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.

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.

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.

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.

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 If the list is outdated, please download the most recent list from the link below.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.

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.

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

OtoMobile dataset is a collection of recordings of failing car components, created by the Interactive Audio Lab at Northwestern University.

OtoMobile consists of 65 recordings of vehicles with failing components, along with annotations. These annotations include the diagnosis of the failing component (one of 12 common automobile issues), the location of the component on the car (front, rear, or wheels), the time during which the sound occurred (while starting, while idling, while driving, while braking, or while turning) the video name and URL, and the start location of the video where the sound was extracted. Excerpts were selected based on the following criteria:

• The diagnosis of the sound coming from the vehicle was provided by either a professional auto mechanic, or someone who had consulted a professional auto mechanic to diagnose the sound.
• At least one second of audio of the problematic vehicle sound was available, during which other noises (e.g., speech) were absent.
• No more than one recording of the same diagnosis was extracted from each video.The selected audio recordings were cropped to contain only the problematic sounds and normalized to all have the same maximum amplitude.

If you find this data useful in your work, please cite the following paper:

Max Morrison and Bryan Pardo, “OtoMechanic: Auditory Automobile Diagnostics via Query-by-Example,” Workshop on Detection and Classification of Acoustic Scenes and Events (DCASE), New York, USA, October 2019.

Contact information:

- Interactive Audio Lab: http://music.eecs.northwestern.edu

- Bryan Pardo pardo@northwestern.edu | http://www.bryanpardo.com

- Max Morrison morrimax@u.northwestern.edu | http://www.maxrmorrison.com

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 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.

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

Analysis of ‘Automobiles Annual Imports and Exports Through Port Authority of NY NJ Maritime Terminals: Beginning 2000’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/d40c1500-1e9c-4e89-a0e8-0485d5edc48f on 27 January 2022.

--- Dataset description provided by original source is as follows ---

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

--- Original source retains full ownership of the source dataset ---

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.

This dataset provides data showing the number of vehicles (including cars, buses, trucks and motorcycles) that pass through each of the bridges and tunnels operated by the MTA each hour of the day. The data is updated weekly.

This dataset contains data from a survey of new-car buying households in 13 US states conducted December 2014 to January 2015. The original study is described in these technical reports:

Kurani, K S., N. Caperello, J. TyreeHageman New Car Buyers' Valuation of Zero-Emission Vehicles: California, Institute of Transportation Studies, University of California, Davis, Research Report UCD-ITS-RR-16-05 (2016). https://escholarship.org/uc/item/28v320rq

Kurani, K.S., N. Caperello, J. TyreeHageman NCST Research Report: Are We Hardwiring Gender Differences into the Market for Plug-in Electric Vehicles? Institute of Transportation Studies, University of California, Davis, Research Report UCD-ITS-RR-18-05 (2018). https://itspubs.ucdavis.edu/publication_detail.php?id=2888

This dataset is associated specifically with a subsequent technical report:

Kurani, K.S. and K. Buch Across Early Policy and Market Contexts Women and Men Show Similar Interest in Electric Vehicles, National Center for Sustainable Transportation, University of California, Davis, Research Report. 2019. https://escholarship.org/uc/item/9zz8n5x5

Data are from households who had a acquired at least one household vehicle as new (rather than used) since January 2008. The questionnaire was administered on-line to households in the following US states: California, Connecticut, Delaware, Maine, Maryland, Massachusetts, New Hampshire, New Jersey, New York, Oregon, Rhode, Island, Vermont, and Washington. Most of these states are so called "ZEV states," i.e., they had adopted California's Zero Emission Vehicle (ZEV) Mandate. Those states that were not ZEV states were included to facilitate regional analysis or because they were otherwise important to the initial launch of retail ZEV sales in 2011. The primary regional analysis was for the Northeast States for Coordinated Air Use Management (NESCAUM). The NESCAUM member states are Connecticut, Maine, Maryland, Massachusetts, New Hampshire, New Jersey, New York, Rhode Island, and Vermont. The total sample size is 5,654 for all states; individual state samples sizes are available in the above referenced, Kurani et al (2016).

Analyses were conducted at the state and regional, i.e., NESCAUM, levels. Thus, there are individual data sets for each state for which there is a state-level analysis (California, Delaware, Maryland, Massachusetts, New Jersey, New York, Oregon, and Washington) and NESCAUM. Data for California are included in this release despite the fact its analysis was previously conducted under a separate study. California serves as the reference case because it has the most supportive policy and market context for ZEVs and its analysis is specifically referenced in the report associated with these data sets.

Since the goal was to produce the best possible analysis for each state or region, there are differences in their data sets. While variable names and codes follow consistent rules across all the data sets, which variables are in the data does vary across states and the NESCAUM region. The data released here are those required to replicate the analyses in the associated report.

For each state and region, data are available in two formats indicated by their file extensions: .jmp and .csv. Files with the .jmp extension are proprietary to the JMP© statistics program from SAS Institute. These files contain the data and as well as information about variable coding, variable values, value ordering, and other information in column notes. In effect, the .jmp files contain the data and the code book. The .csv files are generally accessible for import into a wide variety of analytical software but contain no explanatory notes.

Finally, an annotated version of the on-line questionnaire is available as Appendix F of the original report from California (Kurani et al 2016) cited above. The on-line instrument is customized to each respondent as they complete it. More than simple skip patterns, as respondents answer questions content of subsequent questions is populated with information participants provide. Some of this requires calls to data external to the survey instrument; some of these data are proprietary and some are no longer available. Therefore, no "live" version of the on-line questionnaire from 2014 is maintained. The annotated version and the description of the survey provided in the linked report are provided to assist data users.

While household ownership and purchase of all light-duty passenger cars and trucks approach gender parity, to date zero emission vehicles (ZEVs) are being purchased by far more men than women. Prior analysis of data from California finds no reason based in the prospective interest in ZEVs of female and male respondents why this difference should persist. The present report extends the California analysis to 12 other US states with varying ZEV policy and market contexts.

Among many other contextual, socio-economic, demographic, and attitudinal measures, the survey solicited participants' prospective interest in acquiring an ZEV, that is, their interest in their next new car. Participants then indicated why they were motivated to select a ZEV or what motivated them to not select one. Factor analysis was used to reduce the dimensionality of participants' prior awareness, experience, knowledge, and assessments of ZEVs. Via nominal logistic regression modeling, differences in prospective interest in ZEVs between female and male respondents are examined. Given their prospective interest, the motivations of female and male respondents are compared.

Overall, no difference between female and male participants in prospective interest in a ZEV rises to the level of the observed differences in real markets. Further, the multivariate modeling indicates no statistically significant effect of a sex indicator on prospective interest in ZEVS almost anywhere in these states. Where there is a difference, female participants are estimated to be more likely to choose a ZEV than their male counterparts.

While participants from both sexes tend to give high scores to the same ZEV (de)motivations, differences in their rank orders repeat generalizations from other research. On average, female respondents score environmental motivations higher than do male respondents. On average, interest in "new technology" is more motivating to male than female participants. Conversely, on average female respondents who do not select a ZEV score "unfamiliar technology" more highly than their male counterparts.

Within the variation in policy and market contexts represented by the states in this study, no finding here explains why similar prospective interest among female and male participants in ZEVs from the beginning of 2015 has yet to be turned toward equal participation in ZEV markets. Explanations may lie in factors not modeled here.

Analysis of ‘Freight Cars Moved in Carfloat Operations, Port Authority of NY NJ: Beginning 2009’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/a36acd86-84cf-42fe-ad71-b29057ee9753 on 27 January 2022.

--- Dataset description provided by original source is as follows ---

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

--- Original source retains full ownership of the source dataset ---

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 is collected by DataCluster Labs. To download full dataset or to submit a request for your new data collection needs, please drop a mail to: sales@datacluster.ai This dataset is an extremely challenging set of over 50,000+ original Vehicle images captured and crowdsourced from over 1000+ urban and rural areas, where each image is manually reviewed and verified by computer vision professionals at Datacluster Labs.

Dataset Features

Dataset size : 50,000+ images Captured by : Over 1000+ crowdsource contributors Resolution : 100% images are HD and above (1920x1080 and above) Location : Captured with 1000+ cities accross India Diversity : Various lighting conditions like day, night, varied distances, view points etc. Device used : Captured using mobile phones in 2020-2022 Usage : Vehicle Detection, Automobile detection, Construction vehicle detection, Self driving systems, etc.

Vehicle Classes

Indian Auto Indian Truck Bus Truck Tempo Traveller Tractor Car Two Wheelers

Available Annotation formats COCO, YOLO, PASCAL-VOC, Tf-Record

*To download full datasets or to submit a request for your dataset needs, please drop a mail on sales@datacluster.ai . Visit www.datacluster.ai to know more.

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.

New York Stock Exchange: Index: Dow Jones US Automobiles & Parts Index data was reported at 1,120.650 NA in Feb 2025. This records a decrease from the previous number of 1,477.780 NA for Jan 2025. New York Stock Exchange: Index: Dow Jones US Automobiles & Parts Index data is updated monthly, averaging 968.330 NA from Mar 2024 (Median) to Feb 2025, with 12 observations. The data reached an all-time high of 1,480.710 NA in Dec 2024 and a record low of 786.250 NA in May 2024. New York Stock Exchange: Index: Dow Jones US Automobiles & Parts Index data remains active status in CEIC and is reported by Exchange Data International Limited. The data is categorized under Global Database’s United States – Table US.EDI.SE: New York Stock Exchange: Dow Jones: Monthly.