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.

Feature layer containing authoritative traffic count points for Sioux Falls, South Dakota.The traffic counts listed are 24-hour, weekday, two-directional counts. Traffic counts are normally collected during the summer months, but may be taken any season, as weather permits. The traffic counts are factored by the day of the week as well as by the month of the year to become an Average Annual Daily Total (AADT). Traffic volumes (i.e. count data) can fluctuate depending on the month, week, day of collection; the weather, type of road surface, nearby construction, etc. All of the historical data should be averaged to reflect the "normal" traffic count. More specific count data (time, date, hourly volume) can be obtained from the Sioux Falls Engineering Division at 367-8601.

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.

Here are a few use cases for this project:

1. Autonomous vehicle navigation: Utilize the "Carla traffic dataset" to train self-driving vehicles in detecting vehicles, pedestrians, traffic signs, and traffic lights, enabling them to navigate safely and adhere to traffic regulations.

2. Traffic analysis and management: Implement the dataset to create a smart traffic management system capable of analyzing vehicular and pedestrian movement while adjusting traffic light timings for optimal flow and reduced congestion.

3. Surveillance and security: Integrate the dataset with CCTV cameras and security systems to monitor and detect unusual activities, such as pedestrians or bikers entering restricted areas, as well as violations of traffic rules.

4. Urban planning and infrastructure development: Use the data to analyze pedestrian and vehicle movement patterns, identifying areas requiring improved infrastructure, such as additional bike lanes, crosswalks, or traffic control features.

5. Augmented reality for navigation: Incorporate the "Carla traffic dataset" within AR applications to provide real-time information on traffic conditions, nearby pedestrians, bikers, and traffic signs, enhancing user's navigation and transportation experiences.

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.

Update NotesMar 16 2024, remove spaces in the file and folder names.Mar 31 2024, delete the underscore in the city names with a space (such as San Francisco) in the '02_TransCAD_results' folder to ensure correct data loading by TransCAD (software version: 9.0).Aug 31 2024, add the 'cityname_link_LinkFlows.csv' file in the '02_TransCAD_results' folder to match the link from input data and the link from TransCAD results (LinkFlows) with the same Link_ID.IntroductionThis is a unified and validated traffic dataset for 20 US cities. There are 3 folders for each city.01 Input datathe initial network data obtained from OpenStreetMap (OSM)the visualization of the OSM dataprocessed node / link / od data02 TransCAD results (software version: 9.0)cityname.dbd : geographical network database of the city supported by TransCAD (version 9.0)cityname_link.shp / cityname_node.shp : network data supported by GIS software, which can be imported into TransCAD manually. Then the corresponding '.dbd' file can be generated for TransCAD with a version lower than 9.0od.mtx : OD matrix supported by TransCADLinkFlows.bin / LinkFlows.csv : traffic assignment results by TransCADcityname_link_LinkFlows.csv: the input link attributes with the traffic assignment results by TransCADShortestPath.mtx / ue_travel_time.csv : the traval time (min) between OD pairs by TransCAD03 AequilibraE results (software version: 0.9.3)cityname.shp : shapefile network data of the city support by QGIS or other GIS softwareod_demand.aem : OD matrix supported by AequilibraEnetwork.csv : the network file used for traffic assignment in AequilibraEassignment_result.csv : traffic assignment results by AequilibraEPublicationXu, X., Zheng, Z., Hu, Z. et al. (2024). A unified dataset for the city-scale traffic assignment model in 20 U.S. cities. Sci Data 11, 325. https://doi.org/10.1038/s41597-024-03149-8Usage NotesIf you use this dataset in your research or any other work, please cite both the dataset and paper above.A brief introduction about how to use this dataset can be found in GitHub. More detailed illustration for compiling the traffic dataset on AequilibraE can be referred to GitHub code or Colab code.ContactIf you have any inquiries, please contact Xiaotong Xu (email: kid-a.xu@connect.polyu.hk).

Real Time Traffic Data Market Outlook

The global real-time traffic data market size is anticipated to reach USD 15.3 billion by 2032 from an estimated USD 6.5 billion in 2023, exhibiting a robust CAGR of 10.1% over the forecast period. This substantial growth is driven by the increasing need for efficient traffic management systems and the rising adoption of smart city initiatives worldwide. Governments and commercial entities are investing heavily in advanced technologies to optimize traffic flow and enhance urban mobility, thus fostering market expansion.

The surge in urbanization and the consequent rise in vehicle ownership have led to severe traffic congestion issues in many metropolitan areas. This has necessitated the implementation of real-time traffic data systems that can provide accurate and timely information to manage traffic effectively. With the integration of sophisticated technologies such as IoT, AI, and big data analytics, these systems are becoming more efficient, thereby driving market growth. Furthermore, the growing emphasis on reducing carbon emissions and enhancing road safety is also propelling the adoption of real-time traffic data solutions.

Technological advancements are playing a pivotal role in shaping the real-time traffic data market. Innovations in sensor technology, the proliferation of GPS devices, and the widespread use of mobile data are providing rich sources of real-time traffic information. The ability to integrate data from multiple sources and deliver actionable insights is significantly enhancing traffic management capabilities. Additionally, the development of cloud-based solutions is enabling scalable and cost-effective deployment of traffic data systems, further contributing to market growth.

Another critical growth factor is the increasing investment in smart city projects. Governments across the globe are prioritizing the development of smart transportation infrastructure to improve urban mobility and reduce traffic-related issues. Real-time traffic data systems are integral to these initiatives, providing essential data for optimizing traffic flow, enabling route optimization, and enhancing public transport efficiency. The involvement of private sector players in these projects is also fueling market growth by introducing innovative solutions and fostering public-private partnerships.

The exponential rise in Mobile Data Traffic is another significant factor influencing the real-time traffic data market. As more people rely on smartphones and mobile applications for navigation and traffic updates, the demand for real-time data has surged. Mobile data provides a wealth of information about traffic patterns and congestion levels, enabling more accurate and timely traffic management. The integration of mobile data with other data sources, such as GPS and sensor data, enhances the overall effectiveness of traffic data systems. This trend is particularly evident in urban areas where mobile devices are ubiquitous, and the need for efficient traffic management is critical. The ability to harness mobile data for traffic insights is driving innovation and growth in the market, as companies develop new solutions to leverage this valuable resource.

Regionally, North America and Europe are leading the market due to their early adoption of advanced traffic management technologies and significant investments in smart city projects. However, the Asia Pacific region is expected to witness the highest growth rate over the forecast period, driven by rapid urbanization, increasing vehicle ownership, and growing government initiatives to develop smart transportation infrastructure. Emerging economies in Latin America and the Middle East & Africa are also showing promising growth potential, fueled by ongoing infrastructure development and increasing awareness of the benefits of real-time traffic data solutions.

Component Analysis

The real-time traffic data market by component is segmented into software, hardware, and services. Each component plays a crucial role in the overall functionality and effectiveness of traffic data systems. The software segment includes traffic management software, route optimization software, and other analytical tools that help process and analyze traffic data. The hardware segment comprises sensors, GPS devices, and other data collection tools. The services segment includes installation, maintenance, and consulting services that support the deployment and operation of traffic data systems

MIT Traffic is a dataset for research on activity analysis and crowded scenes. It includes a traffic video sequence of 90 minutes long. It is recorded by a stationary camera. The size of the scene is 720 by 480 and it is divided into 20 clips.

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

This dataset is historical. For recent data, we recommend using https://chicagotraffictracker.com. -- Average Daily Traffic (ADT) counts are analogous to a census count of vehicles on city streets. These counts provide a close approximation to the actual number of vehicles passing through a given location on an average weekday. Since it is not possible to count every vehicle on every city street, sample counts are taken along larger streets to get an estimate of traffic on half-mile or one-mile street segments. ADT counts are used by city planners, transportation engineers, real-estate developers, marketers and many others for myriad planning and operational purposes. Data Owner: Transportation. Time Period: 2006. Frequency: A citywide count is taken approximately every 10 years. A limited number of traffic counts will be taken and added to the list periodically. Related Applications: Traffic Information Interactive Map (http://webapps.cityofchicago.org/traffic/).

A collection of historic traffic count data and guidelines for how to collect new data for Massachusetts Department of Transportation (MassDOT) projects.

PlayStation 4.

This dataset contains Average Daily Traffic (ADT) counts collected for the City of San Jose for the previous 15 years and is updated on a yearly basis. This dataset can be read as follows: The count location is given as “Collected on ‘Street One’, ‘Direction’, ‘Street Two’, in a ‘Travel Direction.’” ADT values are then given as: ‘ADT One’ and ‘ADT Two’ which correspond to the ADT collected in the recorded travel directions. If the street is a one-way street, a travel direction of ‘one-way’ is recorded and ‘ADT One’ and ‘ADT Two’ are left blank. ‘ADT’ corresponds to the total ADT which is a sum of ‘ADT One’ and ‘ADT Two.’ Putting it all together gets the following: “A total ADT of 39, 057 was recorded on 9/26/2018 along Murphy Rd. east of Oakland Road. Travel flows along Murphy Rd. in an East/West direction with a corresponding ADT One of 21,444 and ADT Two of 17,613.” Note that only counts collected after January 2018 will have a travel direction and corresponding ADT One and ADT Two values listed.Data is published on Mondays on a weekly basis.

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.

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.

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.

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.

Live traffic data from Roadway Weather Information System (RWIS) sites in Iowa. Any field of NA or 9999 describes an invalid value being sent from sensor and was excluded for this REST service. This data gets updated every 5 minutes.

This dataset contains the Department of Transport and Main Roads road location details (both spatial and through distance) as well as associated traffic data.

It allows users to locate themselves with respect to road section number and through distance using the spatial coordinates on the state-controlled road network.

Through distance – the distance in kilometres measured from the gazetted start point of the road section.

Note: "Road location and traffic data" resource has been updated as of May 2025.

Traffic Flow Data In Ho Chi Minh City, Viet Nam
This dataset falls under the category Traffic Generating Parameters.
It contains the following data: Traffic flow
This dataset was scouted on 2022-02-10 as part of a data sourcing project conducted by TUMI. License information might be outdated: Check original source for current licensing. The data can be accessed using the following URL / API Endpoint: https://www.kaggle.com/thanhnguyen2612/traffic-flow-data-in-ho-chi-minh-city-viet-nam