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.

Description

This dataset contains hourly data on the traffic volume for westbound I-94, a major interstate highway in the US that connects Minneapolis and St Paul, Minnesota. The data was collected by the Minnesota Department of Transportation (MnDOT) from 2012 to 2018 at a station roughly midway between the two cities.

Key Features

• holiday: a categorical variable that indicates whether the date is a US national holiday or a regional holiday (such as the Minnesota State Fair).
• temp: a numeric variable that shows the average temperature in kelvin.
• rain_1h: a numeric variable that shows the amount of rain in mm that occurred in the hour.
• snow_1h: a numeric variable that shows the amount of snow in mm that occurred in the hour.
• clouds_all: a numeric variable that shows the percentage of cloud cover.
• weather_main: a categorical variable that gives a short textual description of the current weather (such as Clear, Clouds, Rain, etc.).
• weather_description: a categorical variable that gives a longer textual description of the current weather (such as light rain, overcast clouds, etc.).
• date_time: a datetime variable that shows the hour of the data collected in local CST time.
• traffic_volume: a numeric variable that shows the hourly I-94 reported westbound traffic volume.

Potential Use Cases

The dataset can be used for regression tasks to predict the traffic volume based on the weather and holiday features. It can also be used for exploratory data analysis to understand the patterns and trends of traffic volume over time and across different conditions.

Here are a few use cases for this project:

1. Traffic Monitoring and Management: Implement the "Traffic" model in city traffic control systems to monitor and quantify the number of cars, trucks, and buses on the road. This information can assist in optimizing traffic light timings, identifying congestion hotspots, and planning future infrastructure projects.

2. Fleet Management and Logistics: Integrate the model into fleet tracking systems for businesses to monitor their truck, bus, and car fleets in real-time. This helps businesses efficiently manage and coordinate vehicle usage, maintenance scheduling, and route optimization.

3. Vehicle Counting for Research and Planning: Implement the model in studies aimed at understanding patterns of vehicle use, such as assessing the impact of public transport initiatives, measuring emissions, or analyzing the need for new infrastructure (e.g., bus lanes or toll roads).

4. Public Transport Performance Monitoring: Use the "Traffic" model to analyze the efficiency and utilization of public transport systems (e.g., buses) by monitoring the frequency, timing, and occupancy of these vehicles on their specific routes, informing service improvements and resource allocation.

5. Traffic Enforcement and Safety: Implement the model in traffic enforcement systems to identify instances of vehicles violating traffic rules, such as buses or trucks using lanes designated for cars only. This can aid in enforcing traffic regulations and improving road safety.

This dataset, containing 2000 rows and 12 columns, is designed for analyzing and managing urban traffic using deep learning techniques. It includes real-time traffic metrics such as timestamp, location ID, traffic volume, average vehicle speed, and counts of different vehicle types (cars, trucks, bikes). Environmental factors like weather conditions, temperature, and humidity are also included, along with indicators for accidents and current traffic signal status. This dataset can be utilized to train models like CNNs and LSTMs, enabling accurate predictions of traffic flow and dynamic adjustments of traffic signals to reduce congestion and improve mobility in urban areas.

The dataset represents synthetic traffic data for a certain location over a one-year period. It includes information about the traffic volume, weather conditions, and special events that may affect traffic.

Features:

Timestamp: The date and time of the observation.Weather: The weather condition at the time of the observation (e.g., Clear, Cloudy, Rain, Snow).

Events: A binary variable indicating whether there was a special event affecting traffic at the time of the observation (True or False).

Traffic Volume: The volume of traffic at the location at the time of the observation.

The dataset is intended for use in analyzing traffic patterns and trends, as well as for developing and testing models related to traffic prediction and management.

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… See the full description on the dataset page: https://huggingface.co/datasets/UniDataPro/real-time-traffic-video-dataset.

Average Annual Daily Traffic data for use with GIS mapping software, databases, and web applications are from Caliper Corporation and contain data on the total volume of vehicle traffic on a highway or road for a year divided by 365 days. The 2026 version contains AADT data collected in 2024.

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

In Bangladesh, people are sadly not very much concerned about traffic rules. This study focuses on traffic flow patterns at two junctions in Dhaka, Shapla Chattar and Notre Dame College. Footover bridges at both junctions were used to collect video data, which captured single-lane and double-lane traffic situations involving different types of vehicles and also pedestrians crossing. The dataset comprises approximately 5774 images extracted from the videos, taken at five different time periods on a weekday. This dataset provides a unique view on traffic situations in Dhaka, Bangladesh, by presenting unstructured traffic environments at two busy consecutive junctions. Monitoring vehicle fitness, examining pedestrian behavior, and measuring vehicle flow are all possible applications. Researchers can use different machine learning techniques in these areas.

This is a public and downloadable traffic data product of statewide average annual daily traffic (AADT) counts. Annual average daily traffic is a measure of transportation system use that represents the typical traffic volume at a location. Standard traffic monitoring AADT calculation methods vary depending on traffic data collection equipment, data type, and frequency. The TxDOT AADT count data in this layer is calculated as 24 hour volume x Axle Correction Factor x Monthly Adjustment Factor, or by traffic monitoring ramp balancing procedures. In a small number of cases, other methods may be used at TxDOT's discretion. TxDOT AADT Traffic Counts Data Dictionary - Overview Updated: Annually in JulySource: Statewide Traffic Analysis and Reporting System IISecurity Level: Public

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 There are limitations with this dataset whereby a maximum of 24 Volume Detectors are available per site. Internal investigations are underway to explore how this could be addressed.

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.

Dataset Overview

This dataset provides detailed information on website traffic, including page views, session duration, bounce rate, traffic source, time spent on page, previous visits, and conversion rate.

Dataset Description

• Page Views: The number of pages viewed during a session.
• Session Duration: The total duration of the session in minutes.
• Bounce Rate: The percentage of visitors who navigate away from the site after viewing only one page.
• Traffic Source: The origin of the traffic (e.g., Organic, Social, Paid).
• Time on Page: The amount of time spent on the specific page.
• Previous Visits: The number of previous visits by the same visitor.
• Conversion Rate: The percentage of visitors who completed a desired action (e.g., making a purchase).

Data Summary

• Total Records: 2000
• Total Features: 7

Key Features

1. Page Views: This feature indicates the engagement level of the visitors by showing how many pages they visit during their session.
2. Session Duration: This feature measures the length of time a visitor stays on the website, which can indicate the quality of the content.
3. Bounce Rate: A critical metric for understanding user behavior. A high bounce rate may indicate that visitors are not finding what they are looking for.
4. Traffic Source: Understanding where your traffic comes from can help in optimizing marketing strategies.
5. Time on Page: This helps in analyzing which pages are retaining visitors' attention the most.
6. Previous Visits: This can be used to analyze the loyalty of visitors and the effectiveness of retention strategies.
7. Conversion Rate: The ultimate metric for measuring the effectiveness of the website in achieving its goals.

Usage

This dataset can be used for various analyses such as:

• Identifying key drivers of engagement and conversion.
• Analyzing the effectiveness of different traffic sources.
• Understanding user behavior patterns and optimizing the website accordingly.
• Improving marketing strategies based on traffic source performance.
• Enhancing user experience by analyzing time spent on different pages.

Acknowledgments

This dataset was generated for educational purposes and is not from a real website. It serves as a tool for learning data analysis and machine learning techniques.

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.

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.

Intersection Traffic Dataset

## Overview

Intersection Traffic Dataset is a dataset for object detection tasks - it contains Cars Truck Motorcycle Human annotations for 9,897 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).

Here is the traffic dataset description in a single paragraph:

The Traffic Dataset provides comprehensive, real-world data on vehicle movement and road conditions collected from various urban and highway environments. It includes timestamped information such as vehicle counts, speeds, lane occupancy, congestion levels, and recorded traffic incidents, captured through traffic cameras, road sensors, and automated monitoring systems. This dataset is designed to support traffic pattern analysis, congestion prediction, transportation planning, and the development of intelligent transportation systems by offering detailed insights into how traffic behaves across different times of day, weather conditions, and roadway types.

Traffic Video Text Data Notes

  Overview

Preparation notes and schema examples for Traffic tasks using Video Text data. Full source material is intentionally not bundled, so provenance and licensing remain explicit. The whole setup fits in a single module on purpose.

  Environment

Item Value

Python

= 3.9

torch 2.x

safetensors any recent

  Intended use

pipeline testing and source adaptation controlled research split… See the full description on the dataset page: https://huggingface.co/datasets/oliviadiaz9/traffic-dataset.

A dataset containing the traffic network information in Los Angeles city from March to Jun 2012. It is used in the traffic forecasting task in Graph Neural Networks.

The source from the paper: https://arxiv.org/abs/2206.09113

The METR-LA dataset contains 02 information: - A adj_METR-LA.pkl: is the graph that contains the physical connection of 207 loop detectors in the traffic network. This dictionary contains 03 elements: the real sensor ID, the mapped sensor to node ID, and the adjacency matrix. - A METR-LA.h5: is the time series that is collected from each sensor in the traffic network over time.

respectively.