This data set features a hyperlink to the New York State Department of Transportation’s (NYSDOT) Traffic Data (TD) Viewer web page, which includes a link to the Traffic Data interactive map. The Traffic Data Viewer is a geospatially based Geographic Information System (GIS) application for displaying data contained in the roadway inventory database. The interactive map has five viewable data categories or ‘layers’. The five layers include: Average Daily Traffic (ADT); Continuous Counts; Short Counts; Bridges; and Grade Crossings throughout New York State.

Dataset

This dataset was created by Ben Duong

Contents

Annual Average Daily Traffic (AADT) is an estimate of the average daily traffic along a defined segment of roadway. This value is calculated from short term counts taken along the same section which are then factored to produce the estimate of AADT. Because of this process, the most recent AADT for any given roadway will always be for the previous year. Data is available for all New York State Routes and roads that are part of the Federal Aid System.

NYCDOT's Traffic Management Center (TMC) maintains a map of traffic speed detectors throughout the City. The speed detector themselves belong to various city and state agencies. The Traffic Speeds Map is available on the DOT's website. This data feed contains 'real-time' traffic information from locations where NYCDOT picks up sensor feeds within the five boroughs, mostly on major arterials and highways. NYCDOT uses this information for emergency response and management.

Here's the link to the original dataset.

New York City Department of Transportation (NYC DOT) uses Automated Traffic Recorders (ATR) to collect traffic sample volume counts at bridge crossings and roadways. These counts do not cover the entire year, and the number of days counted per location may vary from year to year. Also see Automated Traffic Volume Counts: https://data.cityofnewyork.us/Transportation/Automated-Traffic-Volume-Counts/7ym2-wayt

Data extracted from records of tickets on file with NYS DMV. The tickets were issued to motorists for violations of: NYS Vehicle & Traffic Law (VTL), Thruway Rules and Regulations, Tax Law, Transportation Law, Parks and Recreation Regulations, Local New York City Traffic Ordinances, and NYS Penal Law pertaining to the involvement of a motor vehicle in acts of assault, homicide, manslaughter and criminal negligence resulting in injury or death.

Contains real-time traffic information from locations where NYCDOT picks up sensor feeds within the five NYC boroughs between 2018 and 2022, mostly on major arterials and highways.

Source | Usage Example

This dataset includes (1) a .txt file of processed time-series with four traffic congestion levels for the borough of Manhattan, NYC, averaged every 3 hours for the duration of 2020, and (2) an R script for completing analysis of the traffic time series to determine patterns in traffic over the year 2020, and to evaluate the impact of stay-at-home orders implemented in response to the COIVD-19 pandemic.

Data extracted from records of tickets on file with NYS DMV. The tickets were issued to motorists for violations of:  NYS Vehicle & Traffic Law (VTL),  Thruway Rules and Regulations  Tax Law  Transportation Law  Parks and Recreation Regulations  Local New York City Traffic Ordinances and  NYS Penal Law pertaining to the involvement of a motor vehicle in acts of assault, homicide, manslaughter and criminal negligence resulting in injury or death.

This dataset contains motor vehicle collision reports from the New York City Police Department (NYPD), covering the period January–August 2020. Each record represents an individual collision, including detailed information on the date, time, and location of the accident (borough, ZIP code, street name, latitude/longitude), as well as vehicles, victims, and contributing factors.

Key Features

• Date & Time – Exact timestamp of the reported collision.
• Location – Borough, ZIP code, street(s), and geographical coordinates.
• Vehicles Involved – Type of vehicles and their count per accident.
• Victims – Number of injuries and fatalities, broken down by category (pedestrians, cyclists, motorists).
• Contributing Factors – Primary cause(s) of the accident (e.g., distracted driving, speeding).

Potential Analyses

• Monthly trends – Compare the percentage of total accidents by month to identify seasonal patterns.
• Day & time patterns – Analyze accident frequency by day of the week and hour of the day.
• Street-level analysis – Determine which street recorded the most accidents and what share of total accidents that represents.
• Cause analysis – Identify the most common contributing factors for all accidents and for fatal accidents specifically.

Source

Data originally obtained from NYC Open Data. Licensed under Public Domain.

Graph and download economic data for New York State Canal Traffic for United States (A03020USA601NNBR) from 1837 to 1942 about inland waterway, transportation, NY, and USA.

Traffic Lights of New York (TLoNY)

  Traffic Lights of New York (TLoNY)

An object detection dataset for fine-grained classification of traffic lights in New York City. The dataset includes both vehicular and pedestrian traffic signals with detailed color state annotations.

  Overview

TLoNY provides accurately labeled traffic light data suitable for training object detection models, sourced from publicly available images from Unsplash. The dataset captures… See the full description on the dataset page: https://huggingface.co/datasets/mehmetkeremturkcan/traffic-lights-of-new-york.

Dataset contains information on New York City air quality surveillance data

This dataset compiles a comprehensive database containing 90,327 street segments in New York City, covering their street design features, streetscape design, Vision Zero treatments, and neighborhood land use. It has two scales-street and street segment group (aggregation of same type of street at neighborhood). This dataset is derived based on all publicly available data, most from NYC Open Data. The detailed methods can be found in the published paper, Pedestrian and Car Occupant Crash Casualties Over a 9-Year Span of Vision Zero in New York City. To use it, please refer to the metadata file for more information and cite our work. A full list of raw data source can be found below:

Motor Vehicle Collisions – NYC Open Data: https://data.cityofnewyork.us/Public-Safety/Motor-Vehicle-Collisions-Crashes/h9gi-nx95

Citywide Street Centerline (CSCL) – NYC Open Data: https://data.cityofnewyork.us/City-Government/NYC-Street-Centerline-CSCL-/exjm-f27b

NYC Building Footprints – NYC Open Data: https://data.cityofnewyork.us/Housing-Development/Building-Footprints/nqwf-w8eh

Practical Canopy for New York City: https://zenodo.org/record/6547492

New York City Bike Routes – NYC Open Data: https://data.cityofnewyork.us/Transportation/New-York-City-Bike-Routes/7vsa-caz7

Sidewalk Widths NYC (originally from Sidewalk – NYC Open Data): https://www.sidewalkwidths.nyc/

LION Single Line Street Base Map - The NYC Department of City Planning (DCP): https://www.nyc.gov/site/planning/data-maps/open-data/dwn-lion.page

NYC Planimetric Database Median – NYC Open Data: https://data.cityofnewyork.us/Transportation/NYC-Planimetrics/wt4d-p43d

NYC Vision Zero Open Data (including multiple datasets including all the implementations): https://www.nyc.gov/content/visionzero/pages/open-data

NYS Traffic Data - New York State Department of Transportation Open Data: https://data.ny.gov/Transportation/NYS-Traffic-Data-Viewer/7wmy-q6mb

Smart Location Database - US Environmental Protection Agency: https://www.epa.gov/smartgrowth/smart-location-mapping

Race and ethnicity in area - American Community Survey (ACS): https://www.census.gov/programs-surveys/acs

All 311 Service Requests from 2010 to present. This information is automatically updated daily.

The New York State Department of Transportation (NYSDOT) traffic control devices data set is a list of all of the traffic devices that are either owned or maintained by NYSDOT. The devices included are of various types such as traffic signals, street lights, beacons, flashers, navigational lights, Intelligent Transportation Systems (ITS), etc. Devices in the 5 boroughs of New York City are not owned or maintained by NYSDOT and therefore not represented in this dataset

This repository is an urban traffic speed data set (2015-2019) collected in New York City.

中文描述: 本数据集为纽约城市路网车速数据集, 采集时间为2015年4月至2019年3月, 数据文件是以csv格式保存, csv文件名为【年份 + 月份】.

mostly on major arterials and highways. DOT uses this information for emergency response and management.The metadata defines the fields available in this data feed and explains more about the data.

The volume of containers handled at the Port of New York and New Jersey fluctuated between 2016 and 2022, peaking at *** million TEUs of containers processed in 2022. This represented an increase of almost six percent compared with the previous year.

Context

I wanted to find a better way to provide live traffic updates. We dont all have access to the data from traffic monitoring sensors or whatever gets uploaded from people's smart phones to Apple, Google etc plus I question how accurate the traffic congestion is on Google Maps or other apps. So I figured that since buses are also in the same traffic and many buses stream their GPS location and other data live, that would be an ideal source for traffic data. I investigated the data streams available from many bus companies around the world and found MTA in NYC to be very reliable.

Content

This dataset is from the NYC MTA buses data stream service. In roughly 10 minute increments the bus location, route, bus stop and more is included in each row. The scheduled arrival time from the bus schedule is also included, to give an indication of where the bus should be (how much behind schedule, or on time, or even ahead of schedule).

Acknowledgements

Data is recorded from the MTA SIRI Real Time data feed and the MTA GTFS Schedule data.

Inspiration

I want to see what exploratory & discovery people come up with from this data. Feel free to download this dataset for your own use however I would appreciate as many Kernals included on Kaggle as we can get.

Based on the interest this generates I plan to collect more data for subsequent months down the track.