https://creativecommons.org/publicdomain/zero/1.0/https://creativecommons.org/publicdomain/zero/1.0/
This directory contains data on over 4.5 million Uber pickups in New York City from April to September 2014, and 14.3 million more Uber pickups from January to June 2015. Trip-level data on 10 other for-hire vehicle (FHV) companies, as well as aggregated data for 329 FHV companies, is also included. All the files are as they were received on August 3, Sept. 15 and Sept. 22, 2015.
FiveThirtyEight obtained the data from the NYC Taxi & Limousine Commission (TLC) by submitting a Freedom of Information Law request on July 20, 2015. The TLC has sent us the data in batches as it continues to review trip data Uber and other HFV companies have submitted to it. The TLC's correspondence with FiveThirtyEight is included in the files TLC_letter.pdf
, TLC_letter2.pdf
and TLC_letter3.pdf
. TLC records requests can be made here.
This data was used for four FiveThirtyEight stories: Uber Is Serving New York’s Outer Boroughs More Than Taxis Are, Public Transit Should Be Uber’s New Best Friend, Uber Is Taking Millions Of Manhattan Rides Away From Taxis, and Is Uber Making NYC Rush-Hour Traffic Worse?.
The dataset contains, roughly, four groups of files:
There are six files of raw data on Uber pickups in New York City from April to September 2014. The files are separated by month and each has the following columns:
Date/Time
: The date and time of the Uber pickupLat
: The latitude of the Uber pickupLon
: The longitude of the Uber pickupBase
: The TLC base company code affiliated with the Uber pickupThese files are named:
uber-raw-data-apr14.csv
uber-raw-data-aug14.csv
uber-raw-data-jul14.csv
uber-raw-data-jun14.csv
uber-raw-data-may14.csv
uber-raw-data-sep14.csv
Also included is the file uber-raw-data-janjune-15.csv
This file has the following columns:
Dispatching_base_num
: The TLC base company code of the base that dispatched the UberPickup_date
: The date and time of the Uber pickupAffiliated_base_num
: The TLC base company code affiliated with the Uber pickuplocationID
: The pickup location ID affiliated with the Uber pickupThe Base
codes are for the following Uber bases:
B02512 : Unter B02598 : Hinter B02617 : Weiter B02682 : Schmecken B02764 : Danach-NY B02765 : Grun B02835 : Dreist B02836 : Drinnen
For coarse-grained location information from these pickups, the file taxi-zone-lookup.csv
shows the taxi Zone
(essentially, neighborhood) and Borough
for each locationID
.
The dataset also contains 10 files of raw data on pickups from 10 for-hire vehicle (FHV) companies. The trip information varies by company, but can include day of trip, time of trip, pickup location, driver's for-hire license number, and vehicle's for-hire license number.
These files are named:
American_B01362.csv
Diplo_B01196.csv
Highclass_B01717.csv
Skyline_B00111.csv
Carmel_B00256.csv
Federal_02216.csv
Lyft_B02510.csv
Dial7_B00887.csv
Firstclass_B01536.csv
Prestige_B01338.csv
There is also a file other-FHV-data-jan-aug-2015.csv
containing daily pickup data for 329 FHV companies from January 2015 through August 2015.
The file Uber-Jan-Feb-FOIL.csv
contains aggregated daily Uber trip statistics in January and February 2015.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Analysis of ‘Vehicle Miles Traveled During Covid-19 Lock-Downs ’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/yamqwe/vehicle-miles-travelede on 13 February 2022.
--- Dataset description provided by original source is as follows ---
**This data set was last updated 3:30 PM ET Monday, January 4, 2021. The last date of data in this dataset is December 31, 2020. **
Overview
Data shows that mobility declined nationally since states and localities began shelter-in-place strategies to stem the spread of COVID-19. The numbers began climbing as more people ventured out and traveled further from their homes, but in parallel with the rise of COVID-19 cases in July, travel declined again.
This distribution contains county level data for vehicle miles traveled (VMT) from StreetLight Data, Inc, updated three times a week. This data offers a detailed look at estimates of how much people are moving around in each county.
Data available has a two day lag - the most recent data is from two days prior to the update date. Going forward, this dataset will be updated by AP at 3:30pm ET on Monday, Wednesday and Friday each week.
This data has been made available to members of AP’s Data Distribution Program. To inquire about access for your organization - publishers, researchers, corporations, etc. - please click Request Access in the upper right corner of the page or email kromano@ap.org. Be sure to include your contact information and use case.
Findings
- Nationally, data shows that vehicle travel in the US has doubled compared to the seven-day period ending April 13, which was the lowest VMT since the COVID-19 crisis began. In early December, travel reached a low not seen since May, with a small rise leading up to the Christmas holiday.
- Average vehicle miles traveled continues to be below what would be expected without a pandemic - down 38% compared to January 2020. September 4 reported the largest single day estimate of vehicle miles traveled since March 14.
- New Jersey, Michigan and New York are among the states with the largest relative uptick in travel at this point of the pandemic - they report almost two times the miles traveled compared to their lowest seven-day period. However, travel in New Jersey and New York is still much lower than expected without a pandemic. Other states such as New Mexico, Vermont and West Virginia have rebounded the least.
About This Data
The county level data is provided by StreetLight Data, Inc, a transportation analysis firm that measures travel patterns across the U.S.. The data is from their Vehicle Miles Traveled (VMT) Monitor which uses anonymized and aggregated data from smartphones and other GPS-enabled devices to provide county-by-county VMT metrics for more than 3,100 counties. The VMT Monitor provides an estimate of total vehicle miles travelled by residents of each county, each day since the COVID-19 crisis began (March 1, 2020), as well as a change from the baseline average daily VMT calculated for January 2020. Additional columns are calculations by AP.
Included Data
01_vmt_nation.csv - Data summarized to provide a nationwide look at vehicle miles traveled. Includes single day VMT across counties, daily percent change compared to January and seven day rolling averages to smooth out the trend lines over time.
02_vmt_state.csv - Data summarized to provide a statewide look at vehicle miles traveled. Includes single day VMT across counties, daily percent change compared to January and seven day rolling averages to smooth out the trend lines over time.
03_vmt_county.csv - Data providing a county level look at vehicle miles traveled. Includes VMT estimate, percent change compared to January and seven day rolling averages to smooth out the trend lines over time.
Additional Data Queries
* Filter for specific state - filters
02_vmt_state.csv
daily data for specific state.* Filter counties by state - filters
03_vmt_county.csv
daily data for counties in specific state.* Filter for specific county - filters
03_vmt_county.csv
daily data for specific county.Interactive
The AP has designed an interactive map to show percent change in vehicle miles traveled by county since each counties lowest point during the pandemic:
This dataset was created by Angeliki Kastanis and contains around 0 samples along with Date At Low, Mean7 County Vmt At Low, technical information and other features such as: - County Name - County Fips - and more.
- Analyze State Name in relation to Baseline Jan Vmt
- Study the influence of Date At Low on Mean7 County Vmt At Low
- More datasets
If you use this dataset in your research, please credit Angeliki Kastanis
--- Original source retains full ownership of the source dataset ---
**This data set was last updated 3:30 PM ET Monday, January 4, 2021. The last date of data in this dataset is December 31, 2020. **
Data shows that mobility declined nationally since states and localities began shelter-in-place strategies to stem the spread of COVID-19. The numbers began climbing as more people ventured out and traveled further from their homes, but in parallel with the rise of COVID-19 cases in July, travel declined again.
This distribution contains county level data for vehicle miles traveled (VMT) from StreetLight Data, Inc, updated three times a week. This data offers a detailed look at estimates of how much people are moving around in each county.
Data available has a two day lag - the most recent data is from two days prior to the update date. Going forward, this dataset will be updated by AP at 3:30pm ET on Monday, Wednesday and Friday each week.
This data has been made available to members of AP’s Data Distribution Program. To inquire about access for your organization - publishers, researchers, corporations, etc. - please click Request Access in the upper right corner of the page or email kromano@ap.org. Be sure to include your contact information and use case.
01_vmt_nation.csv - Data summarized to provide a nationwide look at vehicle miles traveled. Includes single day VMT across counties, daily percent change compared to January and seven day rolling averages to smooth out the trend lines over time.
02_vmt_state.csv - Data summarized to provide a statewide look at vehicle miles traveled. Includes single day VMT across counties, daily percent change compared to January and seven day rolling averages to smooth out the trend lines over time.
03_vmt_county.csv - Data providing a county level look at vehicle miles traveled. Includes VMT estimate, percent change compared to January and seven day rolling averages to smooth out the trend lines over time.
* Filter for specific state - filters 02_vmt_state.csv
daily data for specific state.
* Filter counties by state - filters 03_vmt_county.csv
daily data for counties in specific state.
* Filter for specific county - filters 03_vmt_county.csv
daily data for specific county.
The AP has designed an interactive map to show percent change in vehicle miles traveled by county since each counties lowest point during the pandemic:
@(https://interactives.ap.org/vmt-map/)
This data can help put your county's mobility in context with your state and over time. The data set contains different measures of change - daily comparisons and seven day rolling averages. The rolling average allows for a smoother trend line for comparison across counties and states. To get the full picture, there are also two available baselines - vehicle miles traveled in January 2020 (pre-pandemic) and vehicle miles traveled at each geography's low point during the pandemic.
Not seeing a result you expected?
Learn how you can add new datasets to our index.
https://creativecommons.org/publicdomain/zero/1.0/https://creativecommons.org/publicdomain/zero/1.0/
This directory contains data on over 4.5 million Uber pickups in New York City from April to September 2014, and 14.3 million more Uber pickups from January to June 2015. Trip-level data on 10 other for-hire vehicle (FHV) companies, as well as aggregated data for 329 FHV companies, is also included. All the files are as they were received on August 3, Sept. 15 and Sept. 22, 2015.
FiveThirtyEight obtained the data from the NYC Taxi & Limousine Commission (TLC) by submitting a Freedom of Information Law request on July 20, 2015. The TLC has sent us the data in batches as it continues to review trip data Uber and other HFV companies have submitted to it. The TLC's correspondence with FiveThirtyEight is included in the files TLC_letter.pdf
, TLC_letter2.pdf
and TLC_letter3.pdf
. TLC records requests can be made here.
This data was used for four FiveThirtyEight stories: Uber Is Serving New York’s Outer Boroughs More Than Taxis Are, Public Transit Should Be Uber’s New Best Friend, Uber Is Taking Millions Of Manhattan Rides Away From Taxis, and Is Uber Making NYC Rush-Hour Traffic Worse?.
The dataset contains, roughly, four groups of files:
There are six files of raw data on Uber pickups in New York City from April to September 2014. The files are separated by month and each has the following columns:
Date/Time
: The date and time of the Uber pickupLat
: The latitude of the Uber pickupLon
: The longitude of the Uber pickupBase
: The TLC base company code affiliated with the Uber pickupThese files are named:
uber-raw-data-apr14.csv
uber-raw-data-aug14.csv
uber-raw-data-jul14.csv
uber-raw-data-jun14.csv
uber-raw-data-may14.csv
uber-raw-data-sep14.csv
Also included is the file uber-raw-data-janjune-15.csv
This file has the following columns:
Dispatching_base_num
: The TLC base company code of the base that dispatched the UberPickup_date
: The date and time of the Uber pickupAffiliated_base_num
: The TLC base company code affiliated with the Uber pickuplocationID
: The pickup location ID affiliated with the Uber pickupThe Base
codes are for the following Uber bases:
B02512 : Unter B02598 : Hinter B02617 : Weiter B02682 : Schmecken B02764 : Danach-NY B02765 : Grun B02835 : Dreist B02836 : Drinnen
For coarse-grained location information from these pickups, the file taxi-zone-lookup.csv
shows the taxi Zone
(essentially, neighborhood) and Borough
for each locationID
.
The dataset also contains 10 files of raw data on pickups from 10 for-hire vehicle (FHV) companies. The trip information varies by company, but can include day of trip, time of trip, pickup location, driver's for-hire license number, and vehicle's for-hire license number.
These files are named:
American_B01362.csv
Diplo_B01196.csv
Highclass_B01717.csv
Skyline_B00111.csv
Carmel_B00256.csv
Federal_02216.csv
Lyft_B02510.csv
Dial7_B00887.csv
Firstclass_B01536.csv
Prestige_B01338.csv
There is also a file other-FHV-data-jan-aug-2015.csv
containing daily pickup data for 329 FHV companies from January 2015 through August 2015.
The file Uber-Jan-Feb-FOIL.csv
contains aggregated daily Uber trip statistics in January and February 2015.