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 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.

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.

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.

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

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.

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.

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.

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.

a packet sniffer software

Traffic traces from SUMO simulator

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.

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.

D.C.

Location of traffic counters in York. *Please note that the data published within this dataset is a live API link to CYC's GIS server. Any changes made to the master copy of the data will be immediately reflected in the resources of this dataset.The date shown in the "Last Updated" field of each GIS resource reflects when the data was first published.

Traffic volumes data across Dublin City from the SCATS traffic management system. The Sydney Coordinated Adaptive Traffic System (SCATS) is an intelligent transportation system used to manage timing of signal phases at traffic signals. SCATS uses sensors at each traffic signal to detect vehicle presence in each lane and pedestrians waiting to cross at the local site. The vehicle sensors are generally inductive loops installed within the road. 3 resources are provided: SCATS Traffic Volumes Data (Monthly) Contained in this report are traffic counts taken from the SCATS traffic detectors located at junctions. The primary function for these traffic detectors is for traffic signal control. Such devices can also count general traffic volumes at defined locations on approach to a junction. These devices are set at specific locations on approaches to the junction but may not be on all approaches to a junction. As there are multiple junctions on any one route, it could be expected that a vehicle would be counted multiple times as it progress along the route. Thus the traffic volume counts here are best used to represent trends in vehicle movement by selecting a specific junction on the route which best represents the overall traffic flows. Information provided: End Time: time that one hour count period finishes. Region: location of the detector site (e.g. North City, West City, etc). Site: this can be matched with the SCATS Sites file to show location Detector: the detectors/ sensors at each site are numbered Sum volume: total traffic volumes in preceding hour Avg volume: average traffic volumes per 5 minute interval in preceding hour All Dates Traffic Volumes Data This file contains daily totals of traffic flow at each site location. SCATS Site Location Data Contained in this report, the location data for the SCATS sites is provided. The meta data provided includes the following; Site id – This is a unique identifier for each junction on SCATS Site description( CAP) – Descriptive location of the junction containing street name(s) intersecting streets Site description (lower) - – Descriptive location of the junction containing street name(s) intersecting streets Region – The area of the city, adjoining local authority, region that the site is located LAT/LONG – Coordinates Disclaimer: the location files are regularly updated to represent the locations of SCATS sites under the control of Dublin City Council. However site accuracy is not absolute. Information for LAT/LONG and region may not be available for all sites contained. It is at the discretion of the user to link the files for analysis and to create further data. Furthermore, detector communication issues or faulty detectors could also result in an inaccurate result for a given period, so values should not be taken as absolute but can be used to indicate trends.

Here are a few use cases for this project:

1. Smart Traffic Management: The "traffic" computer vision model can be used in intelligent traffic management to detect and categorize various traffic participants. It would help in real-time traffic control by adjusting traffic light patterns based on the density and types of vehicles on the road.

2. Autonomous Vehicles Navigation: Self-driving cars or drones could benefit from this model by identifying and classifying different elements in their path such as other cars, people, buses, 2-wheelers, etc. This would enhance their ability for safe and efficient navigation.

3. Pedestrian Safety: This model can be utilized in pedestrians' mobile applications to alert them about incoming vehicles such as trucks, vans, autos, buses, or 2-wheelers while they are crossing the road or walking on the pavement.

4. Security Surveillance Systems: In commercial or residential zones, the model could assist in accurately identifying and logging vehicle types or detecting anomalies like a person in a vehicle-restricted area, potentially enhancing security measures.

5. Retail & Marketing Research: Stores selling vehicle-related products or services might use this model to monitor the types of vehicles in their parking lots as a form of market research. This data could help them tailor their products, services, or marketing strategies accordingly.

Largest publicly available multi-city traffic dataset with 23,541 stationary detectors across 40 cities worldwide. Rich source for traffic dynamics research and urban mobility studies.

This foot traffic dataset provides GPS-based mobile movement signals from across South America. It is ideal for retailers, city agencies, advertisers, and real estate professionals seeking insights into how people move through physical locations and urban spaces.

Each record includes:

Device ID (IDFA or GAID) Timestamps (in milliseconds and readable format) GPS coordinates (lat/lon) Country code Horizontal accuracy (85%) Optional IP address, mobile carrier, and device model

Access the data via polygon queries (up to 10,000 tiles), and receive files in CSV, JSON, or Parquet, delivered hourly or daily via API, AWS S3, or Google Cloud. Data freshness is strong (95% delivered within 3 days), with full historical backfill available from September 2024.

This solution supports flexible credit-based pricing and is privacy-compliant under GDPR and CCPA.

Key Attributes:

Custom POI or polygon query capability Backfilled GPS traffic available across LATAM High-resolution movement with daily/hourly cadence GDPR/CCPA-aligned with opt-out handling Delivery via API or major cloud platforms

Use Cases:

Competitive benchmarking across malls or stores Transport and infrastructure planning Advertising attribution for outdoor/DOOH campaigns Footfall modeling for commercial leases City zoning, tourism, and planning investments Telecom & tower planning across developing corridors

Tomtom Traffic Api
This dataset falls under the category Individual Transport Traffic Control Systems.
It contains the following data: Traffic flow and Traffic incident on road
This dataset was scouted on 2022-02-05 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://developer.tomtom.com/traffic-api/documentation/product-information/introduction