57 datasets found
  1. A

    ‘Popular Website Traffic Over Time ’ analyzed by Analyst-2

    • analyst-2.ai
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    Analyst-2 (analyst-2.ai) / Inspirient GmbH (inspirient.com), ‘Popular Website Traffic Over Time ’ analyzed by Analyst-2 [Dataset]. https://analyst-2.ai/analysis/kaggle-popular-website-traffic-over-time-62e4/62549059/?iid=003-357&v=presentation
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    Dataset authored and provided by
    Analyst-2 (analyst-2.ai) / Inspirient GmbH (inspirient.com)
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Analysis of ‘Popular Website Traffic Over Time ’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/yamqwe/popular-website-traffice on 13 February 2022.

    --- Dataset description provided by original source is as follows ---

    About this dataset

    Background

    Have you every been in a conversation and the question comes up, who uses Bing? This question comes up occasionally because people wonder if these sites have any views. For this research study, we are going to be exploring popular website traffic for many popular websites.

    Methodology

    The data collected originates from SimilarWeb.com.

    Source

    For the analysis and study, go to The Concept Center

    This dataset was created by Chase Willden and contains around 0 samples along with 1/1/2017, Social Media, technical information and other features such as: - 12/1/2016 - 3/1/2017 - and more.

    How to use this dataset

    • Analyze 11/1/2016 in relation to 2/1/2017
    • Study the influence of 4/1/2017 on 1/1/2017
    • More datasets

    Acknowledgements

    If you use this dataset in your research, please credit Chase Willden

    Start A New Notebook!

    --- Original source retains full ownership of the source dataset ---

  2. m

    Encrypted Traffic Feature Dataset for Machine Learning and Deep Learning...

    • data.mendeley.com
    Updated Dec 6, 2022
    + more versions
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    Zihao Wang (2022). Encrypted Traffic Feature Dataset for Machine Learning and Deep Learning based Encrypted Traffic Analysis [Dataset]. http://doi.org/10.17632/xw7r4tt54g.1
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    Dataset updated
    Dec 6, 2022
    Authors
    Zihao Wang
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    This traffic dataset contains a balance size of encrypted malicious and legitimate traffic for encrypted malicious traffic detection and analysis. The dataset is a secondary csv feature data that is composed of six public traffic datasets.

    Our dataset is curated based on two criteria: The first criterion is to combine widely considered public datasets which contain enough encrypted malicious or encrypted legitimate traffic in existing works, such as Malware Capture Facility Project datasets. The second criterion is to ensure the final dataset balance of encrypted malicious and legitimate network traffic.

    Based on the criteria, 6 public datasets are selected. After data pre-processing, details of each selected public dataset and the size of different encrypted traffic are shown in the “Dataset Statistic Analysis Document”. The document summarized the malicious and legitimate traffic size we selected from each selected public dataset, the traffic size of each malicious traffic type, and the total traffic size of the composed dataset. From the table, we are able to observe that encrypted malicious and legitimate traffic equally contributes to approximately 50% of the final composed dataset.

    The datasets now made available were prepared to aim at encrypted malicious traffic detection. Since the dataset is used for machine learning or deep learning model training, a sample of train and test sets are also provided. The train and test datasets are separated based on 1:4. Such datasets can be used for machine learning or deep learning model training and testing based on selected features or after processing further data pre-processing.

  3. Annual Average Daily Traffic TDA

    • gis-fdot.opendata.arcgis.com
    • mapdirect-fdep.opendata.arcgis.com
    • +1more
    Updated Jul 21, 2017
    + more versions
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    Florida Department of Transportation (2017). Annual Average Daily Traffic TDA [Dataset]. https://gis-fdot.opendata.arcgis.com/datasets/annual-average-daily-traffic-tda
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    Dataset updated
    Jul 21, 2017
    Dataset authored and provided by
    Florida Department of Transportationhttps://www.fdot.gov/
    Area covered
    Description

    The FDOT Annual Average Daily Traffic feature class provides spatial information on Annual Average Daily Traffic section breaks for the state of Florida. In addition, it provides affiliated traffic information like KFCTR, DFCTR and TFCTR among others. This dataset is maintained by the Transportation Data & Analytics office (TDA). The source spatial data for this hosted feature layer was created on: 07/12/2025.Download Data: Enter Guest as Username to download the source shapefile from here: https://ftp.fdot.gov/file/d/FTP/FDOT/co/planning/transtat/gis/shapefiles/aadt.zip

  4. R

    Traffic Dataset

    • universe.roboflow.com
    zip
    Updated Oct 4, 2021
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    Traffic Dataset [Dataset]. https://universe.roboflow.com/traffic/traffic-dataset-z21ak
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    zipAvailable download formats
    Dataset updated
    Oct 4, 2021
    Dataset authored and provided by
    Traffic
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Variables measured
    Vehicle Bounding Boxes
    Description

    Here are a few use cases for this project:

    1. Traffic Flow Analysis: The dataset can be used in machine learning models to analyze traffic flow in cities. It can identify the type of vehicles on the city roads at different times of the day, helping in planning and traffic management.

    2. Vehicle Class Based Toll Collection: Toll booths can use this model to automatically classify and charge vehicles based on their type, enabling a more efficient and automated system.

    3. Parking Management System: Parking lot owners can use this model to easily classify vehicles as they enter for better space management. Knowing the vehicle type can help assign it to the most suitable parking spot.

    4. Traffic Rule Enforcement: The dataset can be used to create a computer vision model to automatically detect any traffic violations like wrong lane driving by different vehicle types, and notify law enforcement agencies.

    5. Smart Ambulance Tracking: The system can help in identifying and tracking ambulances and other emergency vehicles, enabling traffic management systems to provide priority routing during emergencies.

  5. v

    Traffic Volume

    • opendata.victoria.ca
    • open-vicmap.opendata.arcgis.com
    Updated May 6, 2021
    + more versions
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    City of Victoria (2021). Traffic Volume [Dataset]. https://opendata.victoria.ca/datasets/traffic-volume
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    Dataset updated
    May 6, 2021
    Dataset authored and provided by
    City of Victoria
    License

    https://opendata.victoria.ca/pages/open-data-licencehttps://opendata.victoria.ca/pages/open-data-licence

    Area covered
    Description

    Traffic Volume (24hr count). Data are updated as needed by the Transportation department (typically in the summer), and subsequently copied to VicMap and the Open Data Portal the following day.Traffic speed and volume data are collected at various locations around the city, from different locations each year, using a variety of technologies and manual counting. Counters are placed on streets and at intersections, typically for 24-hour periods. Targeted information is also collected during morning or afternoon peak period travel times and can also be done for several days at a time to capture variability on different days of the week. The City collects data year-round and in all types of weather (except for extreme events like snowstorms). The City also uses data from our agency partners like Victoria Police, the CRD or ICBC. Speed values recorded at each location represent the 85th percentile speed, which means 85% or less traffic travels at that speed. This is standard practice among municipalities to reduce anomalies due to excessively speedy or excessively slow drivers. Values recorded are based on the entire 24-hour period.The Traffic Volume dataset is linear. The lines can be symbolized using arrows and the "Direction" attribute. Where the direction value is "one", use an arrow symbol where the arrow is at the end of the line. Where the direction value is "both", use an arrow symbol where there are arrows at both ends of the line. Use the "Label" field to add labels. The label field indicates the traffic volume at each location, and the year the data was collected. So for example, “2108(05)” means 2108 vehicles were counted in the year 2005 at that location.Data are automatically copied to the Open Data Portal. The "Last Updated" date shown on our Open Data Portal refers to the last time the data schema was modified in the portal, or any changes were made to this description. We update our data through automated scripts which does not trigger the "last updated" date to change. Note: Attributes represent each field in a dataset, and some fields will contain information such as ID numbers. As a result some visualizations on the tabs on our Open Data page will not be relevant.

  6. m

    USA POI & Foot Traffic Enriched Geospatial Dataset by Predik Data-Driven

    • app.mobito.io
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    USA POI & Foot Traffic Enriched Geospatial Dataset by Predik Data-Driven [Dataset]. https://app.mobito.io/data-product/usa-enriched-geospatial-framework-dataset
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    Area covered
    United States
    Description

    Our dataset provides detailed and precise insights into the business, commercial, and industrial aspects of any given area in the USA (Including Point of Interest (POI) Data and Foot Traffic. The dataset is divided into 150x150 sqm areas (geohash 7) and has over 50 variables. - Use it for different applications: Our combined dataset, which includes POI and foot traffic data, can be employed for various purposes. Different data teams use it to guide retailers and FMCG brands in site selection, fuel marketing intelligence, analyze trade areas, and assess company risk. Our dataset has also proven to be useful for real estate investment.- Get reliable data: Our datasets have been processed, enriched, and tested so your data team can use them more quickly and accurately.- Ideal for trainning ML models. The high quality of our geographic information layers results from more than seven years of work dedicated to the deep understanding and modeling of geospatial Big Data. Among the features that distinguished this dataset is the use of anonymized and user-compliant mobile device GPS location, enriched with other alternative and public data.- Easy to use: Our dataset is user-friendly and can be easily integrated to your current models. Also, we can deliver your data in different formats, like .csv, according to your analysis requirements. - Get personalized guidance: In addition to providing reliable datasets, we advise your analysts on their correct implementation.Our data scientists can guide your internal team on the optimal algorithms and models to get the most out of the information we provide (without compromising the security of your internal data).Answer questions like: - What places does my target user visit in a particular area? Which are the best areas to place a new POS?- What is the average yearly income of users in a particular area?- What is the influx of visits that my competition receives?- What is the volume of traffic surrounding my current POS?This dataset is useful for getting insights from industries like:- Retail & FMCG- Banking, Finance, and Investment- Car Dealerships- Real Estate- Convenience Stores- Pharma and medical laboratories- Restaurant chains and franchises- Clothing chains and franchisesOur dataset includes more than 50 variables, such as:- Number of pedestrians seen in the area.- Number of vehicles seen in the area.- Average speed of movement of the vehicles seen in the area.- Point of Interest (POIs) (in number and type) seen in the area (supermarkets, pharmacies, recreational locations, restaurants, offices, hotels, parking lots, wholesalers, financial services, pet services, shopping malls, among others). - Average yearly income range (anonymized and aggregated) of the devices seen in the area.Notes to better understand this dataset:- POI confidence means the average confidence of POIs in the area. In this case, POIs are any kind of location, such as a restaurant, a hotel, or a library. - Category confidences, for example"food_drinks_tobacco_retail_confidence" indicates how confident we are in the existence of food/drink/tobacco retail locations in the area. - We added predictions for The Home Depot and Lowe's Home Improvement stores in the dataset sample. These predictions were the result of a machine-learning model that was trained with the data. Knowing where the current stores are, we can find the most similar areas for new stores to open.How efficient is a Geohash?Geohash is a faster, cost-effective geofencing option that reduces input data load and provides actionable information. Its benefits include faster querying, reduced cost, minimal configuration, and ease of use.Geohash ranges from 1 to 12 characters. The dataset can be split into variable-size geohashes, with the default being geohash7 (150m x 150m).

  7. d

    Global Travel, Entertainment, & Transportation Location Data | Foot Traffic...

    • datarade.ai
    .json, .csv
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    dataplor, Global Travel, Entertainment, & Transportation Location Data | Foot Traffic Data [Dataset]. https://datarade.ai/data-products/global-travel-entertainment-transportation-location-data-dataplor
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    .json, .csvAvailable download formats
    Dataset authored and provided by
    dataplor
    Area covered
    Lebanon, Dominican Republic, Guam, Tonga, Fiji, Cuba, Ghana, Grenada, Suriname, French Polynesia
    Description

    In the industries of travel, entertainment, and transportation, success hinges on understanding the intricate web of locations that shape consumer experiences. dataplor's comprehensive location intelligence dataset offers an unparalleled view into this global landscape, providing businesses and organizations with the actionable insights they need to thrive.

    What's Inside the Dataset: - Venue Variety: From airports and resorts to entertainment venues and transportation hubs, our dataset covers a vast array of locations that define the TET landscape. - Detailed Profiles: Delve into the specifics of each venue, including official names, addresses, and more. - Categorization: Gain a clear understanding of each location's role within the industry through precise categorization by segment (travel, entertainment, transportation) and sub-category (e.g., athletic club, beer hall, comedy club, luxury hotel). - Location Precision: Pinpoint the exact location of each venue with latitude and longitude coordinates, ensuring accuracy for mapping, analysis, and decision-making.

    How Our Data Drives Success: - Strategic Planning: Identify untapped markets, assess competition, and make informed decisions about new ventures or expansions. - Targeted Marketing: Develop tailored marketing campaigns that resonate with specific audiences based on location demographics and interests. - Resource Optimization: Improve operational efficiency by understanding the distribution and utilization of transportation infrastructure and entertainment venues. - Enhanced Customer Experiences: Utilize data-driven insights to create personalized experiences that meet the unique needs and preferences of travelers and consumers.

    Empower Your Strategy: dataplor's Global Travel, Entertainment & Transportation Locations Dataset is your key to unlocking the full potential of the travel, entertainment, and transportation industries. With our precise, comprehensive, and up-to-date data, you'll gain the competitive edge needed to navigate this dynamic landscape and achieve your business goals.

  8. d

    Air Traffic Landings Statistics

    • catalog.data.gov
    • data.sfgov.org
    • +1more
    Updated Jun 29, 2025
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    data.sfgov.org (2025). Air Traffic Landings Statistics [Dataset]. https://catalog.data.gov/dataset/air-traffic-landings-statistics
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    Dataset updated
    Jun 29, 2025
    Dataset provided by
    data.sfgov.org
    Description

    A. SUMMARY This dataset consists of San Francisco International Airport (SFO) The aircraft landing dataset contains data about aircraft landings at SFO with monthly landing counts and landed weight by airline, region and aircraft model and type. B. HOW THE DATASET IS CREATED Data is self-reported by airlines and is only available at a monthly level. C. UPDATE PROCESS Data is available starting in July 1999 and will be updated monthly. D. HOW TO USE THIS DATASET Airport data is seasonal in nature; therefore, any comparative analyses should be done on a period-over-period basis (i.e. January 2010 vs. January 2009) as opposed to period-to-period (i.e. January 2010 vs. February 2010). It is also important to note that fact and attribute field relationships are not always 1-to-1. For example, Cargo Statistics belonging to United Airlines will appear in multiple attribute fields and are additive, which provides flexibility for the user to derive categorical Cargo Statistics as desired. E. RELATED DATASETS A summary of monthly comparative air-traffic statistics is also available on SFO’s internet site at https://www.flysfo.com/about/media/facts-statistics/air-traffic-statistics

  9. Data from: Annual Average Daily Traffic

    • gisdata-caltrans.opendata.arcgis.com
    • data.ca.gov
    • +3more
    Updated Sep 30, 2024
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    California_Department_of_Transportation (2024). Annual Average Daily Traffic [Dataset]. https://gisdata-caltrans.opendata.arcgis.com/datasets/d8833219913c44358f2a9a71bda57f76
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    Dataset updated
    Sep 30, 2024
    Dataset provided by
    California Department of Transportationhttp://dot.ca.gov/
    Authors
    California_Department_of_Transportation
    Area covered
    Description

    Annual average daily traffic is the total volume for the year divided by 365 days. The traffic count year is from October 1st through September 30th. Very few locations in California are actually counted continuously. Traffic Counting is generally performed by electronic counting instruments moved from location throughout the State in a program of continuous traffic count sampling. The resulting counts are adjusted to an estimate of annual average daily traffic by compensating for seasonal influence, weekly variation and other variables which may be present. Annual ADT is necessary for presenting a statewide picture of traffic flow, evaluating traffic trends, computing accident rates. planning and designing highways and other purposes.Traffic Census Program Page

  10. O

    Road location and traffic data

    • data.qld.gov.au
    • data.wu.ac.at
    csv, pdf
    Updated Jul 22, 2025
    + more versions
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    Transport and Main Roads (2025). Road location and traffic data [Dataset]. https://www.data.qld.gov.au/dataset/road-location-and-traffic-data
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    pdf(90.5 KiB), pdf(535.5 KiB), csv(290.5 MiB)Available download formats
    Dataset updated
    Jul 22, 2025
    Dataset authored and provided by
    Transport and Main Roads
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    This dataset contains the Department of Transport and Main Roads road location details (both spatial and through distance) as well as associated traffic data.

    It allows users to locate themselves with respect to road section number and through distance using the spatial coordinates on the state-controlled road network.

    Through distance – the distance in kilometres measured from the gazetted start point of the road section.

    Note: "Road location and traffic data" resource has been updated as of June 2025.

  11. Z

    Data from: CESNET-QUIC22: A large one-month QUIC network traffic dataset...

    • data.niaid.nih.gov
    • explore.openaire.eu
    • +1more
    Updated Feb 29, 2024
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    Hynek, Karel (2024). CESNET-QUIC22: A large one-month QUIC network traffic dataset from backbone lines [Dataset]. https://data.niaid.nih.gov/resources?id=zenodo_7409923
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    Dataset updated
    Feb 29, 2024
    Dataset provided by
    Lukačovič, Andrej
    Šiška, Pavel
    Hynek, Karel
    Luxemburk, Jan
    Čejka, Tomáš
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    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} }

  12. C

    Chicago Traffic Tracker - Congestion Estimates by Segments

    • data.cityofchicago.org
    • catalog.data.gov
    csv, xlsx, xml
    Updated Jul 31, 2025
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    City of Chicago (2025). Chicago Traffic Tracker - Congestion Estimates by Segments [Dataset]. https://data.cityofchicago.org/Transportation/Chicago-Traffic-Tracker-Congestion-Estimates-by-Se/n4j6-wkkf
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    xml, csv, xlsxAvailable download formats
    Dataset updated
    Jul 31, 2025
    Dataset authored and provided by
    City of Chicago
    Area covered
    Chicago
    Description

    This dataset contains the current estimated speed for about 1250 segments covering 300 miles of arterial roads. For a more detailed description, please go to https://tas.chicago.gov, click the About button at the bottom of the page, and then the MAP LAYERS tab.

    The Chicago Traffic Tracker estimates traffic congestion on Chicago’s arterial streets (nonfreeway streets) in real-time by continuously monitoring and analyzing GPS traces received from Chicago Transit Authority (CTA) buses. Two types of congestion estimates are produced every ten minutes: 1) by Traffic Segments and 2) by Traffic Regions or Zones. Congestion estimate by traffic segments gives the observed speed typically for one-half mile of a street in one direction of traffic.

    Traffic Segment level congestion is available for about 300 miles of principal arterials. Congestion by Traffic Region gives the average traffic condition for all arterial street segments within a region. A traffic region is comprised of two or three community areas with comparable traffic patterns. 29 regions are created to cover the entire city (except O’Hare airport area). This dataset contains the current estimated speed for about 1250 segments covering 300 miles of arterial roads. There is much volatility in traffic segment speed. However, the congestion estimates for the traffic regions remain consistent for relatively longer period. Most volatility in arterial speed comes from the very nature of the arterials themselves. Due to a myriad of factors, including but not limited to frequent intersections, traffic signals, transit movements, availability of alternative routes, crashes, short length of the segments, etc. speed on individual arterial segments can fluctuate from heavily congested to no congestion and back in a few minutes. The segment speed and traffic region congestion estimates together may give a better understanding of the actual traffic conditions.

  13. S

    Average Daily Traffic

    • data.sanjoseca.gov
    • gisdata-csj.opendata.arcgis.com
    Updated Apr 28, 2025
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    Average Daily Traffic [Dataset]. https://data.sanjoseca.gov/dataset/average-daily-traffic
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    arcgis geoservices rest api, zip, csv, kml, geojson, htmlAvailable download formats
    Dataset updated
    Apr 28, 2025
    Dataset provided by
    City of San José
    Authors
    Enterprise GIS
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    This dataset contains Average Daily Traffic (ADT) counts collected for the City of San Jose for the previous 15 years and is updated on a yearly basis. This dataset can be read as follows: The count location is given as “Collected on ‘Street One’, ‘Direction’, ‘Street Two’, in a ‘Travel Direction.’” ADT values are then given as: ‘ADT One’ and ‘ADT Two’ which correspond to the ADT collected in the recorded travel directions. If the street is a one-way street, a travel direction of ‘one-way’ is recorded and ‘ADT One’ and ‘ADT Two’ are left blank. ‘ADT’ corresponds to the total ADT which is a sum of ‘ADT One’ and ‘ADT Two.’ Putting it all together gets the following: “A total ADT of 39, 057 was recorded on 9/26/2018 along Murphy Rd. east of Oakland Road. Travel flows along Murphy Rd. in an East/West direction with a corresponding ADT One of 21,444 and ADT Two of 17,613.” Note that only counts collected after January 2018 will have a travel direction and corresponding ADT One and ADT Two values listed.

    Data is published on Mondays on a weekly basis.

  14. i

    Gaming Network Traffic Dataset

    • ieee-dataport.org
    Updated Oct 1, 2020
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    Imad Elhajj (2020). Gaming Network Traffic Dataset [Dataset]. https://ieee-dataport.org/open-access/gaming-network-traffic-dataset
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    Dataset updated
    Oct 1, 2020
    Authors
    Imad Elhajj
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    PlayStation 4.

  15. d

    Traffic Signal

    • catalog.data.gov
    • opendata.dc.gov
    • +1more
    Updated Jul 30, 2025
    + more versions
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    City of Washington, DC (2025). Traffic Signal [Dataset]. https://catalog.data.gov/dataset/traffic-signal-a46dd
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    Dataset updated
    Jul 30, 2025
    Dataset provided by
    City of Washington, DC
    Description

    The dataset contains the location and attributes of traffic signals in the District of Columbia. Attributes are aggregated to the controller level (ACISA), where a single controller directs one or more traffic signals at one intersection or clustered intersections. The following attributes of signalized intersections are tracked: leading pedestrian intervals (LPIs), No Turn on Red (NTOR) signage, all-pedestrian phases, bike phases, queue jumps, transit signal priority (TSP), pedestrian recall mode, HAWK/PHB signals, and accessible pedestrian signals (APS).This data is updated periodically and may not be current at the time of viewing. More information on safety features implemented at traffic signals can be found here: https://visionzero.dc.gov/pages/engineering.

  16. d

    Air Traffic Cargo Statistics

    • catalog.data.gov
    • data.sfgov.org
    • +1more
    Updated Jun 29, 2025
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    data.sfgov.org (2025). Air Traffic Cargo Statistics [Dataset]. https://catalog.data.gov/dataset/air-traffic-cargo-statistics
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    Dataset updated
    Jun 29, 2025
    Dataset provided by
    data.sfgov.org
    Description

    A. SUMMARY This dataset consists of San Francisco International Airport (SFO) air traffic cargo dataset contains data about cargo volume into and out of SFO, in both metric tons and pounds, with monthly totals by airline, region and aircraft type. B. HOW THE DATASET IS CREATED Data is self-reported by airlines and is only available at a monthly level. C. UPDATE PROCESS Data is available starting in July 1999 and will be updated monthly. D. HOW TO USE THIS DATASET Airport data is seasonal in nature; therefore, any comparative analyses should be done on a period-over-period basis (i.e. January 2010 vs. January 2009) as opposed to period-to-period (i.e. January 2010 vs. February 2010). It is also important to note that fact and attribute field relationships are not always 1-to-1. For example, Cargo Statistics belonging to United Airlines will appear in multiple attribute fields and are additive, which provides flexibility for the user to derive categorical Cargo Statistics as desired. E. RELATED DATASETS A summary of monthly comparative air-traffic statistics is also available on SFO’s internet site at https://www.flysfo.com/about/media/facts-statistics/air-traffic-statistics

  17. m

    Traffic congestion Dataset

    • data.mendeley.com
    • narcis.nl
    Updated Nov 2, 2020
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    Bedada Bekele (2020). Traffic congestion Dataset [Dataset]. http://doi.org/10.17632/wtp4ssmwsd.1
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    Dataset updated
    Nov 2, 2020
    Authors
    Bedada Bekele
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    The main aim of this dataset is to enable detection of traffic congestion from surveillance cameras using one-stage object detectors. The dataset contains congested and uncongested traffic scenes with their respective labels. This dataset is collected from different surveillance cameras video footage. To prepare the dataset frames are extracted from video sources and resized to a dimension of 500 x 500 with .jpg image format. To Annotate, the image LabelImg software has used. The format of the label is .txt with the same name as the image. The dataset is mainly prepared for YOLO Models but it can be converted to other models format.

  18. Chicago Average Daily Traffic Counts

    • kaggle.com
    zip
    Updated Jul 4, 2019
    + more versions
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    City of Chicago (2019). Chicago Average Daily Traffic Counts [Dataset]. https://www.kaggle.com/chicago/chicago-average-daily-traffic-counts
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    zip(0 bytes)Available download formats
    Dataset updated
    Jul 4, 2019
    Dataset authored and provided by
    City of Chicago
    License

    https://creativecommons.org/publicdomain/zero/1.0/https://creativecommons.org/publicdomain/zero/1.0/

    Area covered
    Chicago
    Description

    Content

    Average Daily Traffic (ADT) counts are analogous to a census count of vehicles on city streets. These counts provide a close approximation to the actual number of vehicles passing through a given location on an average weekday. Since it is not possible to count every vehicle on every city street, sample counts are taken along larger streets to get an estimate of traffic on half-mile or one-mile street segments. ADT counts are used by city planners, transportation engineers, real-estate developers, marketers and many others for myriad planning and operational purposes. Data Owner: Transportation. Time Period: 2006. Frequency: A citywide count is taken approximately every 10 years. A limited number of traffic counts will be taken and added to the list periodically. Related Applications: Traffic Information Interactive Map (http://webapps.cityofchicago.org/traffic/).

    Context

    This is a dataset hosted by the City of Chicago. The city has an open data platform found here and they update their information according the amount of data that is brought in. Explore the City of Chicago using Kaggle and all of the data sources available through the City of Chicago organization page!

    • Update Frequency: This dataset is updated annually.

    Acknowledgements

    This dataset is maintained using Socrata's API and Kaggle's API. Socrata has assisted countless organizations with hosting their open data and has been an integral part of the process of bringing more data to the public.

    Cover photo by Denys Nevozhai on Unsplash
    Unsplash Images are distributed under a unique Unsplash License.

  19. A

    Traffic-Related Data

    • data.boston.gov
    html, pdf
    Updated Mar 25, 2021
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    Boston Transportation Department (2021). Traffic-Related Data [Dataset]. https://data.boston.gov/dataset/traffic-related-data
    Explore at:
    pdf, htmlAvailable download formats
    Dataset updated
    Mar 25, 2021
    Dataset authored and provided by
    Boston Transportation Department
    License

    ODC Public Domain Dedication and Licence (PDDL) v1.0http://www.opendatacommons.org/licenses/pddl/1.0/
    License information was derived automatically

    Description

    Traffic-related data collected by the Boston Transportation Department, as well as other City departments and State agencies. Various types of counts: Turning Movement Counts, Automated Traffic Recordings, Pedestrian Counts, Delay Studies, and Gap Studies.

    ~_Turning Movement Counts (TMC)_ present the number of motor vehicles, pedestrians, and cyclists passing through the particular intersection. Specific movements and crossings are recorded for all street approaches involved with the intersection. This data is used in traffic signal retiming programs and for signal requests. Counts are typically conducted for 2-, 4-, 11-, and 12-Hr periods.

    ~_Automated Traffic Recordings (ATR)_ record the volume of motor vehicles traveling along a particular road, measures of travel speeds, and approximations of the class of the vehicles (motorcycle, 2-axle, large box truck, bus, etc). This type of count is conducted only along a street link/corridor, to gather data between two intersections or points of interest. This data is used in travel studies, as well as to review concerns about street use, speeding, and capacity. Counts are typically conducted for 12- & 24-Hr periods.

    ~_Pedestrian Counts (PED)_ record the volume of individual persons crossing a given street, whether at an existing intersection or a mid-block crossing. This data is used to review concerns about crossing safety, as well as for access analysis for points of interest. Counts are typically conducted for 2-, 4-, 11-, and 12-Hr periods.

    ~_Delay Studies (DEL)_ measure the delay experienced by motor vehicles due to the effects of congestion. Counts are typically conducted for a 1-Hr period at a given intersection or point of intersecting vehicular traffic.

    ~_Gap Studies (GAP)_ record the number of gaps which are typically present between groups of vehicles traveling through an intersection or past a point on a street. This data is used to assess opportunities for pedestrians to cross the street and for analyses on vehicular “platooning”. Counts are typically conducted for a specific 1-Hr period at a single point of crossing.

  20. s

    Traffic Volumes from SCATS Traffic Management System Jul-Dec 2022 DCC -...

    • data.smartdublin.ie
    Updated Dec 31, 2022
    + more versions
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    (2022). Traffic Volumes from SCATS Traffic Management System Jul-Dec 2022 DCC - Dataset - data.smartdublin.ie [Dataset]. https://data.smartdublin.ie/dataset/dcc-scats-detector-volume-jul-dec-2022
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    Dataset updated
    Dec 31, 2022
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Traffic volumes data across Dublin City from the SCATS traffic management system. The Sydney Coordinated Adaptive Traffic System (SCATS) is an intelligent transportation system used to manage timing of signal phases at traffic signals. SCATS uses sensors at each traffic signal to detect vehicle presence in each lane and pedestrians waiting to cross at the local site. The vehicle sensors are generally inductive loops installed within the road. For SCATS junctions locations see: https://data.smartdublin.ie/dataset/traffic-signals-and-scats-sites-locations-dcc NB These are large data files. There would be too many rows for downloading with certain programmes such as Excel. Please choose a software package which can manage such large data files. 3 resources are provided: SCATS Traffic Volumes Data (Monthly) Contained in this report are traffic counts taken from the SCATS traffic detectors located at junctions. The primary function for these traffic detectors is for traffic signal control. Such devices can also count general traffic volumes at defined locations on approach to a junction. These devices are set at specific locations on approaches to the junction but may not be on all approaches to a junction. As there are multiple junctions on any one route, it could be expected that a vehicle would be counted multiple times as it progress along the route. Thus the traffic volume counts here are best used to represent trends in vehicle movement by selecting a specific junction on the route which best represents the overall traffic flows. Information provided: End Time: time that one hour count period finishes. Region: location of the detector site (e.g. North City, West City, etc). Site: this can be matched with the SCATS Sites file to show location Detector: the detectors/ sensors at each site are numbered Sum volume: total traffic volumes in preceding hour Avg volume: average traffic volumes per 5 minute interval in preceding hour All Dates Traffic Volumes Data This file contains daily totals of traffic flow at each site location. SCATS Site Location Data Contained in this report, the location data for the SCATS sites is provided. The meta data provided includes the following; Site id – This is a unique identifier for each junction on SCATS Site description( CAP) – Descriptive location of the junction containing street name(s) intersecting streets Site description (lower) - – Descriptive location of the junction containing street name(s) intersecting streets Region – The area of the city, adjoining local authority, region that the site is located LAT/LONG – Coordinates Disclaimer: the location files are regularly updated to represent the locations of SCATS sites under the control of Dublin City Council. However site accuracy is not absolute. Information for LAT/LONG and region may not be available for all sites contained. It is at the discretion of the user to link the files for analysis and to create further data. Furthermore, detector communication issues or faulty detectors could also result in an inaccurate result for a given period, so values should not be taken as absolute but can be used to indicate trends.

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Analyst-2 (analyst-2.ai) / Inspirient GmbH (inspirient.com), ‘Popular Website Traffic Over Time ’ analyzed by Analyst-2 [Dataset]. https://analyst-2.ai/analysis/kaggle-popular-website-traffic-over-time-62e4/62549059/?iid=003-357&v=presentation

‘Popular Website Traffic Over Time ’ analyzed by Analyst-2

Explore at:
Dataset authored and provided by
Analyst-2 (analyst-2.ai) / Inspirient GmbH (inspirient.com)
License

Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically

Description

Analysis of ‘Popular Website Traffic Over Time ’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/yamqwe/popular-website-traffice on 13 February 2022.

--- Dataset description provided by original source is as follows ---

About this dataset

Background

Have you every been in a conversation and the question comes up, who uses Bing? This question comes up occasionally because people wonder if these sites have any views. For this research study, we are going to be exploring popular website traffic for many popular websites.

Methodology

The data collected originates from SimilarWeb.com.

Source

For the analysis and study, go to The Concept Center

This dataset was created by Chase Willden and contains around 0 samples along with 1/1/2017, Social Media, technical information and other features such as: - 12/1/2016 - 3/1/2017 - and more.

How to use this dataset

  • Analyze 11/1/2016 in relation to 2/1/2017
  • Study the influence of 4/1/2017 on 1/1/2017
  • More datasets

Acknowledgements

If you use this dataset in your research, please credit Chase Willden

Start A New Notebook!

--- Original source retains full ownership of the source dataset ---

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