The Top Websites in the World

How They Change Over Time

About this dataset

This dataset consists of the top 50 most visited websites in the world, as well as the category and principal country/territory for each site. The data provides insights into which sites are most popular globally, and what type of content is most popular in different parts of the world

How to use the dataset

This dataset can be used to track the most popular websites in the world over time. It can also be used to compare website popularity between different countries and categories

Research Ideas

• To track the most popular websites in the world over time
• To see how website popularity changes by region
• To find out which website categories are most popular

Acknowledgements

Dataset by Alexa Internet, Inc. (2019), released on Kaggle under the Open Data Commons Public Domain Dedication and License (ODC-PDDL)

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: df_1.csv | Column name | Description | |:--------------------------------|:---------------------------------------------------------------------| | Site | The name of the website. (String) | | Domain Name | The domain name of the website. (String) | | Category | The category of the website. (String) | | Principal country/territory | The principal country/territory where the website is based. (String) |

The dataset contains information about web requests to a single website. It's a time series dataset, which means it tracks data over time, making it great for machine learning analysis.

SDCC Traffic Congestion Saturation Flow Data for January to June 2023. Traffic volumes, traffic saturation, and congestion data for sites across South Dublin County. Used by traffic management to control stage timings on junctions. It is recommended that this dataset is read in conjunction with the ‘Traffic Data Site Names SDCC’ dataset.A detailed description of each column heading can be referenced below;scn: Site Serial numberregion: A group of Nodes that are operated under SCOOT control at the same common cycle time. Normally these will be nodes between which co-ordination is desirable. Some of the nodes may be double cycling at half of the region cycle time.system: SCOOT STC UTC (UTC-MX)locn: Locationssite: Site numbersday: Days of the week Monday to Sunday. Abbreviations; MO,TU,WE,TH,FR,SA,SU.date: Reflects correct actual Date of when data was collected.start_time: NOTE - Please ignore the date displayed in this column. The actual data collection date is correctly displayed in the 'date' column. The date displayed here is the date of when report was run and extracted from the system, but correctly reflects start time of 15 minute intervals. end_time: End time of 15 minute intervals.flow: A representation of demand (flow) for each link built up over several minutes by the SCOOT model. SCOOT has two profiles:(1) Short – Raw data representing the actual values over the previous few minutes(2) Long – A smoothed average of values over a longer periodSCOOT will choose to use the appropriate profile depending on a number of factors.flow_pc: Same as above ref PC SCOOTcong: Congestion is directly measured from the detector. If the detector is placed beyond the normal end of queue in the street it is rarely covered by stationary traffic, except of course when congestion occurs. If any detector shows standing traffic for the whole of an interval this is recorded. The number of intervals of congestion in any cycle is also recorded.The percentage congestion is calculated from:No of congested intervals x 4 x 100 cycle time in seconds.This percentage of congestion is available to view and more importantly for the optimisers to take into account.cong_pc: Same as above ref PC SCOOTdsat: The ratio of the demand flow to the maximum possible discharge flow, i.e. it is the ratio of the demand to the discharge rate (Saturation Occupancy) multiplied by the duration of the effective green time. The Split optimiser will try to minimise the maximum degree of saturation on links approaching the node.

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.

The census count of vehicles on city streets is normally reported in the form of Average Daily Traffic (ADT) counts. These counts provide a good estimate for the actual number of vehicles on an average weekday at select street segments. Specific block segments are selected for a count because they are deemed as representative of a larger segment on the same roadway. ADT counts are used by transportation engineers, economists, real estate agents, planners, and others professionals for planning and operational analysis. The frequency for each count varies depending on City staff’s needs for analysis in any given area. This report covers the counts taken in our City during the past 12 years approximately.

This traffic-count data is provided by the City of Pittsburgh's Department of Mobility & Infrastructure (DOMI). Counters were deployed as part of traffic studies, including intersection studies, and studies covering where or whether to install speed humps. In some cases, data may have been collected by the Southwestern Pennsylvania Commission (SPC) or BikePGH.

Data is currently available for only the most-recent count at each location.

Traffic count data is important to the process for deciding where to install speed humps. According to DOMI, they may only be legally installed on streets where traffic counts fall below a minimum threshhold. Residents can request an evaluation of their street as part of DOMI's Neighborhood Traffic Calming Program. The City has also shared data on the impact of the Neighborhood Traffic Calming Program in reducing speeds.

Different studies may collect different data. Speed hump studies capture counts and speeds. SPC and BikePGH conduct counts of cyclists. Intersection studies included in this dataset may not include traffic counts, but reports of individual studies may be requested from the City. Despite the lack of count data, intersection studies are included to facilitate data requests.

Data captured by different types of counting devices are included in this data. StatTrak counters are in use by the City, and capture data on counts and speeds. More information about these devices may be found on the company's website. Data includes traffic counts and average speeds, and may also include separate counts of bicycles.

Tubes are deployed by both SPC and BikePGH and used to count cyclists. SPC may also deploy video counters to collect data.

NOTE: The data in this dataset has not updated since 2021 because of a broken data feed. We're working to fix it.

Linear network representing the estimated traffic flows for roads and highways managed by the Ministry of Transport and Sustainable Mobility (MTMD). These flows are obtained using a statistical estimation method applied to data from more than 4,500 collection sites spread over the main roads of Quebec. It includes DJMA (annual average daily flow), DJME (summer average daily flow), DJME (summer average daily flow (June, July, August, September) and DJMH (average daily winter flow (December, January, February, March) as well as other traffic data. It is important to note that these values are calculated for total traffic directions. Interactive map: Some files are accessible by querying an à la carte traffic section with a click (the file links are displayed in the descriptive table that is displayed upon click): • Historical aggregate data (PDF) • Annual reports for permanent sites (PDF and Excel) • Hourly data (hourly average per weekday per month) (Excel) This third party metadata element was translated using an automated translation tool (Amazon Translate).

Update NotesMar 16 2024, remove spaces in the file and folder names.Mar 31 2024, delete the underscore in the city names with a space (such as San Francisco) in the '02_TransCAD_results' folder to ensure correct data loading by TransCAD (software version: 9.0).Aug 31 2024, add the 'cityname_link_LinkFlows.csv' file in the '02_TransCAD_results' folder to match the link from input data and the link from TransCAD results (LinkFlows) with the same Link_ID.IntroductionThis is a unified and validated traffic dataset for 20 US cities. There are 3 folders for each city.01 Input datathe initial network data obtained from OpenStreetMap (OSM)the visualization of the OSM dataprocessed node / link / od data02 TransCAD results (software version: 9.0)cityname.dbd : geographical network database of the city supported by TransCAD (version 9.0)cityname_link.shp / cityname_node.shp : network data supported by GIS software, which can be imported into TransCAD manually. Then the corresponding '.dbd' file can be generated for TransCAD with a version lower than 9.0od.mtx : OD matrix supported by TransCADLinkFlows.bin / LinkFlows.csv : traffic assignment results by TransCADcityname_link_LinkFlows.csv: the input link attributes with the traffic assignment results by TransCADShortestPath.mtx / ue_travel_time.csv : the traval time (min) between OD pairs by TransCAD03 AequilibraE results (software version: 0.9.3)cityname.shp : shapefile network data of the city support by QGIS or other GIS softwareod_demand.aem : OD matrix supported by AequilibraEnetwork.csv : the network file used for traffic assignment in AequilibraEassignment_result.csv : traffic assignment results by AequilibraEPublicationXu, X., Zheng, Z., Hu, Z. et al. (2024). A unified dataset for the city-scale traffic assignment model in 20 U.S. cities. Sci Data 11, 325. https://doi.org/10.1038/s41597-024-03149-8Usage NotesIf you use this dataset in your research or any other work, please cite both the dataset and paper above.A brief introduction about how to use this dataset can be found in GitHub. More detailed illustration for compiling the traffic dataset on AequilibraE can be referred to GitHub code or Colab code.ContactIf you have any inquiries, please contact Xiaotong Xu (email: kid-a.xu@connect.polyu.hk).

AADT represents current (most recent) Annual Average Daily Traffic on sampled road systems. This information is displayed using the Traffic Count Locations Active feature class as of the annual HPMS freeze in January. Historical AADT is found in another table. Please note that updates to this dataset are on an annual basis, therefore the data may not match ground conditions or may not be available for new roadways. Resource Contact: Christy Prentice, Traffic Forecasting & Analysis (TFA), http://www.dot.state.mn.us/tda/contacts.html#TFA

Check other metadata records in this package for more information on Annual Average Daily Traffic Locations Information.


Link to ESRI Feature Service:

Annual Average Daily Traffic Locations in Minnesota: Annual Average Daily Traffic Locations

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).

pNEUMA is an open large-scale dataset of naturalistic trajectories of half a million vehicles that have been collected by a one-of-a-kind experiment by a swarm of drones in the congested downtown area of Athens, Greece. A unique observatory of traffic congestion, a scale an-order-of-magnitude higher than what was not available until now, that researchers from different disciplines around the globe can use to develop and test their own models.

How are the .csv files organized?

For more details about the pNEUMA dataset, please check our website at https://open-traffic.epfl.ch

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. 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.

NOTE: The Historic Traffic Data Dashboard & Feature Hosted Service have been retired.Network operations traffic data from Main Roads Western Australia for 2015 to 2019. The data provided includes data collected on the Perth Metropolitan State Road Network (PMSRN) at 15 minute intervals. The Historic Traffic Data is provided in CSV format per year. Each table has over 34 million rows and can be linked to the M-Links Road Network using the M-Links ID. A data dictionary for M-Links Road Network and the Historic Traffic Data is at the following link:https://bit.ly/2S86uSnNetwork Operations traffic data can also be accessed via the Daily Traffic Data API at the following link: https://bit.ly/34ZsyAK The network operations traffic data provided here is of variable quality and has not been checked, quality assured or manually corrected. An automated process is used to patch over missing or suspect data with the most representative data available within the database. Patches may be reapplied as new data becomes available and patched data may change over time. Note that you are accessing this data pursuant to a Creative Commons (Attribution) Licence which has a disclaimer of warranties and limitation of liability. You accept that the data provided pursuant to the Licence is subject to changes. Pursuant to section 3 of the Licence you are provided with the following notice to be included when you Share the Licenced Material:- “The Commissioner of Main Roads is the creator and owner of the data and Licenced Material, which is accessed pursuant to a Creative Commons (Attribution) Licence, which has a disclaimer of warranties and limitation of liability.”

This table provides location data and summary statistics of each traffic study. The SDOT Traffic Counts group runs studies across the city to collect traffic volumes. Most studies are done with pneumatic tubes, but some come from video systems as well. Use the field study_id to match it with other tables for more detailed information. Data are binned in 15 minute and 60 minute bins in other tables.

LANE_DESIGNATION_CODE_ID 1     Standard 2     Right Turn 3     Left Turn 4     Thru Only 5     Thru + Right Turn 6     Thru + Left Turn 7     Aggregate Element 8     Anomaly / Special Event 9     Unknown 10    0 11    1 12    2 13    3 14    4 15    5 16    6

TRAFFIC_FLOW_DIR_ID: 1 N 2 S 3 E 4 W 5 NE 6 SE 7 SW 8 NW 9 REV 10 UNKNOWN 11 TOTAL

This dataset contains the traffic volume at Traffic Signal locations. It is aggregated into 15 minute time periods and the counts are assigned to each traffic loop detector (per lane) at each traffic signal site. This information is sourced from the SCATS system where a detector is a loop of wire installed into the road surface and is activated when a vehicle passes over it and sends a pulse to the traffic signal. NOTE

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.

NYCDOT's Traffic Management Center (TMC) maintains a map of traffic speed detectors throughout the City. The speed detector themselves belong to various city and state agencies. The Traffic Speeds Map is available on the DOT's website. This data feed contains 'real-time' traffic information from locations where NYCDOT picks up sensor feeds within the five boroughs, mostly on major arterials and highways. NYCDOT uses this information for emergency response and management.

Here's the link to the original dataset.

Network traffic datasets created by Single Flow Time Series Analysis

Datasets were created for the paper: Network Traffic Classification based on Single Flow Time Series Analysis -- Josef Koumar, Karel Hynek, Tomáš Čejka -- which was published at The 19th International Conference on Network and Service Management (CNSM) 2023. Please cite usage of our datasets as:

J. Koumar, K. Hynek and T. Čejka, "Network Traffic Classification Based on Single Flow Time Series Analysis," 2023 19th International Conference on Network and Service Management (CNSM), Niagara Falls, ON, Canada, 2023, pp. 1-7, doi: 10.23919/CNSM59352.2023.10327876.

This Zenodo repository contains 23 datasets created from 15 well-known published datasets which are cited in the table below. Each dataset contains 69 features created by Time Series Analysis of Single Flow Time Series. The detailed description of features from datasets is in the file: feature_description.pdf

In the following table is a description of each dataset file:

The Smart Mobility and Traffic Optimization Dataset integrates data from cyber-physical networks (CPNs) and social networks (SNs) to enhance intelligent traffic management and smart mobility solutions. It includes real-time traffic patterns, vehicle telemetry, ride-sharing demand, public transport efficiency, social media sentiment, and environmental factors.

This dataset is designed to support machine learning models for traffic congestion prediction, mobility optimization, and smart city planning by analyzing key factors such as vehicle density, road occupancy, weather conditions, social media feedback, and emissions data.

Key Features Traffic Data: Vehicle count, speed, road occupancy, and traffic light status. Weather & Accidents: Weather conditions and accident reports impacting congestion. Social Network Sentiment: Public opinions on mobility and congestion from social media. Smart Mobility Factors: Ride-sharing demand, parking availability, and public transport delays. Environmental Impact: CO₂ emissions and pollution levels. Target Variable Traffic Congestion Level: Categorized as Low, Medium, or High, based on traffic density, speed, and road occupancy. This dataset is valuable for urban planners, smart city developers, and AI researchers working on intelligent mobility solutions. 🚦🚗💡

A collection of historic traffic count data and guidelines for how to collect new data for Massachusetts Department of Transportation (MassDOT) projects.