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.

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 If the list is outdated, please download the most recent list from the link below.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

The Motor Vehicle Collisions vehicle table contains details on each vehicle involved in the crash. Each row represents a motor vehicle involved in a crash. The data in this table goes back to April 2016 when crash reporting switched to an electronic system. 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. 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 containing 2 verified Mountain cable car businesses in New York, United States with complete contact information, ratings, reviews, and location data.

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.

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

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.

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

NYC 311's mission is to provide the public with quick and easy access to all New York City government services and information while offering the best customer service. Each day, NYC311 receives thousands of requests related to several hundred types of non-emergency services, including noise complaints, plumbing issues, and illegally parked cars. These requests are received by NYC311 and forwarded to the relevant agencies such as the police, buildings, or transportation. The agency responds to the request, addresses it, and then closes it.

NYC Traffic Cams

## Overview

NYC Traffic Cams is a dataset for object detection tasks - it contains Cars Trucks Bikes Pedestrians annotations for 526 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

This dataset is about book subjects. It has 1 row and is filtered where the books is Hard coal and coal cars : hauling anthracite on the New York, Ontario & Western Railway. It features 10 columns including number of authors, number of books, earliest publication date, and latest publication date.

Parking Violations Issuance datasets contain violations issued during the respective fiscal year. The Issuance datasets are not updated to reflect violation status, the information only represents the violation(s) at the time they are issued. Since appearing on an issuance dataset, a violation may have been paid, dismissed via a hearing, statutorily expired, or had other changes to its status. To see the current status of outstanding parking violations, please look at the Open Parking & Camera Violations dataset.

• Parking Violations Issued Fiscal Year 2022 can be found here • Parking Violations Issued Fiscal Year 2021 can be found here • Parking Violations Issued Fiscal Year 2020 can be found here • Parking Violations Issued Fiscal Year 2019 can be found here • Parking Violations Issued Fiscal Year 2018 can be found here • Parking Violations Issued Fiscal Year 2017 can be found here • Parking Violations Issued Fiscal Year 2016 can be found here • Parking Violations Issued Fiscal Year 2015 can be found here • Parking Violations Issued Fiscal Year 2014 can be found here

This deprecated 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. For a more detailed look traffic, refer to dataset https://data.ny.gov/d/ebfx-2m7v/.

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 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.

Work in progress: data might be changed

The data set contains the locations of public roadside parking spaces in the northeastern part of Hanover Linden-Nord. As a sample data set, it explicitly does not provide a complete, accurate or correct representation of the conditions! It was collected and processed as part of the 5GAPS research project on September 22nd and October 6th 2022 as a basis for further analysis and in particular as input for simulation studies.

Vehicle Detections

Based on the mapping methodology of Bock et al. (2015) and processing of Leichter et al. (2021), the utilization was determined using vehicle detections in segmented 3D point clouds. The corresponding point clouds were collected by driving over the area on two half-days using a LiDAR mobile mapping system, resulting in several hours between observations. Accordingly, these are only a few sample observations. The trips are made in such a way that combined they cover a synthetic day from about 8-20 clock.

The collected point clouds were georeferenced, processed, and automatically segmented semantically (see Leichter et al., 2021). To automatically extract cars, those points with car labels were clustered by observation epoch and bounding boxes were estimated for the clusters as a representation of car instances. The boxes serve both to filter out unrealistically small and large objects, and to rudimentarily complete the vehicle footprint that may not be fully captured from all sides.

https://data.uni-hannover.de/dataset/0945cd36-6797-44ac-a6bd-b7311f0f96bc/resource/807618b6-5c38-4456-88a1-cb47500081ff/download/detection_map.png" alt="Overview map of detected vehicles" title="Overview map of detected vehicles"> Figure 1: Overview map of detected vehicles

Parking Areas

The public parking areas were digitized manually using aerial images and the detected vehicles in order to exclude irregular parking spaces as far as possible. They were also tagged as to whether they were aligned parallel to the road and assigned to a use at the time of recording, as some are used for construction sites or outdoor catering, for example. Depending on the intended use, they can be filtered individually.

https://data.uni-hannover.de/dataset/0945cd36-6797-44ac-a6bd-b7311f0f96bc/resource/16b14c61-d1d6-4eda-891d-176bdd787bf5/download/parking_area_example.png" alt="Example parking area occupation pattern" title="Visualization of example parking areas on top of an aerial image [by LGLN]"> Figure 2: Visualization of example parking areas on top of an aerial image [by LGLN]

Parking Occupancy

For modelling the parking occupancy, single slots are sampled as center points every 5 m from the parking areas. In this way, they can be integrated into a street/routing graph, for example, as prepared in Wage et al. (2023). Own representations can be generated from the parking area and vehicle detections. Those parking points were intersected with the vehicle boxes to identify occupancy at the respective epochs.

https://data.uni-hannover.de/dataset/0945cd36-6797-44ac-a6bd-b7311f0f96bc/resource/ca0b97c8-2542-479e-83d7-74adb2fc47c0/download/datenpub-bays.png" alt="Overview map of parking slots' average load" title="Overview map of parking slots' average load"> Figure 3: Overview map of average parking lot load

However, unoccupied spaces cannot be determined quite as trivially the other way around, since no detected vehicle can result just as from no measurement/observation. Therefore, a parking space is only recorded as unoccupied if a vehicle was detected at the same time in the neighborhood on the same parking lane and therefore it can be assumed that there is a measurement.

To close temporal gaps, interpolations were made by hour for each parking slot, assuming that between two consecutive observations with an occupancy the space was also occupied in between - or if both times free also free in between. If there was a change, this is indicated by a proportional value. To close spatial gaps, unobserved spaces in the area are drawn randomly from the ten closest occupation patterns around.

This results in an exemplary occupancy pattern of a synthetic day. Depending on the application, the value could be interpreted as occupancy probability or occupancy share.

https://data.uni-hannover.de/dataset/0945cd36-6797-44ac-a6bd-b7311f0f96bc/resource/184a1f75-79ab-4d0e-bb1b-8ed170678280/download/occupation_example.png" alt="Example parking area occupation pattern" title="Example parking area occupation pattern"> Figure 4: Example parking area occupation pattern

References

• F. Bock, D. Eggert and M. Sester (2015): On-street Parking Statistics Using LiDAR Mobile Mapping, 2015 IEEE 18th International Conference on Intelligent Transportation Systems, Gran Canaria, Spain, 2015, pp. 2812-2818. https://doi.org/10.1109/ITSC.2015.452
• A. Leichter, U. Feuerhake, and M. Sester (2021): Determination of Parking Space and its Concurrent Usage Over Time Using Semantically Segmented Mobile Mapping Data, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2021, 185–192. https://doi.org/10.5194/isprs-archives-XLIII-B2-2021-185-2021
• O. Wage, M. Heumann, and L. Bienzeisler (2023): Modeling and Calibration of Last-Mile Logistics to Study Smart-City Dynamic Space Management Scenarios. In 1st ACM SIGSPATIAL International Workshop on Sustainable Mobility (SuMob ’23), November 13, 2023, Hamburg, Germany. ACM, New York, NY, USA, 4 pages. https://doi.org/10.1145/3615899.3627930

These data are part of NACJD's Fast Track Release and are distributed as they there received from the data depositor. The files have been zipped by NACJD for release, but not checked or processed except of the removal of direct identifiers. Users should refer to the accompany readme file for a brief description of the files available with this collections and consult the investigator(s) if further information is needed.

This study interviewed young men living in New York City about their experiences being stopped by the police on the street or in their cars. It examined how experience with the police as well as general evaluations of police policies, practices and behaviors in the respondent's neighborhood shaped views about police legitimacy, and law related behavior, such as compliance with the law and cooperation with legal authorities.

Title: 1985 Auto Imports Database

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

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

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.

Number of Instances: 205

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

Attribute Information: Attribute: Attribute Range:

symboling: -3, -2, -1, 0, 1, 2, 3. normalized-losses: continuous from 65 to 256. 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 fuel-type: diesel, gas. aspiration: std, turbo. num-of-doors: four, two. body-style: hardtop, wagon, sedan, hatchback, convertible. drive-wheels: 4wd, fwd, rwd. engine-location: front, rear. wheel-base: continuous from 86.6 120.9. length: continuous from 141.1 to 208.1. width: continuous from 60.3 to 72.3. height: continuous from 47.8 to 59.8. curb-weight: continuous from 1488 to 4066. engine-type: dohc, dohcv, l, ohc, ohcf, ohcv, rotor. num-of-cylinders: eight, five, four, six, three, twelve, two. engine-size: continuous from 61 to 326. fuel-system: 1bbl, 2bbl, 4bbl, idi, mfi, mpfi, spdi, spfi. bore: continuous from 2.54 to 3.94. stroke: continuous from 2.07 to 4.17. compression-ratio: continuous from 7 to 23. horsepower: continuous from 48 to 288. peak-rpm: continuous from 4150 to 6600. city-mpg: continuous from 13 to 49. highway-mpg: continuous from 16 to 54. price: continuous from 5118 to 45400. Missing Attribute Values: (denoted by "?") Attribute #: Number of instances missing a value:

41 2 4 4 2 2 4