Here are a few use cases for this project:

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.

Public (anonymized) road traffic prediction datasets from Huawei Munich Research Center.

Datasets from a variety of traffic sensors (i.e. induction loops) for traffic prediction. The data is useful for forecasting traffic patterns and adjusting stop-light control parameters, i.e. cycle length, offset and split times.

The dataset contains recorded data from 6 crosses in the urban area for the last 56 days, in the form of flow timeseries, depicted the number of vehicles passing every 5 minutes for a whole day (i.e. 12 readings/h, 288 readings/day, 16128 readings / 56 days).

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.

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

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.

You can also access an API version of this dataset.

TMS

(traffic monitoring system) daily-updated traffic counts API

Important note: due to the size of this dataset, you won't be able to open it fully in Excel. Use notepad / R / any software package which can open more than a million rows.

Data reuse caveats: as per license.

Data quality

statement: please read the accompanying user manual, explaining:

how

 this data is collected identification 

 of count stations traffic 

 monitoring technology monitoring 

 hierarchy and conventions typical 

 survey specification data 

 calculation TMS 

 operation. 

Traffic

monitoring for state highways: user manual

[PDF 465 KB]

The data is at daily granularity. However, the actual update

frequency of the data depends on the contract the site falls within. For telemetry

sites it's once a week on a Wednesday. Some regional sites are fortnightly, and

some monthly or quarterly. Some are only 4 weeks a year, with timing depending

on contractors’ programme of work.

Data quality caveats: you must use this data in

conjunction with the user manual and the following caveats.

The

 road sensors used in data collection are subject to both technical errors and 

 environmental interference.Data 

 is compiled from a variety of sources. Accuracy may vary and the data 

 should only be used as a guide.As 

 not all road sections are monitored, a direct calculation of Vehicle 

 Kilometres Travelled (VKT) for a region is not possible.Data 

 is sourced from Waka Kotahi New Zealand Transport Agency TMS data.For 

 sites that use dual loops classification is by length. Vehicles with a length of less than 5.5m are 

 classed as light vehicles. Vehicles over 11m long are classed as heavy 

 vehicles. Vehicles between 5.5 and 11m are split 50:50 into light and 

 heavy.In September 2022, the National Telemetry contract was handed to a new contractor. During the handover process, due to some missing documents and aged technology, 40 of the 96 national telemetry traffic count sites went offline. Current contractor has continued to upload data from all active sites and have gradually worked to bring most offline sites back online. Please note and account for possible gaps in data from National Telemetry Sites. 

The NZTA Vehicle

Classification Relationships diagram below shows the length classification (typically dual loops) and axle classification (typically pneumatic tube counts),

and how these map to the Monetised benefits and costs manual, table A37,

page 254.

Monetised benefits and costs manual [PDF 9 MB]

For the full TMS

classification schema see Appendix A of the traffic counting manual vehicle

classification scheme (NZTA 2011), below.

Traffic monitoring for state highways: user manual [PDF 465 KB]

State highway traffic monitoring (map)

State highway traffic monitoring sites

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.

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 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.

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.

Annual average daily traffic is the total volume for the year divided by 365 days. The traffic count year is from October 1st through September 30th. Very few locations in California are actually counted continuously. Traffic Counting is generally performed by electronic counting instruments moved from location throughout the State in a program of continuous traffic count sampling. The resulting counts are adjusted to an estimate of annual average daily traffic by compensating for seasonal influence, weekly variation and other variables which may be present. Annual ADT is necessary for presenting a statewide picture of traffic flow, evaluating traffic trends, computing accident rates. planning and designing highways and other purposes.Traffic Census Program Page

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.

a packet sniffer software

Context

Well,the data is taken form the machine hack site.It leads us to the problem of finding the traffic problems in the metro cities. It is also about how to regulate the movement of the cabs so as to get control over the traffic problems.

Content

Modern cities are changing. The rise of vehicular traffic has been changing the design of our cities. It is very important to know how traffic moves in a city and how it changes during different times in a week. Hence it is very important to analyse and gain insights from traffic data. We invite data scientists, analysts and people from all technical interests to analyse the traffic data from Bengaluru. The data gives us some information about how traffic moves from source to destination under various circumstances. The data is sourced from Uber Movement. Uber Movement provides anonymized data from over two billion trips to help urban planning around the world.

Acknowledgements

1. Machine Hack

Inspiration

1. How can we manage day to day traffic ? 2 .How the moments of cabs to be regulated ?
2. Awareness about the Use of public transport.

Traffic volumes; measured and calculated amounts of vehicle traffic that travel the sections of road.

This traffic-count data is provided by the City of Pittsburgh's Department of Mobility & Infrastructure (DOMI). Counters were deployed as part of traffic studies, including intersection studies, and studies covering where or whether to install speed humps. In some cases, data may have been collected by the Southwestern Pennsylvania Commission (SPC) or BikePGH.

Data is currently available for only the most-recent count at each location.

Traffic count data is important to the process for deciding where to install speed humps. According to DOMI, they may only be legally installed on streets where traffic counts fall below a minimum threshhold. Residents can request an evaluation of their street as part of DOMI's Neighborhood Traffic Calming Program. The City has also shared data on the impact of the Neighborhood Traffic Calming Program in reducing speeds.

Different studies may collect different data. Speed hump studies capture counts and speeds. SPC and BikePGH conduct counts of cyclists. Intersection studies included in this dataset may not include traffic counts, but reports of individual studies may be requested from the City. Despite the lack of count data, intersection studies are included to facilitate data requests.

Data captured by different types of counting devices are included in this data. StatTrak counters are in use by the City, and capture data on counts and speeds. More information about these devices may be found on the company's website. Data includes traffic counts and average speeds, and may also include separate counts of bicycles.

Tubes are deployed by both SPC and BikePGH and used to count cyclists. SPC may also deploy video counters to collect data.

NOTE: The data in this dataset has not updated since 2021 because of a broken data feed. We're working to fix it.

This dataset contains the current estimated congestion for the 29 traffic regions. For a 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 (non-freeway 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 10 minutes: 1) by Traffic Segments and 2) by Traffic Regions or Zones. Congestion estimates by traffic segments gives 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).

There is much volatility in traffic segment speed. However, the congestion estimates for the traffic regions remain consistent for a 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.

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).

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.