The data set contains registered vehicle population count by various criteria such as vehicle class, vehicle status, vechicle make, vehicle model, vehicle year, plate class, plate declaration, county, weight related class and other vehicle decriptors.

Context

The UK government collects and publishes (usually on an annual basis) detailed information about traffic accidents across the country. This information includes, but is not limited to, geographical locations, weather conditions, type of vehicles, number of casualties and vehicle manoeuvres, making this a very interesting and comprehensive dataset for analysis and research.

The creation of this dataset was inspired by the one previously published by Dave Fisher-Hickey. However, this current dataset features the following significant improvements over its predecessor:

• It covers a wider date range of events.
• Most of the coded data variables have been transformed to textual strings using relevant lookup tables, enabling more efficient and "human-readable" analysis.
• It features detailed information about the vehicles involved in the accidents.

Content

The data come from the Open Data website of the UK government, where they have been published by the Department of Transport.

The dataset comprises of two csv files:

• Accident_Information.csv: every line in the file represents a unique traffic accident (identified by the Accident_Index column), featuring various properties related to the accident as columns. Date range: 2005-2017
• Vehicle_Information.csv: every line in the file represents the involvement of a unique vehicle in a unique traffic accident, featuring various vehicle and passenger properties as columns. Date range: 2004-2016

The two above-mentioned files/datasets can be linked through the unique traffic accident identifier (Accident_Index column).

The dataset will keep being updated as more data become available by the Department of Transport.

Acknowledgements

Thanks to Dave Fisher-Hickey for previously publishing, what I consider to be, the first solid and structured version of this dataset on Kaggle.

Also thanks to data.gov.uk for making this information publicly available.

Last but not least, thanks to The Data Lab for allocating me some much needed time to assemble this dataset.

Inspiration

Go crazy using the dataset. Don't go crazy while driving.

The feature class indicates the specific types of motorized vehicles allowed on the designated routes and their seasons of use. The feature class is designed to be consistent with the MVUM (Motor Vehicle Use Map). It is compiled from the GIS Data Dictionary data and NRM Infra tabular data that the administrative units have prepared for the creation of their MVUMs. Only roads with a SYMBOL attribute value of 1, 2, 3, 4, 11, and 12 are Forest Service System roads and contain data concerning their availability for OHV (Off Highway Vehicle) use. This data is published and refreshed on a unit by unit basis as needed. Data for each individual unit must be verified and proved consistent with the published MVUMs prior to publication.The Forest Service's Natural Resource Manager (NRM) Infrastructure (Infra) is the agency standard for managing and reporting information about inventory of constructed features and land units as well as the permits sold to the general public and to partners. Metadata

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/).

We collected and annotated a dataset containing 105,544 annotated vehicle instances from 24700 image frames within seven different videos, sourced online under creative commons license. The video frames are annotated using DarkLabel tool. In the interest of reusability and generalisation of the deep learning model, we consider the diversity within the collected dataset. This diversity includes changes of lighting amongst the video, as well as other factors such as weather conditions, angle of observation, varying speed of the moving vehicles, traffic flow, and road conditions etc. The videos collected obviously include stationary vehicles, to perform the validation of stopped vehicle detection method. It can be noticed that the road conditions (e.g., motorways, city, country roads), directions, data capture timings and camera views, vary in the dataset producing annotated dataset with diversity. the dataset may have several uses such as vehicle detection, vehicle identification, stopped vehicle detection on smart motorways and local roads (smart city applications) and many more.

This dataset shows counts of transactions associated with authorizing vehicles to be used on public roads, commonly referred to as “buying tabs” or “buying tags”. The data includes registration activity by fuel type, county, primary use class, and date. This is comparable to the Fee Distribution Report #13, that is titled "Motor Vehicle Registration By Class and County".

Kenya Road Transport: Number of Motor Vehicles: Registered data was reported at 4,588,770.000 Unit in 2022. This records an increase from the previous number of 4,353,891.000 Unit for 2021. Kenya Road Transport: Number of Motor Vehicles: Registered data is updated yearly, averaging 2,011,972.000 Unit from Dec 2004 (Median) to 2022, with 19 observations. The data reached an all-time high of 4,588,770.000 Unit in 2022 and a record low of 711,142.000 Unit in 2004. Kenya Road Transport: Number of Motor Vehicles: Registered data remains active status in CEIC and is reported by Kenya National Bureau of Statistics. The data is categorized under Global Database’s Kenya – Table KE.TA001: Road Transport: Number of Motor Vehicles: Registered.

Vehicle Information Vehicle Type: This column represents the type of vehicle. Possible values include:

Car: A standard passenger vehicle. Truck: A larger vehicle used for transporting goods. Bus: A vehicle designed to carry multiple passengers. Motorcycle: A two-wheeled motor vehicle. Fuel Type: This column indicates the type of fuel the vehicle uses. Possible values are:

Petrol: Also known as gasoline, a common fuel for internal combustion engines. Diesel: A type of fuel used in diesel engines, often found in larger vehicles like trucks and buses. Electric: Vehicles powered by electric batteries. Hybrid: Vehicles that use a combination of an internal combustion engine and electric propulsion. Engine Size: The size of the vehicle's engine, measured in liters. Larger engines typically produce more power and can lead to higher emissions.

Age of Vehicle: The age of the vehicle in years. Older vehicles may have higher emissions due to wear and tear or outdated technology.

Mileage: The total distance the vehicle has traveled, measured in kilometers or miles. Higher mileage can indicate more wear and potentially higher emissions.

Driving Conditions Speed: The average speed of the vehicle during the measurement period, measured in kilometers per hour (km/h) or miles per hour (mph). Vehicle emissions can vary with speed.

Acceleration: The rate at which the vehicle's speed increases, measured in meters per second squared (m/s²). Rapid acceleration can lead to higher emissions.

Road Type: The type of road the vehicle is driving on. Possible values include:

Highway: Major roads designed for fast travel. City: Urban roads with frequent stops and lower speeds. Rural: Country roads that may have varying conditions. Traffic Conditions: The level of traffic during the measurement period. Possible values include:

Free flow: Minimal traffic, allowing for smooth travel. Moderate: Some traffic, but generally steady movement. Heavy: High traffic, often leading to stop-and-go conditions. Environmental Conditions Temperature: The ambient temperature during the measurement period, measured in degrees Celsius (°C) or Fahrenheit (°F). Temperature can affect engine performance and emissions.

Humidity: The relative humidity of the air during the measurement period, measured as a percentage. Humidity can affect the combustion process and emissions.

Wind Speed: The speed of the wind during the measurement period, measured in meters per second (m/s) or kilometers per hour (km/h). Wind can influence the dispersion of emissions.

Air Pressure: The atmospheric pressure during the measurement period, measured in hectopascals (hPa). Air pressure can affect engine efficiency and emissions.

Emission Data CO2 Emissions: The amount of carbon dioxide emitted by the vehicle, measured in grams per kilometer (g/km). CO2 is a major greenhouse gas contributing to climate change.

NOx Emissions: The amount of nitrogen oxides emitted by the vehicle, measured in grams per kilometer (g/km). NOx contributes to air pollution and can cause respiratory problems.

PM2.5 Emissions: The amount of particulate matter with a diameter of 2.5 micrometers or smaller emitted by the vehicle, measured in grams per kilometer (g/km). PM2.5 can penetrate deep into the lungs and cause health issues.

VOC Emissions: The amount of volatile organic compounds emitted by the vehicle, measured in grams per kilometer (g/km). VOCs contribute to the formation of ground-level ozone and smog.

SO2 Emissions: The amount of sulfur dioxide emitted by the vehicle, measured in grams per kilometer (g/km). SO2 can contribute to acid rain and respiratory problems.

Target Variable Emission Level: This column categorizes the overall emission level of the vehicle into three classes: Low: Vehicles with low emissions. Medium: Vehicles with moderate emissions. High: Vehicles with high emissions. Summary Categorical Features: Vehicle Type, Fuel Type, Road Type, Traffic Conditions, Emission Level. Continuous Numerical Features: Engine Size, Age of Vehicle, Mileage, Speed, Acceleration, Temperature, Humidity, Wind Speed, Air Pressure, CO2 Emissions, NOx Emissions, PM2.5 Emissions, VOC Emissions, SO2 Emissions.

This dataset shows the number of vehicles that have passed under a gantry on that particular day. This dataset does not show trips, it only shows segments. Segments are compiled to make trips. There are 10 gantries on the InterCounty Connector (ICC) and 5 interchanges. The eastbound gantries are 101, 105, 107, 109, 113, and the westbound gantries are 102, 106, 108, 110, 114. The dataset has a column for each gantry going east and west, then a total for each gantry. The ICC is an all electronic tolling road which opened February 2011. The first opening was a partial opening, with only the first interchange being available for use.There was a free period from February 23, 2011 through March 6, 2011. The rest of the ICC opened in November 2011, and there was another free period from November 22, 2011 through December 4, 2011. There are a few days where a low number of traffic passed under gantries (rows 196,198, 269,271...), these were either testing periods or construction vehicles.

This dataset shows the Number Of Cumulative Motor Vehicles Registered By Type, Malaysia, 2000 - 2021. Footnote: 1) Since 2013, bus, taxi and hired car have been combined and known as Public Transport. 2) Commercial vehicle Formerly known as goods vehicles. 3) Other Vehicles - Includes vehicles such as caravans, government & private fire vehicles, driving school vehicles, hearse, vehicle for disabled, government vehicles, local authority vehicles, ambulance and embassy vehicles. 4) 2000-2013 data are updated. Source: Road Transport Department, Malaysia

This annual release provides a snapshot of the number of active vehicle registration counts of light-duty vehicles and medium-duty vehicles by type of vehicle and fuel type, heavy-duty vehicles, buses, and motorcycles and mopeds. Data are obtained from the administrative files from provincial and territorial governments.

The U.S. auto industry sold nearly ************* cars in 2024. That year, total car and light truck sales were approximately ************ in the United States. U.S. vehicle sales peaked in 2016 at roughly ************ units. Pandemic impact The COVID-19 pandemic deeply impacted the U.S. automotive market, accelerating the global automotive semiconductor shortage and leading to a drop in demand during the first months of 2020. However, as demand rebounded, new vehicle supply could not keep up with the market. U.S. inventory-to-sales ratio dropped to its lowest point in February 2022, as Russia's war on Ukraine lead to gasoline price hikes. During that same period, inflation also impacted new and used car prices, pricing many U.S. consumers out of a market with increasingly lower car stocks. Focus on fuel economy The U.S. auto industry had one of its worst years in 1982 when customers were beginning to feel the effects of the 1973 oil crisis and the energy crisis of 1979. Since light trucks would often be considered less fuel-efficient, cars accounted for about ** percent of light vehicle sales back then. Thanks to improved fuel economy for light trucks and cheaper gas prices, this picture had completely changed in 2020. That year, prices for Brent oil dropped to just over ** U.S. dollars per barrel. The decline occurred in tandem with lower gasoline prices, which came to about **** U.S. dollars per gallon in 2020 - and cars only accounted for less than one-fourth of light vehicle sales that year. Four years on, prices are dropping again, after being the highest on record since 1990 in 2022.

i.e.

Context

What would we use this dataset for? Firstly, crime prevention and detection. Following on, so much more else too, like finer traffic control management. Want to make a case to your local council for building that bypass around your village, already?

Content

With a comprehensive set of images of each of the popular vehicle brands, it should be possible to "teach" a machine-learning application to recognize car brands in real time from live video and camera observations. This gives an added attribute to a vehicle-of-interest's registration / license plate, or at least provides some backup information where the license plate could not be read.

These images have all been manually curated to prevent any ambiguities in the ML process, and advertisements and other useless vehicle views (like vehicle interiors and photos of your smiling salesman) have been removed.

Acknowledgements

The data collection process is described in https://github.com/gerritonagoodday/VehicleBrandDatasetScraping and used web scraping from popular car deal websites. I used ScrapingBee to do the web scraping where websites had put up obstacles to prevent web scraping.

If you want to enhance the dataset further or create datasets for other countries besides the UK, you can make a few configuration changes in the Python scripts. You will also need your own API key, at $29 per month. Sign up through this link and get your own API key:

https://www.scrapingbee.com?fpr=nobnose-inc27

Inspiration

Southern Africa has a huge problem with crime, corruption, and wildlife poaching. Much of this crime is committed by government officials, directly or indirectly. The problem to secure convictions has always been to pinpoint the culprits with irrefutable evidence within an already corrupt judiciary system. This could go some way towards achieving this.

This dataset provides 12,730 images of off-road terrain over 44 miles to assist researchers in the space of autonomous driving in making progress for off-road environments. This dataset also includes readings from the accelerometer, gyroscope, magnetometer, GPS, and wheel rotation speed sensor. Further, we include 8 potential roughness labels derived from the vehicle's z-axis acceleration for the subset of images in the dataset which have sufficient sensor data to calculate the image labels and depict clear, visible terrain.

Please consider citing: Gresenz, G., White, J., & Schmidt, D. C. (2021). "An Off-Road Terrain Dataset Including Images Labeled With Measures of Terrain Roughness." Proceedings of the IEEE International Conference in Autonomous Systems, 309-313.

Papers

This dataset is described and published in Gresenz et al. [1].

Content

Data was collected with a mountain bike on off-road trails during five different dates in the late summer and early fall. The bike was equipped with Garmin 830 dual GPS receivers, Garmin Virb Ultra dual high resolution Inertial Measurement Units (IMU's), a Garmin Virb Ultra 4k 30 fps camera, and a Garmin Bike Speed Sensor 2 wheel rotation speed sensor. The camera was time synchronized to both IMU's.

Images were extracted from the videos collected by the camera at 1 second intervals. They are located in the Images folder, sorted into subfolders by the date they were collected, and labeled with their UTC timestamp in order to be used alongside the corresponding sensor data.

Sensor data was collected in a file format called a FIT file. We converted the FIT files to CSVs using tools provided by Garmin [2, 3]. We then created distinct CSVs for each of the major sensor readings and formatted each in a state-based representation, where a single row is labeled by UTC timestamp and contains all relevant readings at that timestamp. Sensor data is located in the SensorData folder and is sorted into subfolders based on the date the data was collected.

The Three D Sensor Adjustment Plugin [3] provided by Garmin calibrates three dimensional readings, meaning that the readings are converted to the conventionally understood units and the x, y, and z-axis readings correspond directly to these axes. Accelerometer and gyroscope readings were calibrated using this plugin. It is important to note that our data did not contain the necessary calibration information to calibrate the magnetometer readings, so these readings are uncalibrated in our dataset.

The ImageLabels folder contains two CSVs for the subset of images which had sufficient sensor data to calculate their labels and depicted a clear, visible path.

tsm_1_labels.csv contains the following labels: 1. The standard deviation of a 1 second sampling of z-axis acceleration readings centered around 5 meters ahead of the image's timestamp, discretized using data visualization. 2. The standard deviation of a 1 second sampling of z-axis acceleration readings centered around 5 meters ahead of the image's timestamp, discretized using k-means clustering with k = 2. 3. The standard deviation of a 1 second sampling of z-axis acceleration readings centered around 5 meters ahead of the image's timestamp, discretized using k-means clustering with k = 3. 4. The standard deviation of a 1 second sampling of z-axis acceleration readings centered around 5 meters ahead of the image's timestamp, discretized using k-means clustering with k = 4.

tsm_2_labels.csv contains the following labels: 5. The standard deviation of a 1 second sampling of z-axis acceleration readings directly ahead of the image's timestamp, discretized using data visualization. 6. The standard deviation of a 1 second sampling of z-axis acceleration readings directly ahead of the image's timestamp, discretized using k-means clustering with k = 2. 7. The standard deviation of a 1 second sampling of z-axis acceleration readings directly ahead of the image's timestamp, discretized using k-means clustering with k = 3. 8. The standard deviation of a 1 second sampling of z-axis acceleration readings directly ahead of the image's timestamp, discretized using k-means clustering with k = 4.

These labeling schemas, along with how effectively they were able to be learned, are described in depth in Gresenz et al. [1].

Links

Check out our other dataset, Off-Road Terrain Attention Region Images.

The Github repo for the papers associated with these datasets is located here.

References

[1] Gresenz, G., White, J., & Schmidt, D.C. (2021). "An Off-Road Terrain Dataset Including Images Labeled With Measures of Terrain Roughness." Proceedings of the IEEE International Conference in Autonomous Systems, 309-31...

15 smart sensors were installed on Mill Road and surrounding streets to record numbers of pedestrians, bicycles, cars and other vehicles. The data being collated and analysed by the Smart Cambridge programme will help the Greater Cambridge Partnership understand how people use the road network.

Data will be released monthly for these locations until the end of 2020. Please note that due to the level of insight that can be gained from these sensors, additional sensors in more locations have been installed in Cambridge since the summer of 2019. Some sensors will remain beyond 2020 in strategic locations and the network is expected to grow. Data for those more permanent sites, outside of the Mill Road project will be published here: https://data.cambridgeshireinsight.org.uk/dataset/cambridge-city-smart-s...

Mill Road Bridge was closed for eight weeks from 1 July 2019 for crucial work being carried out to improve rail services. Pedestrians and cyclists will still be able to cross the railway for most of the working time.

A high concentration of sensors were installed for approximately 18 months to gather data before the closure, during the time when there is no vehicle traffic coming over Mill Road Bridge and then after the bridge is re-opened. This has allowed engineers to see the impact of the closure on surrounding roads, including on air quality. Keeping the sensors in place for this long has also allowed teams to make greater comparisons, by taking in to account daily, weekly, monthly and annual variations in traffic levels.

The below data release offers counts for each sensor over 1 hour periods. The curent data covers the period 03/06/2019 to 13/12/2020.

Hourly counts are broken down by inbound and outbound journeys. .

Counts are also broken down by vehicle type. This includes:

Pedestrians Cyclists Buses LGV OGV 1 OGV 2 The release also includes a full list of sensor sites with geographic point location data.

This data set is sourced from Dundee City Council's Public Space Camera Surveillance System. It shows a count of road vehicles broken down by vehicle classification type in 8 specified areas across Dundee. The data set shows a snapshot of road vehicles within these areas every Monday, Wednesday and Saturday during the period 1pm-2pm. The vehicle classifications shown on the extract are: * Bus * Car * Motorcycle * Pickup Truck * Truck e.g HGV's etc * Van This data is experimental and subject to further refinement. Please note that due the nature of CCTV cameras at times data may not be collected as specified above. Therefore, caution should be exercised when analysing data and drawing conclusions from this data set.