Traffic congestion and related problems are a common concern in urban areas. Understanding traffic patterns and analyzing data can provide valuable insights for transportation planning, infrastructure development, and congestion management.

What exactly is this dataset and how was it created? it is a valuable resource for studying traffic conditions as it contains information collected by a computer vision model. The model detects four classes of vehicles: cars, bikes, buses, and trucks. The dataset is stored in a CSV file and includes additional columns such as time in hours, date, days of the week, and counts for each vehicle type (CarCount, BikeCount, BusCount, TruckCount). The "Total" column represents the total count of all vehicle types detected within a 15-minute duration.

The dataset is updated every 15 minutes, providing a comprehensive view of traffic patterns over the course of one month. Additionally, the dataset includes a column indicating the traffic situation categorized into four classes: 1-Heavy, 2-High, 3-Normal, and 4-Low. This information can help assess the severity of congestion and monitor traffic conditions at different times and days of the week.

In what cases can it be useful? The dataset is useful in transportation planning, congestion management, and traffic flow analysis. It helps understand vehicle demand, identify congested areas, and inform infrastructure improvements. The dataset enables targeted interventions like signal optimizations and lane adjustments. It allows researchers to study traffic patterns by hour, day, or specific dates and explore correlations with external factors. It supports transportation research on vehicle relationships and traffic behavior. Urban planners can assess traffic impact for zoning and infrastructure decisions. Overall, the dataset empowers stakeholders to make data-driven decisions, enhance urban mobility, and create efficient and sustainable cities.

Is there a new update? Yes, in the next update, the dataset will be expanded to include the speed of the cars. Additionally, the data will not be limited to a single route; instead, it will encompass a traffic intersection. This expansion aims to provide a more comprehensive understanding of traffic dynamics and enable better analysis and decision-making for traffic management. The inclusion of speed data will offer insights into the flow and efficiency of vehicles, further enhancing the dataset's value for transportation planning and congestion management efforts.

Thanks

Deploy a powerful traffic monitoring model trained on a massive 880-image dataset for comprehensive urban surveillance. This project features a pre-trained computer vision model optimized to detect 10 distinct classes, including cars, buses, emergency vehicles, and pedestrians, providing the scale needed for advanced smart city infrastructure.

Ways to Use Traffic Detection Dataset

1. Traffic Flow Analysis: The dataset can be used in machine learning models to analyze traffic flow in cities. It can identify the type of vehicles on the city roads at different times of the day, helping in planning and traffic management.

2. Vehicle Class Based Toll Collection: Toll booths can use this model to automatically classify and charge vehicles based on their type, enabling a more efficient and automated system.

3. Parking Management System: Parking lot owners can use this model to easily classify vehicles as they enter for better space management. Knowing the vehicle type can help assign it to the most suitable parking spot.

4. Traffic Rule Enforcement: The dataset can be used to create a computer vision model to automatically detect any traffic violations like wrong lane driving by different vehicle types, and notify law enforcement agencies.

5. Smart Ambulance Tracking: The system can help in identifying and tracking ambulances and other emergency vehicles, enabling traffic management systems to provide priority routing during emergencies.

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.

The dataset is designed for the study and development of machine learning and intelligent traffic prediction models for congestion classification. This dataset contains structured traffic observations collected using a virtual IoT-based traffic monitoring approach from selected road segments in the Vijay Nagar area, Indore. The dataset is organized into four congestion classes based on traffic delay values: (1) FREE: Low or no traffic delay (2) LIGHT: Minor traffic delay (3) MODERATE: Noticeable traffic delay

(4) HEAVY: Significant traffic congestion

Format: CSV (Comma Separated Values) Type: Spatio-temporal traffic dataset Features: Road ID, Hour, Day, Delay, Congestion Status: Primary dataset (collected and structured for machine learning) Applications: * Multi-class classification of traffic congestion levels * Training machine learning models for intelligent traffic prediction * Development of IoT-based smart traffic monitoring systems * Traffic pattern analysis and congestion forecasting * Smart city and intelligent transportation research

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.

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.

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.

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.

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

Real Time Traffic Data market was valued at $9.84 billion in 2025, projected to reach $25.51 billion by 2034, growing at 11.2% CAGR. Complete market analysis and forecast.

Traffic traces from SUMO simulator

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.

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).

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Explore the Website Traffic Dataset featuring structured analytics data including page views, session duration, bounce rate, traffic sources, user behavior metrics, and conversion rates. Suitable for AI-driven marketing analytics, predictive modeling, and performance optimization.