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-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.

This dataset is designed for urban traffic flow prediction and includes temporal, spatial, and categorical features essential for analyzing traffic patterns.

Key Features: Timestamp: Records the exact date and time in 15-minute intervals, enabling the modeling of temporal dependencies. Location: Identifies the traffic sensor locations (e.g., Sensor_01, Sensor_02), capturing spatial variability. Vehicle_Count: Represents the number of vehicles detected by sensors during each interval. Vehicle_Speed: Measures the average speed of vehicles in km/h, indicating traffic conditions. Congestion_Level: An ordinal variable representing traffic congestion on a scale (e.g., 0 for no congestion, 5 for high congestion). Peak_Off_Peak: Categorical data distinguishing between peak and off-peak hours for better contextual analysis. Target_Vehicle_Count: The predicted vehicle count for the subsequent time interval, serving as the target variable for predictive modeling. Data Overview: Rows: 200 Columns: 7 Temporal Coverage: 2 days and 15 minutes intervals, providing high-resolution data for short-term prediction.

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.

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.

NYCDOT's Traffic Management Center (TMC) maintains a map of traffic speed detectors throughout the City. The speed detector themselves belong to various city and state agencies. The Traffic Speeds Map is available on the DOT's website. This data feed contains 'real-time' traffic information from locations where NYCDOT picks up sensor feeds within the five boroughs, mostly on major arterials and highways. NYCDOT uses this information for emergency response and management.

Here's the link to the original dataset.

a packet sniffer software

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.

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).

Introduction: Traffic congestion remains a significant challenge in urban environments, and optimizing traffic signals plays a crucial role in easing traffic flow. This dataset is designed to aid researchers and developers working on intelligent traffic management systems. It provides comprehensive data collected from three different sources, each offering unique insights into vehicle detection and traffic patterns. Dataset's collection strategy: Kaggle Data Collection 1.Source: Curated datasets from Kaggle, including well-known vehicle detection collections. 2.Content: Contains images and labels of vehicles such as cars, buses, and bikes. 3.Purpose: Provides standardized data for baseline testing and model comparisons. 4.Format: Images in JPEG format with associated YOLO compatible label files (.txt). Link: https://www.kaggle.com/datasets/tubasiddiqui/toy-cars-annotated-on-yolo-format

Custom Data Collection 1.Source: Synthetic and toy vehicle images created in controlled conditions. 2.Content: Features miniature models of cars, buses, and motorcycles. 3.Purpose: Ensures a controlled environment for initial model training and testing, simulating various lighting and angle conditions. 4.Format: JPEG images with YOLO annotation files. Real-Environment Data (Skardu City) 1.Source: Collected from various locations in Skardu city. 2.Content: Real-world images capturing vehicles in diverse scenarios, including intersections, narrow streets, and busy roads. 3.Purpose: Provides data reflecting real traffic conditions, environmental variations, and vehicle diversity, crucial for training robust models. 4.Format: High-resolution JPEG images with detailed annotation files.

Potential Applications Traffic Signal Optimization: Train machine learning models to adjust traffic signals dynamically based on real-time vehicle detection. Autonomous Vehicle Navigation: Use real-world data to enhance the perception systems of self-driving cars. Traffic Flow Analysis: Analyze congestion patterns and develop predictive models for traffic management. Smart City Initiatives: Develop solutions to improve urban mobility and reduce traffic-related issues. How to Use the Dataset 1.Download: interested user can download the dataset from this platform. 2.Training: Use the YOLO compatible images and labels to train object detection models. 3.Testing and Validation: Validate your models on real-world data to assess performance under varying conditions. Acknowledgments We thank the team involved in data collection across Skardu city and the community contributions from Kaggle. This dataset aims to facilitate advancements in smart traffic systems and support innovative solutions for traffic management. Contribute Feedback and contributions are welcome! Let's collaborate to improve and expand this dataset for future research and practical applications.

Dataset from Land Transport Authority. For more information, visit https://data.gov.sg/datasets/d_3136f317a1f282a33fe7a2f6a907c047/view

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

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

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.

The Smart Mobility and Traffic Optimization Dataset integrates data from cyber-physical networks (CPNs) and social networks (SNs) to improve traffic management and smart mobility solutions. By combining real-time traffic patterns, vehicle telemetry, ride-sharing demand, public transport efficiency, social media sentiment, and environmental factors, this dataset provides a comprehensive foundation for optimizing urban mobility.

Designed to support machine learning models, the dataset enables accurate predictions of traffic congestion, mobility optimization, and smart city planning. It incorporates key metrics such as vehicle density, road occupancy, weather conditions, social media feedback, and emissions data to generate actionable insights.

Key Features: Traffic Data: Includes vehicle count, speed, road occupancy, and traffic light status, offering a granular view of real-time traffic conditions. Weather & Accidents: Integrates weather conditions and accident reports to assess their impact on congestion levels. Social Network Sentiment: Analyzes public opinions and complaints about mobility and congestion, extracted from social media platforms. Smart Mobility Factors: Examines ride-sharing demand, parking availability, and public transport delays, aiding in urban mobility planning. Environmental Impact: Monitors CO₂ emissions and pollution levels, ensuring eco-friendly traffic optimization. Target Variable: The dataset categorizes traffic congestion levels into three main groups: Low, Medium, or High, based on real-time traffic density, speed, and road occupancy.

This dataset is an essential resource for urban planners, smart city developers, and AI researchers, empowering them to create intelligent mobility solutions that reduce congestion, enhance efficiency, and improve overall urban sustainability.

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.

elcome to the cutting-edge world of traffic prediction! This Kaggle dataset is a goldmine for data enthusiasts, machine learning aficionados, and urban planning visionaries. In this treasure trove of information, you'll find a comprehensive collection of real-world traffic data meticulously curated for your analytical prowess.

Unravel the complexities of urban mobility as you delve into diverse parameters such as historical traffic patterns, weather conditions, special events, and more. Whether you're a seasoned data scientist or a curious novice, this dataset offers a unique opportunity to fine-tune your predictive models and contribute to the future of transportation efficiency.

Harness the power of data to foresee traffic bottlenecks, optimize city planning, and revolutionize the way we navigate our urban landscapes. Join the community of innovators working towards a seamlessly connected and intelligently managed transportation ecosystem. The road to the future starts here—take the wheel and drive progress with the Traffic Prediction Dataset on Kaggle!

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 an à la carte traffic section with a click (the file links are displayed in the descriptive table that is displayed upon click): • Historical aggregate data (PDF) • 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).

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.

This dataset contains estimates of the average number of vehicles that used roads throughout the City of Detroit. Each record indicates the Annual Average Daily Traffic (AADT) and Commercial Annual Average Daily Traffic (CAADT) for a road segment, where the road segment is located, and other characteristics. This data is derived from Michigan Department of Transportation's (MDOT) Open Data Portal. SEMCOG was the source for speed limits and number of lanes.The primary measure, Annual Average Daily Traffic (AADT), is the estimated mean daily traffic volume for all types of vehicles. Commercial Annual Average Daily Traffic (CAADT) is the estimated mean daily traffic volume for commercial vehicles, a subset of vehicles included in the AADT. The Route ID is an identifier for each road in Detroit (e.g., Woodward Ave). Routes are divided into segments by features such as cross streets, and Location ID's are used to uniquely identify those segments. Along with traffic volume, each record also states the number of lanes, the posted speed limit, and the type of road (e.g., Trunkline or Ramp) based on the Federal Highway Administration (FHWA) functional classification system.According to MDOT's Traffic Monitoring Program a commercial vehicle would be anything Class 4 and up in the FHWA vehicle classification system. This includes vehicles such as buses, semi-trucks, and personal recreational vehicles (i.e., RVs or campers). Methods used to determine traffic volume vary by site, and may rely on continuous monitoring or estimates based on short-term studies. Approaches to vehicle classification similarly vary, depending on the equipment used at a site, and may consider factors such as vehicle weight and length between axles.For more information, please visit MDOT Traffic Monitoring Program.