights into device movements, customer behavior, and spatial dynamics. The extensive data collected, including identification, temporal, detection, and location information, supports a wide range of applications from retail optimization to urban planning. By leveraging this data, Aurora:GeoStudio® enables businesses and planners to make informed decisions, optimize operations, and enhance customer experiences, thereby setting a new standard in geospatial analytics and location-based services.

Urban SDK is a GIS data management platform and global provider of mobility, urban characteristics, and alt datasets. Urban SDK Traffic data provides traffic volume, average speed, average travel time and congestion for logistics, transportation planning, traffic monitoring, routing and urban planning. Traffic data is generated from cars, trucks and mobile devices for major road networks in US and Canada.

"With the old data I used, it took me 3-4 weeks to create a presentation. I will be able to do 3-4x the work with your Urban SDK traffic data."

Traffic Volume, Speed and Congestion Data Type Profile:

• Traffic volume in annual average daily and daily traffic volumes per roadway
• Average travel speed in 15 minute and hourly intervals per roadway
• Travel time in seconds in 15 minute intervals per roadway
• Commute travel time in minutes in annual interval estimates in geohash boundaries
• Congested roadway segments based on travel time reliability in monthly intervals per roadway
• Traffic data attributed spatially to state, county, road functional class, road name, road segment, segment length in km or miles as geojson

Industry Solutions include:

• Transportation Planning
• Traffic Monitoring
• Congestion Management and Trend Analysis
• Travel Demand Modeling
• Traffic Impact Analysis
• Parking Analysis
• Transit System Planning
• Route Planning
• Civil Engineering
• Site Selection

Use cases:

• Traffic monitoring, data analysis, and forecasting for transportation, transit, and urban planning.
• Improve dynamic routing with accurate travel time and congestion data
• Environmental and emissions analysis
• Travel demand and transportation modeling
• Location analysis and assessment for commercial site selection for retail or logistics related locations

The real-time traffic information providers industry in the UK has undergone a substantial transformation amid the technological revolution, solidifying its role as a vital service for modern consumers. The uptake in smartphone usage combined with dwindling mobile prices has made real-time traffic data easily accessible to many users. Despite the temporary setback brought on by the pandemic, with driving miles dropping significantly, the industry rebounded quickly as normalcy returned, highlighting its resilience and essential nature in day-to-day commuting and commercial logistics. Revenue is estimated to improve by 2.7% in 2024-25. Industry revenue is anticipated to hike at a compound annual rate of 2.4% over the five years through 2024-25, to £98.8 million. The industry's growth has been primarily driven by sophisticated technological methods and strategic partnerships. Traffic information is now sourced from millions of data points encompassing taxis, smartphones, registered vehicles and traditional road sensors. This growing data compilation and necessary computer infrastructure have permeated virtually every aspect of travel in the UK. Notable providers like HERE and INRIX have formed alliances with automotive giants and mobile companies, integrating their services into preinstalled vehicle GPS systems and popular phone mapping applications. These collaborations and the increasing volume of real-time traffic data have necessitated a higher investment in research and development, albeit at the cost of restricted profit growth amid heightened wage expenditures and continuous technological advancements. Industry revenue is projected to grow at a compound annual rate of 2.9% over the five years through 2029-30, to reach £113.8 million. Providers are expected to extend their geographical coverage and enhance the granularity of their offerings, providing deeper insights into road conditions in smaller towns and villages. With disposable incomes recovering after the cost-of-living crisis, consumers' interest in well-enabled cars is likely to climb, supporting revenue. Companies like HERE plan to diversify revenue streams via mobile advertising platforms, elevating monetisation through targeted ads. However, talent acquisition will remain a pressing challenge, spurred by a constricted talent pool and immigration barriers, necessitating innovative recruitment strategies in a competitive landscape.

Expert industry market research on the Real-Time Traffic Information Providers in the US (2005-2030). Make better business decisions, faster with IBISWorld's industry market research reports, statistics, analysis, data, trends and forecasts.

Portuguese mobile broadband internet traffic was dominated by NOS, which surpassed Vodafone and, in 2022, presented a traffic share of 36.4 percent. By 2023, it had a share of 35.7. Vodafone followed, with a share of 35.4 percent, in 2023, while MEO presented a quota of 28 percent.

Colombia Internet Traffic: International: HVTV data was reported at 0.000 GB in 27 Sep 2020. This stayed constant from the previous number of 0.000 GB for 26 Sep 2020. Colombia Internet Traffic: International: HVTV data is updated daily, averaging 0.000 GB from Mar 2020 (Median) to 27 Sep 2020, with 180 observations. The data reached an all-time high of 0.000 GB in 27 Sep 2020 and a record low of 0.000 GB in 27 Sep 2020. Colombia Internet Traffic: International: HVTV data remains active status in CEIC and is reported by Communications Regulation Commission. The data is categorized under Global Database’s Colombia – Table CO.TB004: Internet Traffic: by Provider.

Colombia Internet Traffic: Local: Edatel S.A data was reported at 0.000 GB in 27 Sep 2020. This stayed constant from the previous number of 0.000 GB for 26 Sep 2020. Colombia Internet Traffic: Local: Edatel S.A data is updated daily, averaging 0.000 GB from Mar 2020 (Median) to 27 Sep 2020, with 182 observations. The data reached an all-time high of 0.000 GB in 27 Sep 2020 and a record low of 0.000 GB in 27 Sep 2020. Colombia Internet Traffic: Local: Edatel S.A data remains active status in CEIC and is reported by Communications Regulation Commission. The data is categorized under Global Database’s Colombia – Table CO.TB004: Internet Traffic: by Provider.

Please refer to the original data article for further data description: Jan Luxemburk et al. CESNET-QUIC22: A large one-month QUIC network traffic dataset from backbone lines, Data in Brief, 2023, 108888, ISSN 2352-3409, https://doi.org/10.1016/j.dib.2023.108888. We recommend using the CESNET DataZoo python library, which facilitates the work with large network traffic datasets. More information about the DataZoo project can be found in the GitHub repository https://github.com/CESNET/cesnet-datazoo. The QUIC (Quick UDP Internet Connection) protocol has the potential to replace TLS over TCP, which is the standard choice for reliable and secure Internet communication. Due to its design that makes the inspection of QUIC handshakes challenging and its usage in HTTP/3, there is an increasing demand for research in QUIC traffic analysis. This dataset contains one month of QUIC traffic collected in an ISP backbone network, which connects 500 large institutions and serves around half a million people. The data are delivered as enriched flows that can be useful for various network monitoring tasks. The provided server names and packet-level information allow research in the encrypted traffic classification area. Moreover, included QUIC versions and user agents (smartphone, web browser, and operating system identifiers) provide information for large-scale QUIC deployment studies. Data capture The data was captured in the flow monitoring infrastructure of the CESNET2 network. The capturing was done for four weeks between 31.10.2022 and 27.11.2022. The following list provides per-week flow count, capture period, and uncompressed size:

W-2022-44

Uncompressed Size: 19 GB Capture Period: 31.10.2022 - 6.11.2022 Number of flows: 32.6M W-2022-45

Uncompressed Size: 25 GB Capture Period: 7.11.2022 - 13.11.2022 Number of flows: 42.6M W-2022-46

Uncompressed Size: 20 GB Capture Period: 14.11.2022 - 20.11.2022 Number of flows: 33.7M W-2022-47

Uncompressed Size: 25 GB Capture Period: 21.11.2022 - 27.11.2022 Number of flows: 44.1M CESNET-QUIC22

Uncompressed Size: 89 GB Capture Period: 31.10.2022 - 27.11.2022 Number of flows: 153M

Data description The dataset consists of network flows describing encrypted QUIC communications. Flows were created using ipfixprobe flow exporter and are extended with packet metadata sequences, packet histograms, and with fields extracted from the QUIC Initial Packet, which is the first packet of the QUIC connection handshake. The extracted handshake fields are the Server Name Indication (SNI) domain, the used version of the QUIC protocol, and the user agent string that is available in a subset of QUIC communications. Packet Sequences Flows in the dataset are extended with sequences of packet sizes, directions, and inter-packet times. For the packet sizes, we consider payload size after transport headers (UDP headers for the QUIC case). Packet directions are encoded as ±1, +1 meaning a packet sent from client to server, and -1 a packet from server to client. Inter-packet times depend on the location of communicating hosts, their distance, and on the network conditions on the path. However, it is still possible to extract relevant information that correlates with user interactions and, for example, with the time required for an API/server/database to process the received data and generate the response to be sent in the next packet. Packet metadata sequences have a length of 30, which is the default setting of the used flow exporter. We also derive three fields from each packet sequence: its length, time duration, and the number of roundtrips. The roundtrips are counted as the number of changes in the communication direction (from packet directions data); in other words, each client request and server response pair counts as one roundtrip. Flow statistics Flows also include standard flow statistics, which represent aggregated information about the entire bidirectional flow. The fields are: the number of transmitted bytes and packets in both directions, the duration of flow, and packet histograms. Packet histograms include binned counts of packet sizes and inter-packet times of the entire flow in both directions (more information in the PHISTS plugin documentation There are eight bins with a logarithmic scale; the intervals are 0-15, 16-31, 32-63, 64-127, 128-255, 256-511, 512-1024, >1024 [ms or B]. The units are milliseconds for inter-packet times and bytes for packet sizes. Moreover, each flow has its end reason - either it was idle, reached the active timeout, or ended due to other reasons. This corresponds with the official IANA IPFIX-specified values. The FLOW_ENDREASON_OTHER field represents the forced end and lack of resources reasons. The end of flow detected reason is not considered because it is not relevant for UDP connections. Dataset structure The dataset flows are delivered in compressed CSV files. CSV files contain one flow per row; data columns are summarized in the provided list below. For each flow data file, there is a JSON file with the number of saved and seen (before sampling) flows per service and total counts of all received (observed on the CESNET2 network), service (belonging to one of the dataset's services), and saved (provided in the dataset) flows. There is also the stats-week.json file aggregating flow counts of a whole week and the stats-dataset.json file aggregating flow counts for the entire dataset. Flow counts before sampling can be used to compute sampling ratios of individual services and to resample the dataset back to the original service distribution. Moreover, various dataset statistics, such as feature distributions and value counts of QUIC versions and user agents, are provided in the dataset-statistics folder. The mapping between services and service providers is provided in the servicemap.csv file, which also includes SNI domains used for ground truth labeling. The following list describes flow data fields in CSV files:

ID: Unique identifier SRC_IP: Source IP address DST_IP: Destination IP address DST_ASN: Destination Autonomous System number SRC_PORT: Source port DST_PORT: Destination port PROTOCOL: Transport protocol QUIC_VERSION QUIC: protocol version QUIC_SNI: Server Name Indication domain QUIC_USER_AGENT: User agent string, if available in the QUIC Initial Packet TIME_FIRST: Timestamp of the first packet in format YYYY-MM-DDTHH-MM-SS.ffffff TIME_LAST: Timestamp of the last packet in format YYYY-MM-DDTHH-MM-SS.ffffff DURATION: Duration of the flow in seconds BYTES: Number of transmitted bytes from client to server BYTES_REV: Number of transmitted bytes from server to client PACKETS: Number of packets transmitted from client to server PACKETS_REV: Number of packets transmitted from server to client PPI: Packet metadata sequence in the format: [[inter-packet times], [packet directions], [packet sizes]] PPI_LEN: Number of packets in the PPI sequence PPI_DURATION: Duration of the PPI sequence in seconds PPI_ROUNDTRIPS: Number of roundtrips in the PPI sequence PHIST_SRC_SIZES: Histogram of packet sizes from client to server PHIST_DST_SIZES: Histogram of packet sizes from server to client PHIST_SRC_IPT: Histogram of inter-packet times from client to server PHIST_DST_IPT: Histogram of inter-packet times from server to client APP: Web service label CATEGORY: Service category FLOW_ENDREASON_IDLE: Flow was terminated because it was idle FLOW_ENDREASON_ACTIVE: Flow was terminated because it reached the active timeout FLOW_ENDREASON_OTHER: Flow was terminated for other reasons

Link to other CESNET datasets

https://www.liberouter.org/technology-v2/tools-services-datasets/datasets/ https://github.com/CESNET/cesnet-datazoo Please cite the original data article:

@article{CESNETQUIC22, author = {Jan Luxemburk and Karel Hynek and Tomáš Čejka and Andrej Lukačovič and Pavel Šiška}, title = {CESNET-QUIC22: a large one-month QUIC network traffic dataset from backbone lines}, journal = {Data in Brief}, pages = {108888}, year = {2023}, issn = {2352-3409}, doi = {https://doi.org/10.1016/j.dib.2023.108888}, url = {https://www.sciencedirect.com/science/article/pii/S2352340923000069} }

Colombia Internet Traffic: Network Access Point: Virgin Mobile data was reported at 0.000 GB in 27 Sep 2020. This stayed constant from the previous number of 0.000 GB for 26 Sep 2020. Colombia Internet Traffic: Network Access Point: Virgin Mobile data is updated daily, averaging 0.000 GB from Mar 2020 (Median) to 27 Sep 2020, with 182 observations. The data reached an all-time high of 0.000 GB in 27 Sep 2020 and a record low of 0.000 GB in 27 Sep 2020. Colombia Internet Traffic: Network Access Point: Virgin Mobile data remains active status in CEIC and is reported by Communications Regulation Commission. The data is categorized under Global Database’s Colombia – Table CO.TB004: Internet Traffic: by Provider.

Colombia Internet Traffic: Local: Avantel S.A.S En Reorganizacion data was reported at 0.000 GB in 27 Sep 2020. This stayed constant from the previous number of 0.000 GB for 26 Sep 2020. Colombia Internet Traffic: Local: Avantel S.A.S En Reorganizacion data is updated daily, averaging 0.000 GB from Mar 2020 (Median) to 27 Sep 2020, with 182 observations. The data reached an all-time high of 0.000 GB in 27 Sep 2020 and a record low of 0.000 GB in 27 Sep 2020. Colombia Internet Traffic: Local: Avantel S.A.S En Reorganizacion data remains active status in CEIC and is reported by Communications Regulation Commission. The data is categorized under Global Database’s Colombia – Table CO.TB004: Internet Traffic: by Provider.

Over the observed period, there has been a significant rise in mobile internet data traffic in Hungary. In the second quarter of 2024, the volume of mobile data traffic reached 391 petabytes compared to 110 petabytes in the third quarter of 2019. Mobile networks In 2023, most of the mobile internet traffic was transmitted through 4G networks, with 3G networks accounting for approximately two percent of the mobile data traffic. In April 2020, Telekom launched its 5G network with two other significant telecommunication service providers, namely Telenor and Vodafone following in its footsteps over the next year. By the fourth quarter of 2023, 5G networks accounted for nearly a third of domestic mobile data traffic. Mobile internet providers In the second quarter of 2024, the leading mobile internet provider was Magyar Telekom in Hungary, accounting for approximately 44 percent of the mobile internet traffic. Vodafone ranked second, with a 27 percent market share while Yettel followed, accounting for 26 percent of the market.

Turkey Annual Call Traffic Volume: Fixed Service Providers data was reported at 7.600 min bn in 2017. This records a decrease from the previous number of 9.200 min bn for 2016. Turkey Annual Call Traffic Volume: Fixed Service Providers data is updated yearly, averaging 23.600 min bn from Dec 2003 (Median) to 2017, with 15 observations. The data reached an all-time high of 74.100 min bn in 2003 and a record low of 7.600 min bn in 2017. Turkey Annual Call Traffic Volume: Fixed Service Providers data remains active status in CEIC and is reported by Information and Communication Technologies Authority . The data is categorized under Global Database’s Turkey – Table TR.TB002: Usage Volume.

Moving Traffic Media is a leading provider of traffic and transportation data, offering insights that help organizations optimize their logistics and operations. With a focus on accuracy and reliability, the company aggregates data from a range of sources to provide a comprehensive view of traffic patterns, road conditions, and transportation trends.

By leveraging Moving Traffic Media's vast datasets, clients can gain a deeper understanding of transportation corridors, identify bottlenecks and inefficiencies, and make data-driven decisions to improve their operations. Whether it's route optimization, traffic management, or infrastructure planning, Moving Traffic Media's data provides a valuable resource for those in the transportation industry.

9 Active Global Road traffic suppliers, manufacturers list and Global Road traffic exporters directory compiled from actual Global export shipments of Road traffic.

Colombia Internet Traffic: Edatel S.A data was reported at 1,305,396.000 GB in 27 Sep 2020. This records an increase from the previous number of 1,279,692.000 GB for 26 Sep 2020. Colombia Internet Traffic: Edatel S.A data is updated daily, averaging 1,265,598.000 GB from Mar 2020 (Median) to 27 Sep 2020, with 182 observations. The data reached an all-time high of 1,417,932.000 GB in 16 Aug 2020 and a record low of 859,140.000 GB in 22 Apr 2020. Colombia Internet Traffic: Edatel S.A data remains active status in CEIC and is reported by Communications Regulation Commission. The data is categorized under Global Database’s Colombia – Table CO.TB004: Internet Traffic: by Provider.