This dataset is a structured collection of traffic data extracted from video footage, designed to support machine learning and data analysis projects. It includes attributes such as vehicle counts, average speed, time taken to cross frames, and vehicle types. The dataset is well-suited for traffic prediction, clustering, and classification tasks.

Key Features: Frame-wise traffic data, including counts of cars, trucks, bikes, and buses. Calculated features such as average speed, crossing time, and total vehicles. Supports tasks like PCA, regression, clustering, and classification. Extracted using YOLOv8 for object detection and tracking. Applications: Predict traffic density for smart traffic management systems. Analyze traffic patterns and vehicle distributions. Implement clustering and PCA to identify meaningful patterns in traffic data. Train machine learning models for real-time traffic monitoring. This dataset provides a foundational resource for researchers and developers working on traffic-related machine learning and computer vision projects.

Overview

The Traffic System Dataset provides a collection of key metrics for analyzing and optimizing traffic flow patterns across multiple lanes and intersections. It can be used for predictive modeling, congestion analysis, and intelligent traffic control system design. This dataset includes real-time measurements such as vehicle counts, average speed, lane occupancy, and waiting time, making it ideal for researchers, data scientists, and urban mobility engineers working on smart city and transportation analytics.

Context

Efficient traffic management is one of the most crucial challenges in modern urban planning. With the growth of smart cities, AI and data-driven solutions have become essential for monitoring traffic flow, predicting congestion, and reducing waiting times. This dataset captures multiple time-based and performance-related parameters of a traffic system, providing a foundation for: • Predictive traffic control systems • Vehicle flow optimization • Intelligent transportation system (ITS) modeling • Reinforcement learning applications in traffic light scheduling

Dataset Features

Feature Name Description vehicle_count Number of vehicles passing through a specific observation point during a given time interval. average_speed Mean speed (in km/h or mph) of all vehicles detected in the observation period. lane_occupancy Percentage of lane space occupied by vehicles, indicating traffic density. flow_rate Rate of vehicle flow per unit time (e.g., vehicles per minute). time_of_day Time label or categorical feature representing different traffic periods (e.g., morning peak, afternoon, evening). waiting_time Average waiting time (in seconds) for vehicles during signal cycles or congestion periods.

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.

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, and intelligent transportation solutions.- Get the data

Each frame is meticulously labeled in COCO-style JSON format, enabling seamless integration with object detection and tracking pipelines.With its focus on two primary object categories - cars and pedestrians - this dataset serves as a practical resource for developing and testing object detection and tracking algorithms in traffic scenarios.

💵 Buy the Dataset: This is a limited preview of the data. To access the full dataset, please contact us at https://unidata.pro to discuss your requirements and pricing options.

1920×1080 resolution across all frames facilitates reliable model training and evaluation for applications such as traffic flow optimization, pedestrian safety systems, and autonomous vehicle development.

🌐 UniData provides high-quality datasets, content moderation, data collection and annotation for your AI/ML projects

Network-level vehicle miles traveled data supporting climate and transportation performance monitoring across all US geographies.

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.

This dataset contains a collection of traffic videos captured in various traffic scenarios. The videos are designed to facilitate research and development in fields like traffic management, transportation systems, autonomous vehicles, and computer vision.

Each video captures real-world traffic dynamics, including vehicles, pedestrians, traffic lights, and road signs. The dataset is ideal for applications such as object detection, traffic flow analysis, and pedestrian behavior modeling.

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 table provides the traffic studies in hourly bins and some statistics. 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 information.

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: 08/01/2026.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

Proportion of trips by auto, transit, walking, biking, and other modes for corridors and geographies across the United States.

This is static historical dataset. New York City Department of Transportation (NYC DOT) uses Automated Traffic Recorders (ATR) to collect traffic sample volume counts at bridge crossings and roadways. These counts do not cover the entire year, and the number of days counted per location may vary from year to year.

For a current feed of Automated Traffic Volume Counts, see: https://data.cityofnewyork.us/Transportation/Automated-Traffic-Volume-Counts/7ym2-wayt

respectively.

Yolov8 Traffic

## Overview

Yolov8 Traffic is a dataset for object detection tasks - it contains Car Truck Bus Motobike Bike annotations for 1,502 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 [Public Domain license](https://creativecommons.org/licenses/Public Domain).

The Traffic-Net dataset, released in the version 1.0, contains 4,400 images of sparse traffic, dense traffic, accident, and fire. This dataset can be used for various computer vision tasks, including object detection, image classification, and segmentation.

The images in the dataset are of varying sizes and resolutions, and were collected from different sources, including Google Images, Bing Images, and Flickr. The dataset is divided into four classes, each with a distinct set of images and labels:

1. Sparse traffic: This class contains images of traffic signs and signals in low-traffic areas, such as rural roads and small towns.

2. Dense traffic: This class contains images of traffic signs and signals in high-traffic areas, such as urban roads and highways.

3. Accident: This class contains images of traffic accidents and related objects, such as damaged cars and emergency services.

4. Fire: This class contains images of fire-related objects, such as burning vehicles and buildings.

Researchers and developers can use the Traffic-Net dataset to train and evaluate their own models for traffic sign recognition and related tasks. The dataset can also be used to benchmark existing models and compare their performance on this specific dataset.

This table provides traffic studies in 15 minutes bins and some statistics. 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 information.

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

Here are a few use cases for this project:

1. Smart Traffic Management: The "traffic" computer vision model can be used in intelligent traffic management to detect and categorize various traffic participants. It would help in real-time traffic control by adjusting traffic light patterns based on the density and types of vehicles on the road.

2. Autonomous Vehicles Navigation: Self-driving cars or drones could benefit from this model by identifying and classifying different elements in their path such as other cars, people, buses, 2-wheelers, etc. This would enhance their ability for safe and efficient navigation.

3. Pedestrian Safety: This model can be utilized in pedestrians' mobile applications to alert them about incoming vehicles such as trucks, vans, autos, buses, or 2-wheelers while they are crossing the road or walking on the pavement.

4. Security Surveillance Systems: In commercial or residential zones, the model could assist in accurately identifying and logging vehicle types or detecting anomalies like a person in a vehicle-restricted area, potentially enhancing security measures.

5. Retail & Marketing Research: Stores selling vehicle-related products or services might use this model to monitor the types of vehicles in their parking lots as a form of market research. This data could help them tailor their products, services, or marketing strategies accordingly.

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.

Driving behavior signals and crash records supporting road safety analysis, vulnerable road user exposure assessment, and high-risk location identification.