This dataset contains the file of vehicle, snowmobile and boat registrations in NYS. Registrations expired more than 2 years are excluded. Records that have a scofflaw, revocation and/or suspension are included with indicators specifying those kinds of records.

TLC authorized For-Hire vehicles that are active. This list is accurate to the date and time represented in the Last Date Updated and Last Time Updated fields. For inquiries about the contents of this dataset, please email licensinginquiries@tlc.nyc.gov.

TLC authorized For-Hire vehicles that are active or inactive. This list is accurate to the date and time represented in the Last Date Updated and Last Time Updated fields. For inquiries about the contents of this dataset, please email licensinginquiries@tlc.nyc.gov.

The Uber Ride Dataset for New York City contains detailed information about every Uber ride in the city. The dataset includes the TLC license number of the HVFHS base or business, the TLC Base License Number of the base that dispatched the trip, the date and time of the trip pick-up and drop-off, the TLC Taxi Zone in which the trip began and ended, the base number of the base that received the original trip request, and the date and time when the passenger requested to be picked up.

The dataset also provides information about the total miles for the passenger trip, the total time in seconds for the passenger trip, the base passenger fare before tolls, tips, taxes, and fees, the total amount of all tolls paid in the trip, the total amount collected in the trip for the Black Car Fund, the total amount collected in the trip for NYS sales tax, the total amount collected in the trip for NYS congestion surcharge, and the airport fee of $2.50 for both drop off and pick up at LaGuardia, Newark, and John F. Kennedy airports.

Moreover, the dataset includes the total amount of tips received from the passenger, the total driver pay (not including tolls or tips and net of commission, surcharges, or taxes), the flag indicating whether the passenger agreed to a shared/pooled ride and whether the passenger shared the vehicle with another passenger who booked separately at any point during the trip.

The dataset also includes information about whether the trip was administered on behalf of the Metropolitan Transportation Authority (MTA), whether the passenger requested a wheelchair-accessible vehicle (WAV), and whether the trip occurred in a wheelchair-accessible vehicle (WAV). This comprehensive dataset can be used for a variety of research and analysis purposes, including traffic patterns, fare analysis, and more.

The datasets are broken down by month and formatted in parquet. To use the parquet formatted files in pandas, there is an example in my notebook in the code section. If you need more details, look at the pdfs in the datasets. The data is originally from https://www.nyc.gov/site/tlc/about/tlc-trip-record-data.page

This is the EVENT data captured from the New York City CV Pilot project that was processed by the independent evaluators at Volpe. Additional data collected and data dictionary are in the attachments. Each EVENT record documents the details of one application warning that occurred on an Aftermarket Safety Device (ASD) in an equipped host vehicle and includes CV messages from a defined recording time both before and after the warning was generated by the host ASD. Messages in the recording time window include the Basic Safety Messages (BSM) of the host vehicle that received the warning, as well as other BSMs received from the warning target equipped vehicle (for V2V applications) or other nearby equipped vehicles. Depending on the application warning type, MAP messages, Signal Phase and Timing (SPaT) messages, and Traveler Information Messages (TIM) that were heard by the host vehicle may also be included in the event record.

This dataset contains the file of vehicle, snowmobile and boat registrations in NYS. Expired registrations are excluded. Records that have a scofflaw, revocation and/or suspension are included with indicators specifying this.

This dataset represents the total number of Automobiles Imported and Exported annually through maritime terminals located within Port Authority property in the Port of New York and New Jersey in vehicle units beginning 2000

Content

This dataset captures the park name, address, and county in which a maufactured home park is located; the number of site capacity and number of occupied sites; and the name and contact number for the park owner/operator. New York State Homes and Community Renewal’s (HCR) Division of Housing and Community Renewal (DHCR) oversees the registration of these parks in accordance with NYS Real Property Law Section 233 sub-section (v.) which requires owners of manufactured home parks with three or more homes register their park with DHCR.

Context

This is a dataset hosted by the State of New York. The state has an open data platform found here and they update their information according the amount of data that is brought in. Explore New York State using Kaggle and all of the data sources available through the State of New York organization page!

• Update Frequency: This dataset is updated annually.

Acknowledgements

This dataset is maintained using Socrata's API and Kaggle's API. Socrata has assisted countless organizations with hosting their open data and has been an integral part of the process of bringing more data to the public.

Cover photo by Willian Justen de Vasconcellos on Unsplash
Unsplash Images are distributed under a unique Unsplash License.

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.

Comprehensive dataset of 7 Mountain cable cars in New York, United States as of June, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.

This dataset contains a one-month sample of flattened EVENT data records from the New York City (NYC) Connected Vehicle (CV) Pilot that have undergone obfuscation of precise time and location details as well as other vehicle identifiers. The full unflattened event data from NYC CV pilot can be found in the ITS Sandbox. Each EVENT record documents the details of one application warning that occurred on an Aftermarket Safety Device (ASD) in an equipped host vehicle and includes CV messages from a defined recording time both before and after the warning was generated by the host ASD. Messages in the recording time window include the Basic Safety Messages (BSM) of the host vehicle that received the warning, as well as other BSMs received from the warning target equipped vehicle (for V2V applications) or other nearby equipped vehicles. Depending on the application warning type, MAP messages, Signal Phase and Timing (SPaT) messages, and Traveler Information Messages (TIM) that were heard by the host vehicle may also be included in the event record.

TLC authorized For-Hire vehicles that are active or inactive. This list is accurate to the date and time represented in the Last Date Updated and Last Time Updated fields.

This dataset provides volume of loaded freight rail cars transported between NY and NJ by New York New Jersey Rail, a carfloat operation owned by the Port Authority of NY & NJ. Total volume, including empty cars, is estimated at twice the volume of loaded. 2013 entry is year-to-date volume at this time

PLEASE NOTE: This dataset, which includes all TLC licensed for-hire vehicles which are in good standing and able to drive, is updated every day in the evening between 4-7pm. Please check the 'Last Update Date' field to make sure the list has updated successfully. 'Last Update Date' should show either today or yesterday's date, depending on the time of day. If the list is outdated, please download the most recent list from the link below. http://www1.nyc.gov/assets/tlc/downloads/datasets/tlc_for_hire_vehicle_active_and_inactive.csv

TLC authorized For-Hire vehicles that are active. This list is accurate to the date and time represented in the Last Date Updated and Last Time Updated fields. For inquiries about the contents of this dataset, please email licensinginquiries@tlc.nyc.gov.

TLC authorized For-Hire vehicles that are active or inactive. This list is accurate to the date and time represented in the Last Date Updated and Last Time Updated fields.

Content

Diesel retail prices weekly average by region dataset provides the weekly average retail diesel prices for New York State and eight New York metropolitan regions in U.S. dollars per gallon. Data is a weekly average from October 2007 through current. Some metropolitan regions begin in 2017.

Average daily retail diesel prices are collected from the American Automobile Association (AAA) Daily Fuel Gauge Report. The AAA Daily Fuel Gauge Report prices are averaged to produce a weekly average retail price for New York State and each metropolitan region.

The New York State metropolitan regions in the dataset are Albany (Albany-Schenectady-Troy), Batavia, Binghamton, Buffalo (Buffalo-Niagara Falls), Dutchess (Dutchess-Putnam), Elmira, Glens Falls, Ithaca, Kingston, Nassau (Nassau-Suffolk), New York City, Rochester, Syracuse, Utica (Utica-Rome), Watertown (Watertown-Fort Drum), and White Plains.

Context

This is a dataset hosted by the State of New York. The state has an open data platform found here and they update their information according the amount of data that is brought in. Explore New York State using Kaggle and all of the data sources available through the State of New York organization page!

• Update Frequency: This dataset is updated weekly.

Acknowledgements

This dataset is maintained using Socrata's API and Kaggle's API. Socrata has assisted countless organizations with hosting their open data and has been an integral part of the process of bringing more data to the public.

In 2023, California had the most automobile registrations: almost 13.2 million such vehicles were registered in the most populous U.S. federal state. California also had the highest number of registered motor vehicles overall: nearly 30.4 million registrations.

The Port Authority of New York and New Jersey quarterly produces a data file and provides information on count of the number of vehicles in the airport public parking lots. This dataset represents the number of cars parked in public parking lots at John F. Kennedy International Airport, LaGuardia Airport, Newark Liberty International Airport, and Stewart International Airport beginning in 2002.

TLC authorized For-Hire vehicles that are active or inactive. This list is accurate to the date and time represented in the Last Date Updated and Last Time Updated fields.

1. Title: 1985 Auto Imports Database

2. Source Information: -- Creator/Donor: Jeffrey C. Schlimmer (Jeffrey.Schlimmer@a.gp.cs.cmu.edu) -- Date: 19 May 1987 -- Sources: 1) 1985 Model Import Car and Truck Specifications, 1985 Ward's Automotive Yearbook. 2) Personal Auto Manuals, Insurance Services Office, 160 Water Street, New York, NY 10038 3) Insurance Collision Report, Insurance Institute for Highway Safety, Watergate 600, Washington, DC 20037

3. Past Usage: -- Kibler,~D., Aha,~D.~W., & Albert,~M. (1989). Instance-based prediction of real-valued attributes. {\it Computational Intelligence}, {\it 5}, 51--57. -- Predicted price of car using all numeric and Boolean attributes -- Method: an instance-based learning (IBL) algorithm derived from a localized k-nearest neighbor algorithm. Compared with a linear regression prediction...so all instances with missing attribute values were discarded. This resulted with a training set of 159 instances, which was also used as a test set (minus the actual instance during testing). -- Results: Percent Average Deviation Error of Prediction from Actual -- 11.84% for the IBL algorithm -- 14.12% for the resulting linear regression equation

4. Relevant Information: -- Description This data set consists of three types of entities: (a) the specification of an auto in terms of various characteristics, (b) its assigned insurance risk rating, (c) its normalized losses in use as compared to other cars. The second rating corresponds to the degree to which the auto is more risky than its price indicates. Cars are initially assigned a risk factor symbol associated with its price. Then, if it is more risky (or less), this symbol is adjusted by moving it up (or down) the scale. Actuarians call this process "symboling". A value of +3 indicates that the auto is risky, -3 that it is probably pretty safe.

   The third factor is the relative average loss payment per insured vehicle year. This value is normalized for all autos within a particular size classification (two-door small, station wagons, sports/speciality, etc...), and represents the average loss per car per year.

   -- Note: Several of the attributes in the database could be used as a "class" attribute.

5. Number of Instances: 205

6. Number of Attributes: 26 total -- 15 continuous -- 1 integer -- 10 nominal

7. Attribute Information:
   Attribute: Attribute Range:

   1. symboling: -3, -2, -1, 0, 1, 2, 3.
   2. normalized-losses: continuous from 65 to 256.
   3. make: alfa-romero, audi, bmw, chevrolet, dodge, honda, isuzu, jaguar, mazda, mercedes-benz, mercury, mitsubishi, nissan, peugot, plymouth, porsche, renault, saab, subaru, toyota, volkswagen, volvo
   4. fuel-type: diesel, gas.
   5. aspiration: std, turbo.
   6. num-of-doors: four, two.
   7. body-style: hardtop, wagon, sedan, hatchback, convertible.
   8. drive-wheels: 4wd, fwd, rwd.
   9. engine-location: front, rear.
   10. wheel-base: continuous from 86.6 120.9.
   11. length: continuous from 141.1 to 208.1.
   12. width: continuous from 60.3 to 72.3.
   13. height: continuous from 47.8 to 59.8.
   14. curb-weight: continuous from 1488 to 4066.
   15. engine-type: dohc, dohcv, l, ohc, ohcf, ohcv, rotor.
   16. num-of-cylinders: eight, five, four, six, three, twelve, two.
   17. engine-size: continuous from 61 to 326.
   18. fuel-system: 1bbl, 2bbl, 4bbl, idi, mfi, mpfi, spdi, spfi.
   19. bore: continuous from 2.54 to 3.94.
   20. stroke: continuous from 2.07 to 4.17.
   21. compression-ratio: continuous from 7 to 23.
   22. horsepower: continuous from 48 to 288.
   23. peak-rpm: continuous from 4150 to 6600.
   24. city-mpg: continuous from 13 to 49.
   25. highway-mpg: continuous from 16 to 54.
   26. price: continuous from 5118 to 45400.

8. Missing Attribute Values: (denoted by "?") Attribute #: Number of instances missing a value:

   1. 41
   2. 2
   3. 4
   4. 4
   5. 2
   6. 2
   7. 4