Traffic data from traffic detectors installed on strategic routes / major roads including traffic volume, traffic speed and road occupancy (Raw Data). Traffic speeds from traffic detectors installed on strategic routes / major roads mapped onto the respective road network segments (Processed Data).

Daily utilization metrics for data.lacity.org and geohub.lacity.org. Updated monthly

This dataset contains the San Francisco Traffic dataset used by Lai et al. (2017). It contains 862 hourly time series showing the road occupancy rates on the San Francisco Bay area freeways from 2015 to 2016.

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.

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 a section of traffic à la carte with a click (the file links are displayed in the descriptive table that is displayed when clicking): • Historical aggregated data (PDF) • Annual reports for permanent sites (PDF and Excel) • Hourly data (hourly average per weekday per month) (Excel) • 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).**

Feature layer containing authoritative traffic count segments for Sioux Falls, South Dakota. The traffic counts listed are 24-hour, weekday, two-directional counts. Traffic counts are normally collected during the summer months, but may be taken any season, as weather permits. The traffic counts are factored by the day of the week as well as by the month of the year to become an Average Annual Daily Total (AADT). Traffic volumes (i.e. count data) can fluctuate depending on the month, week, day of collection; the weather, type of road surface, nearby construction, etc. All of the historical data should be averaged to reflect the "normal" traffic count. More specific count data (time, date, hourly volume) can be obtained from the Sioux Falls Engineering Division at 367-8601.

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.

Explore our detailed website traffic dataset featuring key metrics like page views, session duration, bounce rate, traffic source, and conversion rates.

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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.

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.

Traffic Dataset - 500 Videos

Dataset comprises 500 videos of urban traffic captured by surveillance cameras, providing real-time traffic data enriched with bounding box annotations for vehicles and pedestrians. Designed for traffic monitoring and safety research, the dataset supports tasks like vehicle detection, traffic flow analysis, and accident prediction. By leveraging this dataset, researchers and engineers can advance real-time object detection, traffic surveillance systems… See the full description on the dataset page: https://huggingface.co/datasets/UniDataPro/real-time-traffic-video-dataset.

Dataset Traffic

## Overview

Dataset Traffic is a dataset for object detection tasks - it contains Traffic annotations for 681 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

2020-2024 Traffic Count DataThe Sacramento County Department of Transportation's Traffic Count Program collects data regarding the number of vehicles that travel various county roads. Traffic counts are collected utilizing pneumatic hose counters, traffic signal detector loops or by staff field observations. DOT Traffic Count Program

Annual Average Daily Traffic (AADT) is an estimate of the average daily traffic along a defined segment of roadway. This value is calculated from short term counts taken along the same section which are then factored to produce the estimate of AADT. Because of this process, the most recent AADT for any given roadway will always be for the previous year. Data is available for all New York State Routes and roads that are part of the Federal Aid System.

This is a traffic dataset which contains balance size of encrypted malicious and legitimate traffic for encrypted malicious traffic detection. The dataset is a secondary csv feature data which is composed of five public traffic datasets. Our dataset is composed based on three criteria: The first criterion is to combine widely considered public datasets which contain both encrypted malicious and legitimate traffic in existing works, such as the Malwares Capture Facility Project dataset and the CICIDS-2017 dataset. The second criterion is to ensure the data balance, i.e., balance of malicious and legitimate network traffic and similar size of network traffic contributed by each individual dataset. Thus, approximate proportions of malicious and legitimate traffic from each selected public dataset are extracted by using random sampling. We also ensured that there will be no traffic size from one selected public dataset that is much larger than other selected public datasets. The third criterion is that our dataset includes both conventional devices' and IoT devices' encrypted malicious and legitimate traffic, as these devices are increasingly being deployed and are working in the same environments such as offices, homes, and other smart city settings.

Based on the criteria, 5 public datasets are selected. After data pre-processing, details of each selected public dataset and the final composed dataset are shown in “Dataset Statistic Analysis Document”. The document summarized the malicious and legitimate traffic size we selected from each selected public dataset, proportions of selected traffic size from each selected public dataset with respect to the total traffic size of the composed dataset (% w.r.t the composed dataset), proportions of selected encrypted traffic size from each selected public dataset (% of selected public dataset), and total traffic size of the composed dataset. From the table, we are able to observe that each public dataset equally contributes to approximately 20% of the composed dataset, except for CICDS-2012 (due to its limited number of encrypted malicious traffic). This achieves a balance across individual datasets and reduces bias towards traffic belonging to any dataset during learning. We can also observe that the size of malicious and legitimate traffic are almost the same, thus achieving class balance. The datasets now made available were prepared aiming at encrypted malicious traffic detection. Since the dataset is used for machine learning model training, a sample of train and test sets are also provided. The train and test datasets are separated based on 1:4 and stratification is applied during data split. Such datasets can be used directly for machine or deep learning model training based on selected features.

The IDMT-TRAFFIC dataset includes 17,506 2-second long stereo audio excerpts of recorded vehicle passings as well as different background sounds alongside streets. The dataset includes recordings from 4 different recording locations, 4 different vehicle types (bus, car, motorcycle, and truck), three different tempo limit areas, as well as dry and wet weather/road conditions. The direction of movement is annotated as well. Recordings with both high-quality sE8 microphones as well as medium-quality MEMS microphones are included.

Mini Traffic Detection dataset comprises 8 classes with 30 instances each, divided into 70% for training and 30% for validation. Primarily designed for computer vision tasks, it focuses on traffic object detection. It's an excellent choice for transfer learning with Detectron2 for custom object detection and segmentation projects. The dataset includes classes such as bicycle, bus, car, motorcycle, person, traffic_light, truck, and stop_sign.

The City of Perth traffic count provides information about the number of vehicles, speed of travel and peak travel numbers on particular roads within the Perth LGA (Local Government Area). Show full description

Location of traffic counters in York. *Please note that the data published within this dataset is a live API link to CYC's GIS server. Any changes made to the master copy of the data will be immediately reflected in the resources of this dataset.The date shown in the "Last Updated" field of each GIS resource reflects when the data was first published.