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

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 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.

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.

~150 million nationwide parcels with land use type, building density, housing units, points of interest, and job density.

Replica OD data captures where people travel from and to, why they travel, how they travel, and who is traveling — at daily and seasonal cadences across all US geographies.

Foot traffic and pedestrian activity counts for specific sites, corridors, and areas across the United States.

Roadway, transit, active transportation, and freight network data — including functional classification, intersection attributes, transit routes and stops, and bicycle/pedestrian infrastructure.

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.

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 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.

Historically, research in traffic and incidents has proceeded along two distinct but intrinsically linked tracks. The traffic domain has focused on enhancing deep learning models to incrementally improve prediction accuracy, while the incident track has predominantly concentrated on isolated studies of incident risks and patterns. For the first time, our XTraffic dataset integrates these two tracks both spatially and temporally across a comprehensive regional scale, encompassing 16,972 traffic nodes for the entire year of 2023. The dataset includes detailed time-series data on traffic flow, lane occupancy, and average vehicle speed, as well as meticulously aligned records of incidents across seven different classes, synchronized with the traffic data. Each node also features extensive physical and policy-level meta-attributes of lanes.

Average weekday traffic counts (AWT) are collected at count stations throughout the city and represent the daily average for Monday-Friday traffic volume. Count stations in the eastern section of the city are collected in even numbered years, while those in the city's western section are collected in odd numbered years. Field descriptions/definitions for traffic count data as follows: ObjectID: GIS auto-generated unique identifiermslink: mslink of street segmentsegment_na: street segment nameSTATION: count station numberSOURCE: designates segment with counter or linkedSTATION: volume count station numberSOURCE: volume count station or linked segmentAWT_Count: most recent average weekday traffic countAWT_Yr: year of most recent countShape: GIS geometry typeYear_Txt: year of most recent count (text field)Shape.STLength(): GIS calculated segment length

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.

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.

Description

This is a countrywide traffic congestion dataset that covers 49 states of the USA. The congestion events data were collected from February 2016 to September 2022, using multiple APIs that provide streaming traffic incident (or event) data. These APIs broadcast traffic data captured by various entities, including the US and state departments of transportation, law enforcement agencies, traffic cameras, and traffic sensors within the road networks. The dataset contains approximately 33 million congestion records. We also provide a sampled version of data that includes 2 million events for easier processing and handling for those who prefer to work with a smaller amount of data.

Acknowledgements

If you use this dataset, please kindly cite the following paper:

• Moosavi, Sobhan, Mohammad Hossein Samavatian, Arnab Nandi, Srinivasan Parthasarathy, and Rajiv Ramnath. "Short and long-term pattern discovery over large-scale geo-spatiotemporal data." In Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining, pp. 2905-2913. 2019.

Inspiration

The US Traffic Congestion dataset can be used for numerous applications, such as traffic modeling, simulated routing, identifying traffic hotspot locations, and exploring intrinsic traffic patterns and how they evolve over time.

Missing Data and Update Policy

Please note that the dataset may be missing data for certain days, which could be due to network connectivity issues during data collection. The dataset will not be updated, and this version should be considered the latest.

Usage Policy and Legal Disclaimer

This dataset is being distributed solely for research purposes under the Creative Commons Attribution-Noncommercial-ShareAlike license (CC BY-NC-SA 4.0). By downloading the dataset, you agree to use it only for non-commercial, research, or academic applications. If you use this dataset, it is necessary to cite the paper mentioned above.

Inquiries or need help?

For any inquiries or assistance, please contact Sobhan Moosavi at sobhan.mehr84@gmail.com

Traffic traces from SUMO simulator

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.

Dataset Card for road-traffic

** The original COCO dataset is stored at dataset.tar.gz**

  Dataset Summary

road-traffic

  Supported Tasks and Leaderboards

object-detection: The dataset can be used to train a model for Object Detection.

  Languages

English

  Dataset Structure





  Data Instances

A data point comprises an image and its object annotations. { 'image_id': 15, 'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB… See the full description on the dataset page: https://huggingface.co/datasets/Francesco/road-traffic.