This statistic shows a comparison of webpage traffic sources of Slack and Salesforce in April 2019. According to data collected by GP Bullhound, ninety-six percent of Slack's webpage traffic during the measured period was direct, compared to Salesforce's more mixed traffic strategy.

Jorge Godoy

This statistic depicts how convenience store operators in the United States anticipate their total number of visitors in 2018 will compare to the total number of visitors in 2017. According to the survey, ** percent of respondents believe that store foot traffic will be slightly higher in 2018 compared to 2017.

As of the last quarter of 2023, 31.57 percent of web traffic in the United States originated from mobile devices, down from 49.51 percent in the fourth quarter of 2022. In comparison, over half of web traffic worldwide was generated via mobile in the last examined period.

In 2019, Volume of Road Traffic in Ireland rose 0.2% compared to the previous year.

Provides a comparison table of the top ten accidents at traffic intersections in Kaohsiung City in 109 years

January 2024, Netflix.com generated over 412 million visits in the United States. Traffic to the SVoD platform increased by seven percent compared to the previous month. Overall, Netflix was the leading subscription video-on-demand service in terms of traffic during the examined period. Between the second half of 2022 and the beginning of 2023, search and visit volume trends on streaming sites in the market appeared to have normalized after the usage increase brought by the COVID-19 pandemic in 2020 and 2021.

The advent of self-driving cars has sparked discussions about eye contact in traffic, particularly due to challenges that automated vehicles face in non-verbal communication with human road users. In his 1992 book, Turn Signals Are The Facial Expressions Of Automobiles, Don Norman describes how drivers in Mexico City deliberately avoid eye contact when entering a roundabout to create uncertainty in the minds of other drivers, leading the latter to yield right of way. Norman argued that such manipulative or aggressive behavior would not be tolerated in the United States. In the present study, we tested these claims through an online survey involving 3,857 respondents from 20 countries. The results confirmed that Mexican drivers reported a higher frequency of non-speeding ‘aggressive’ violations compared to those from most other countries. Regarding eye contact in roundabout scenarios, national differences were found not so much in the frequency of eye contact but in the reasons behind its use. Mexican drivers tended to avoid eye contact to reduce tension or avoid conflict with other drivers. However, they also frequently reported making eye contact to assert or subtly enforce their right of way. In higher-income countries like the United States, driver-driver eye contact is often deemed unnecessary. In conclusion, our findings partially correspond with Norman’s anecdote based on his experiences in 1950 s Mexico City. These results may have implications for understanding the stability of traffic cultures and the challenges related to eye contact and non-verbal communication faced by developers of automated vehicles.

This dataset provides values for CONTAINER PORT TRAFFIC TEU 20 FOOT EQUIVALENT UNITS WB DATA.HTML reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

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:

File name Detection problem Citation of original raw dataset

botnet_binary.csv Binary detection of botnet S. García et al. An Empirical Comparison of Botnet Detection Methods. Computers & Security, 45:100–123, 2014.

botnet_multiclass.csv Multi-class classification of botnet S. García et al. An Empirical Comparison of Botnet Detection Methods. Computers & Security, 45:100–123, 2014.

cryptomining_design.csv Binary detection of cryptomining; the design part Richard Plný et al. Datasets of Cryptomining Communication. Zenodo, October 2022

cryptomining_evaluation.csv Binary detection of cryptomining; the evaluation part Richard Plný et al. Datasets of Cryptomining Communication. Zenodo, October 2022

dns_malware.csv Binary detection of malware DNS Samaneh Mahdavifar et al. Classifying Malicious Domains using DNS Traffic Analysis. In DASC/PiCom/CBDCom/CyberSciTech 2021, pages 60–67. IEEE, 2021.

doh_cic.csv Binary detection of DoH

Mohammadreza MontazeriShatoori et al. Detection of doh tunnels using time-series classification of encrypted traffic. In DASC/PiCom/CBDCom/CyberSciTech 2020, pages 63–70. IEEE, 2020

doh_real_world.csv Binary detection of DoH Kamil Jeřábek et al. Collection of datasets with DNS over HTTPS traffic. Data in Brief, 42:108310, 2022

dos.csv Binary detection of DoS Nickolaos Koroniotis et al. Towards the development of realistic botnet dataset in the Internet of Things for network forensic analytics: Bot-IoT dataset. Future Gener. Comput. Syst., 100:779–796, 2019.

edge_iiot_binary.csv Binary detection of IoT malware Mohamed Amine Ferrag et al. Edge-iiotset: A new comprehensive realistic cyber security dataset of iot and iiot applications: Centralized and federated learning, 2022.

edge_iiot_multiclass.csv Multi-class classification of IoT malware Mohamed Amine Ferrag et al. Edge-iiotset: A new comprehensive realistic cyber security dataset of iot and iiot applications: Centralized and federated learning, 2022.

https_brute_force.csv Binary detection of HTTPS Brute Force Jan Luxemburk et al. HTTPS Brute-force dataset with extended network flows, November 2020

ids_cic_binary.csv Binary detection of intrusion in IDS Iman Sharafaldin et al. Toward generating a new intrusion detection dataset and intrusion traffic characterization. ICISSp, 1:108–116, 2018.

ids_cic_multiclass.csv Multi-class classification of intrusion in IDS Iman Sharafaldin et al. Toward generating a new intrusion detection dataset and intrusion traffic characterization. ICISSp, 1:108–116, 2018.

ids_unsw_nb_15_binary.csv Binary detection of intrusion in IDS Nour Moustafa and Jill Slay. Unsw-nb15: a comprehensive data set for network intrusion detection systems (unsw-nb15 network data set). In 2015 military communications and information systems conference (MilCIS), pages 1–6. IEEE, 2015.

ids_unsw_nb_15_multiclass.csv Multi-class classification of intrusion in IDS Nour Moustafa and Jill Slay. Unsw-nb15: a comprehensive data set for network intrusion detection systems (unsw-nb15 network data set). In 2015 military communications and information systems conference (MilCIS), pages 1–6. IEEE, 2015.

iot_23.csv Binary detection of IoT malware Sebastian Garcia et al. IoT-23: A labeled dataset with malicious and benign IoT network traffic, January 2020. More details here https://www.stratosphereips.org /datasets-iot23

ton_iot_binary.csv Binary detection of IoT malware Nour Moustafa. A new distributed architecture for evaluating ai-based security systems at the edge: Network ton iot datasets. Sustainable Cities and Society, 72:102994, 2021

ton_iot_multiclass.csv Multi-class classification of IoT malware Nour Moustafa. A new distributed architecture for evaluating ai-based security systems at the edge: Network ton iot datasets. Sustainable Cities and Society, 72:102994, 2021

tor_binary.csv Binary detection of TOR Arash Habibi Lashkari et al. Characterization of Tor Traffic using Time based Features. In ICISSP 2017, pages 253–262. SciTePress, 2017.

tor_multiclass.csv Multi-class classification of TOR Arash Habibi Lashkari et al. Characterization of Tor Traffic using Time based Features. In ICISSP 2017, pages 253–262. SciTePress, 2017.

vpn_iscx_binary.csv Binary detection of VPN Gerard Draper-Gil et al. Characterization of Encrypted and VPN Traffic Using Time-related. In ICISSP, pages 407–414, 2016.

vpn_iscx_multiclass.csv Multi-class classification of VPN Gerard Draper-Gil et al. Characterization of Encrypted and VPN Traffic Using Time-related. In ICISSP, pages 407–414, 2016.

vpn_vnat_binary.csv Binary detection of VPN Steven Jorgensen et al. Extensible Machine Learning for Encrypted Network Traffic Application Labeling via Uncertainty Quantification. CoRR, abs/2205.05628, 2022

vpn_vnat_multiclass.csv Multi-class classification of VPN Steven Jorgensen et al. Extensible Machine Learning for Encrypted Network Traffic Application Labeling via Uncertainty Quantification. CoRR, abs/2205.05628, 2022

Comparison of traffic parameters for different outer lanes.

DESCRIPTION OF THE RESEARCH AND DATA: This work presents the Madrid Traffic Dataset (MTD), a comprehensive resource for the analysis and modeling of traffic patterns in Madrid. The dataset integrates data from traffic sensors, weather observations, calendar information, road infrastructure, and geolocation data to support advanced studies of urban mobility and predictive modeling.

In addition to the core data sources, the dataset includes temporal sequences and a traffic adjacency matrix, enabling the application of time-series analysis and graph-based modeling approaches.

-COMPLETE DATASET: The complete version of the MTD includes data from 554 traffic sensors distributed across the Madrid region, covering a total of 30 months (from June 2022 to November 2024).

-SUBSET DATASET: A more compact version derived from the complete dataset, focused on a subset of 300 traffic sensors with 17 months of data (from June 2022 to October 2023). This subset is designed for researchers requiring a lighter dataset.

DATA ORGANIZATION: The dataset is organized in a main directory containing a subfolder identified by the configuration data hash. This subfolder includes all key components: datasets, temporal sequences, adjacency matrices, and configuration files. The structure ensures that all resources are clearly arranged to facilitate easy access and reproducibility for researchers.

For more details, see [Submitted to IEEE Internet of the Things Journal].

Across popular online marketplace websites visited by users in Australia in February 2025, temu.com registered the highest growth in its website traffic compared to the previous year, with an annual growth of over 56 percent. In comparison, ebay.com.au saw a decrease in its website traffic compared to the previous year, with an annual decrease of around 11.9 percent.

Only used for code comparison of traffic accident data tables before 105 years, The format of the traffic accident data table was updated after 106 years, and the Chinese comparison has been attached.

The Traffic Camera dataset contains the location and number for every Traffic camera in the City of Toronto. These datasets will be updated within 2 minutes when cameras are added, changed, or removed. The camera list files can be found at: https://opendata.toronto.ca/transportation/tmc/rescucameraimages/Data/ tmcearthcameras.csv - CSV, camera list in CSV tmcearthcameras.json - json formatted list. tmcearthcamerassn.json - json formatted file containing the timestamp of the list files. tmcearthcameras.xml - xml formatted list. TMCEarthCameras.xsd - xml schema document. The dataset includes the number, name, WGS84 information (latitude, longitude), comparison directions (1- Looking North, 2-Looking East, 3-Looking South and 4-Looking West), and camera group. The camera images associated with the dataset can be found at: https://opendata.toronto.ca/transportation/tmc/rescucameraimages/CameraImages. And the comparison images can be found at: https://opendata.toronto.ca/transportation/tmc/rescucameraimages/ComparisonImages. The camera image file name is created as follows: loc####.jpg - where #### is the camera number. (i.e. loc1234.jpg) The camera comparison image file names are created as follows: loc####D.jpg - where #### is the camera number and D is the direction. (i.e. loc1234e.jpg and loc1234w.jpg) The camera images are displayed on the City's website at http://www.toronto.ca/rescu/index.htmor http://www.toronto.ca/rescu/list.htm

This dataset contains the Annual Comparative Statement of Traffic on international Scheduled Services for Last three years. It includes passengers carried, freight carried, mail carried, passenger load factor, and passenger kilometres performed.

This dataset is available on Brisbane City Council’s open data website – data.brisbane.qld.gov.au. The site provides additional features for viewing and interacting with the data and for downloading the data in various formats.

Traffic Volume for Key Brisbane Corridors. Includes traffic volumes, travel times and incidents.

This dataset will no longer be updated. Data is being published in a new format in a new dataset called Traffic Management — Key Corridor — Monthly Performance Report.

Information on Traffic Management is available on the Brisbane City Council website.

This dataset contains the following resources:1. Traffic Volume for Key Brisbane Corridors.

Excel file containing: * 6-Month Average Daily, AM & PM Peak Traffic Volume * Network Daily Traffic Volume Comparison * 6-Month Average AM & PM Peak Travel Time * Network Travel Time Comparison * Incident Data * Note: volume day of the week and TT day of week was discontinued and is not included from Jul-Dec 2015

1. Traffic Volume for Key Brisbane Corridors.

Excel file containing: * 6-Month Average Daily, AM & PM Peak Traffic Volume * Network Daily Traffic Volume Comparison * 6-Month Average AM & PM Peak Travel Time * Network Travel Time Comparison * Incident Data * Average daily traffic volume for each day of the week (veh/day) * Travel time per kilometre by day of the week (mm:ss/km)

Analysis of ‘Daily traffic indicators France and Regions, COVID-19 ’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from http://data.europa.eu/88u/dataset/615eed271fd73f6703435810 on 12 January 2022.

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

Daily Road Traffic Indicators make it possible to compare the road traffic of all vehicles (ITV or All Vehicles Index) or only heavy goods vehicles (IPL or Index Poids Lourds) with a situation before the COVID-19 crisis.

# # Calculation method

They are constructed by comparing current traffic with pre-crisis traffic based on average daily flow rates between 13 January and 2 February 2020.

This period was chosen in order to avoid the effects of school holidays.

‘0’ therefore represents a ‘pre-crisis’ situation and the curves directly give the observed falls (negative index) or traffic increases (positive index).

Traffic indicators at the level of France and regions are calculated on the basis of traffic data of more than 1200 counting stations spread across the unlicensed national road network and 450 stations spread across the national road network as a whole.

# # Dictionary of variables

— ‘Zone’: Geographical area (e.g. Bourgogne-Franche-Comté, France, etc.) — ‘ITV’: All Vehicles Index (between -1 and 1) — ‘IPL’: Weight Lourds index (between -1 and 1) — ‘MGL_ITV’: Rolling average All vehicles (between -1 and 1) — ‘MGL_IPL’: Rolling Average Weight Lourds (between -1 and 1)

# Data-visualisation

image of DataViz

The [dataviz.cerema.fr/trafic-routier] platform (https://dataviz.cerema.fr/trafic-routier) allows you to explore, visualise and analyse the state of traffic in France, day after day

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

France: Number of 20-foot containers passing through the ports: The latest value from 2022 is 6.47 million containers, an increase from 6.4 million containers in 2021. In comparison, the world average is 9.59 million containers, based on data from 86 countries. Historically, the average for France from 2000 to 2022 is 4.75 million containers. The minimum value, 2.92 million containers, was reached in 2000 while the maximum of 6.47 million containers was recorded in 2022.

Comparison of traffic parameters for all the vehicles.