Overview

This dataset consists of details on daily used Tesla cars sold in United States from Tesla. Data fields include vin, year, model, color, miles, trim, sold price, interior, wheels, features, country, location, metro, state, currency, sold date.

Fields

• vin
• year
• model
• color
• miles
• trim
• sold_price
• interior
• wheels
• features
• country
• location
• metro
• state
• currency
• sold_date

Sample data from May 2022

vin	year	model	color	miles	trim
5YJSA1E27KF308860	2019	ms	WHITE	20891	100D Long Range All-Wheel Drive

sold_price	interior	wheels	features
81900	WHITE	NINETEEN	Pearl White Multi-Coat Paint;19" Silver Slipstream Wheels;Black and White Premium Interior;Full Self-Driving Capability;Smart Air Suspension;Glass Roof;Ultra High Fidelity Sound;HEPA Air Filtration System;Subzero Weather Package;Keyless Entry;Power Liftgate;GPS Enabled Homelink;Dark Ash Wood Décor;Dark Headliner;Infotainment Upgrade;

country	location	metro	state	currency	sold_date
US	Pomona, CA		CA	USD	2022-05-30

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Specifications

• Dataset Format: CSV (comma-separated)
• Update Frequency: Monthly
• Geographic coverage: US

Data Source

From tesla.com

Subscription

• Email

Support

You can reach us at support@saturndatacloud.com for any questions on the dataset.

About Saturn Data

Saturn Data provides data mining solutions from public sources to deliver insights for enterprises and the market. If you are interested in acquiring other datasets or customized data mining service, email us at info@saturndatacloud.com.

Overview

This dataset consists of details on daily Carvana cars sold in United States from Carvana. Data fields include vehicle_id, make, year, model, miles, trim, sold price, discounted sold price, partnered dealership, delivery fee, earliest delivery date, sold date.

Fields

• vehicle_id
• stock_number
• year
• make
• model
• miles
• trim
• sold_price
• discounted_sold_price
• partnered_dealership
• delivery_fee
• earliest_delivery_date
• sold_date

Sample data from August 2022

vehicle_id	stock_number	year	make	model	miles
2388462	2001823541	2016	Ford	Focus	77108

trim	sold_price	discounted_sold_price	partnered_dealership
SE	14590	14590	FALSE

delivery_fee	earliest_delivery_date	sold_date
490	2022-08-11T16:29:53.448Z	8/5/2022

Download Sample Dataset

Specifications

• Dataset Format: CSV (comma-separated)
• Update Frequency: Monthly
• Geographic coverage: US

Data Source

From carvana.com

Subscription

• Email

Support

You can reach us at support@saturndatacloud.com for any questions on the dataset.

About Saturn Data

Saturn Data provides data mining solutions from public sources to deliver insights for enterprises and the market. If you are interested in acquiring other datasets or customized data mining service, email us at info@saturndatacloud.com.

PP4AV is the first public dataset with faces and license plates annotated with driving scenarios. P4AV provides 3,447 annotated driving images for both faces and license plates. For normal camera data, dataset sampled images from the existing videos in which cameras were mounted in moving vehicles, running around the European cities. The images in PP4AV were sampled from 6 European cities at various times of day, including nighttime. This dataset use the fisheye images from the WoodScape dataset to select 244 images from the front, rear, left, and right cameras for fisheye camera data. PP4AV dataset can be used as a benchmark suite (evaluating dataset) for data anonymization models in autonomous driving.

"Data for removing derelict vehicle operations from city streets. Gives disposition (complaints) of derelict vehicles reported to DSNY from 311. For information on how to report an abandoned vehicle, go to: http://www1.nyc.gov/nyc-resources/service/989/abandoned-vehicle.

Related datasets: - https://data.cityofnewyork.us/City-Government/Derelict-Vehicle-Dispositions-Tow/vr8p-8shw - https://data.cityofnewyork.us/City-Government/Derelict-Vehicles-Dispositions-Complaints/pq5i-thsu

The main dataset is a 304 MB file of trajectory data (I90_94_stationary_final.csv) that contains position, speed, and acceleration data for small and large automated (L2) vehicles and non-automated vehicles on a highway in an urban environment. Supporting files include aerial reference images for six distinct data collection “Runs” (I90_94_Stationary_Run_X_ref_image.png, where X equals 1, 2, 3, 4, 5, and 6). Associated centerline files are also provided for each “Run” (I-90-stationary-Run_X-geometry-with-ramps.csv). In each centerline file, x and y coordinates (in meters) marking each lane centerline are provided. The origin point of the reference image is located at the top left corner. Additionally, in each centerline file, an indicator variable is used for each lane to define the following types of road sections: 0=no ramp, 1=on-ramps, 2=off-ramps, and 3=weaving segments. The number attached to each column header is the numerical ID assigned for the specific lane (see “TGSIM – Centerline Data Dictionary – I90_94Stationary.csv” for more details). The dataset defines six northbound lanes using these centerline files. Twelve different numerical IDs are used to define the six northbound lanes (1, 2, 3, 4, 5, 6, 10, 11, 12, 13, 14, and 15) depending on the run. Images that map the lanes of interest to the numerical lane IDs referenced in the trajectory dataset are stored in the folder titled “Annotation on Regions.zip”. Lane IDs are provided in the reference images in red text for each data collection run (I90_94_Stationary_Run_X_ref_image_annotated.jpg, where X equals 1, 2, 3, 4, 5, and 6).

This dataset was collected as part of the Third Generation Simulation Data (TGSIM): A Closer Look at the Impacts of Automated Driving Systems on Human Behavior project. During the project, six trajectory datasets capable of characterizing human-automated vehicle interactions under a diverse set of scenarios in highway and city environments were collected and processed. For more information, see the project report found here: https://rosap.ntl.bts.gov/view/dot/74647. This dataset, which is one of the six collected as part of the TGSIM project, contains data collected using the fixed location aerial videography approach with one high-resolution 8K camera mounted on a helicopter hovering over a short segment of I-94 focusing on the merge and diverge points in Chicago, IL. The altitude of the helicopter (approximately 213 meters) enabled the camera to capture 1.3 km of highway driving and a major weaving section in each direction (where I-90 and I-94 diverge in the northbound direction and merge in the southbound direction). The segment has two off-ramps and two on-ramps in the northbound direction. All roads have 88 kph (55 mph) speed limits. The camera captured footage during the evening rush hour (4:00 PM-6:00 PM CT) on a cloudy day. During this period, two SAE Level 2 ADAS-equipped vehicles drove through the segment, entering the northbound direction upstream of the target section, exiting the target section on the right through I-94, and attempting to perform a total of three lane-changing maneuvers (if safe to do so). These vehicles are indicated in the dataset.

As part of this dataset, the following files were provided:

• I90_94_stationary_final.csv contains the numerical data to be used for analysis that includes vehicle level trajectory data at every 0.1 second. Vehicle type, width, and length are provided with instantaneous location, speed, and acceleration data. All distance measurements (width, length, location) were converted from pixels to meters using the following conversion factor: 1 pixel = 0.3-meter conversion.
• I90_94_Stationary_Run_X_ref_image.png are the aerial reference images that define the geographic region for each run X.
• I-90-stationary-Run_X-geometry-with-ramps.csv contain the coordinates that define the lane centerlines for each Run X. The "x" and "y" columns represent the horizontal and vertical locations (in meters) in the reference image, respectively. The "ramp" columns define the type of roadway segment (0=no ramp, 1=on-ramps, 2=off-ramps, and 3=weaving segments). In total, the centerline files define six northbound lanes (using twelve numerical IDs depending on the run).
• Annotation on Regions.zip, which includes images that visually map physical lanes (I90_94_Stationary_Run_X_ref_image_annotated.jpg, where X equals 1, 2, 3, 4, 5, and 6) to their associated numerical lane IDs for each run.

"Provides data on abandoned vehicles on city streets that did not meet DSNY guidelines to be classified as derelict and that were tagged and reported to the NYPD Rotation Tow (ro-tow) Program for towing. For information on how to report an abandoned vehicle, go to: http://www1.nyc.gov/nyc-resources/service/989/abandoned-vehicle Related datasets: - https://data.cityofnewyork.us/City-Government/Derelict-Vehicles-Dispositions-Complaints/pq5i-thsu - https://data.cityofnewyork.us/City-Government/Derelict-Vehicle-Dispositions-Vehicles/bjuu-44hx

The Motor Vehicle Collisions crash table contains details on the crash event. Each row represents a crash event. The Motor Vehicle Collisions data tables contain information from all police reported motor vehicle collisions in NYC. The police report (MV104-AN) is required to be filled out for collisions where someone is injured or killed, or where there is at least $1000 worth of damage (https://www.nhtsa.gov/sites/nhtsa.dot.gov/files/documents/ny_overlay_mv-104an_rev05_2004.pdf). It should be noted that the data is preliminary and subject to change when the MV-104AN forms are amended based on revised crash details.For the most accurate, up to date statistics on traffic fatalities, please refer to the NYPD Motor Vehicle Collisions page (updated weekly) or Vision Zero View (updated monthly).

Due to success of the CompStat program, NYPD began to ask how to apply the CompStat principles to other problems. Other than homicides, the fatal incidents with which police have the most contact with the public are fatal traffic collisions. Therefore in April 1998, the Department implemented TrafficStat, which uses the CompStat model to work towards improving traffic safety. Police officers complete form MV-104AN for all vehicle collisions. The MV-104AN is a New York State form that has all of the details of a traffic collision. Before implementing Trafficstat, there was no uniform traffic safety data collection procedure for all of the NYPD precincts. Therefore, the Police Department implemented the Traffic Accident Management System (TAMS) in July 1999 in order to collect traffic data in a uniform method across the City. TAMS required the precincts manually enter a few selected MV-104AN fields to collect very basic intersection traffic crash statistics which included the number of accidents, injuries and fatalities. As the years progressed, there grew a need for additional traffic data so that more detailed analyses could be conducted. The Citywide traffic safety initiative, Vision Zero started in the year 2014. Vision Zero further emphasized the need for the collection of more traffic data in order to work towards the Vision Zero goal, which is to eliminate traffic fatalities. Therefore, the Department in March 2016 replaced the TAMS with the new Finest Online Records Management System (FORMS). FORMS enables the police officers to electronically, using a Department cellphone or computer, enter all of the MV-104AN data fields and stores all of the MV-104AN data fields in the Department’s crime data warehouse. Since all of the MV-104AN data fields are now stored for each traffic collision, detailed traffic safety analyses can be conducted as applicable.

A digital record of all Tesla fires - including cars and other products, e.g. Tesla MegaPacks - that are corroborated by news articles or confirmed primary sources. Latest version hosted at https://www.tesla-fire.com.

This dataset shows all active tow truck companies licensed by the NYC Department of Consumer and Worker Protection (DCWP) and the tow company's enrollment status in the Directed Accident Response Program (DARP) and/or the Rotation Tow Program (ROTOW).

In New York City, licensed tow companies enrolled in DARP or ROTOW may tow vehicles without the vehicle owner's consent.

Tow companies enrolled in DARP may tow vehicles that have been involved in an accident and cannot safely be driven under their own power.

Tow companies enrolled in ROTOW may tow stolen or abandoned vehicles that have been recovered, and when a vehicle is blocking driveways.

A business may apply to participate in DARP and ROTOW after holding a Tow Truck Company license for more than one year.