Name: Uber Pickups In New York City
Creator: Kaggle
License: https://creativecommons.org/publicdomain/zero/1.0/

About this Dataset

Uber Pickups In New York City

Uber TLC FOIL Response

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 Data

The dataset contains, roughly, four groups of files:

Uber trip data from 2014 (April - September), separated by month, with detailed location information
Uber trip data from 2015 (January - June), with less fine-grained location information
non-Uber FHV (For-Hire Vehicle) trips. 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.
aggregate ride and vehicle statistics for all FHV companies (and, occasionally, for taxi companies)

Uber trip data from 2014

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 pickup
Lat : The latitude of the Uber pickup
Lon : The longitude of the Uber pickup
Base : The TLC base company code affiliated with the Uber pickup

These 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

Uber trip data from 2015

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 Uber
Pickup_date : The date and time of the Uber pickup
Affiliated_base_num : The TLC base company code affiliated with the Uber pickup
locationID : The pickup location ID affiliated with the Uber pickup

The 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.

Non-Uber FLV trips

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

Aggregate Statistics

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.

Tables

Other American B01362

@kaggle.fivethirtyeight_uber_pickups_in_new_york_city.other_american_b01362

454.47 kB
91,712 rows
6 columns

CREATE TABLE other_american_b01362 (
  "date" TIMESTAMP,
  "time" VARCHAR,
  "pick_up_address" VARCHAR,
  "unnamed_3" VARCHAR  -- Unnamed: 3,
  "unnamed_4" VARCHAR  -- Unnamed: 4,
  "unnamed_5" VARCHAR  -- Unnamed: 5
);

Other Dial7 B00887

@kaggle.fivethirtyeight_uber_pickups_in_new_york_city.other_dial7_b00887

1.53 MB
194,992 rows
6 columns

CREATE TABLE other_dial7_b00887 (
  "date" TIMESTAMP,
  "time" VARCHAR,
  "state" VARCHAR,
  "pufrom" VARCHAR,
  "address" VARCHAR,
  "street" VARCHAR
);

Other Federal 02216

@kaggle.fivethirtyeight_uber_pickups_in_new_york_city.other_federal_02216

27.77 kB
276 rows
7 columns

CREATE TABLE other_federal_02216 (
  "date" TIMESTAMP,
  "time" VARCHAR,
  "pu_address" VARCHAR,
  "do_address" VARCHAR,
  "routing_details" VARCHAR,
  "pu_address_1" VARCHAR,
  "status" VARCHAR
);

Other Fhv Services Jan Aug 2015

@kaggle.fivethirtyeight_uber_pickups_in_new_york_city.other_fhv_services_jan_aug_2015

113.5 kB
26,181 rows
5 columns

CREATE TABLE other_fhv_services_jan_aug_2015 (
  "base_number" VARCHAR,
  "base_name" VARCHAR,
  "pick_up_date" TIMESTAMP,
  "number_of_trips" BIGINT,
  "number_of_vehicles" VARCHAR
);

Other Lyft B02510

@kaggle.fivethirtyeight_uber_pickups_in_new_york_city.other_lyft_b02510

2.54 MB
267,701 rows
4 columns

CREATE TABLE other_lyft_b02510 (
  "time_of_trip" TIMESTAMP,
  "start_lat" DOUBLE,
  "start_lng" DOUBLE,
  "unnamed_3" VARCHAR  -- Unnamed: 3
);

Other Skyline B00111

@kaggle.fivethirtyeight_uber_pickups_in_new_york_city.other_skyline_b00111

834.78 kB
127,696 rows
6 columns

CREATE TABLE other_skyline_b00111 (
  "date" TIMESTAMP,
  "time" VARCHAR,
  "n__street_address" VARCHAR  -- Street Address,
  "n__city_state" VARCHAR  -- City State,
  "unnamed_4" VARCHAR  -- Unnamed: 4,
  "unnamed_5" VARCHAR  -- Unnamed: 5
);

Uber Jan Feb Foil

@kaggle.fivethirtyeight_uber_pickups_in_new_york_city.uber_jan_feb_foil

8.08 kB
354 rows
4 columns

CREATE TABLE uber_jan_feb_foil (
  "dispatching_base_number" VARCHAR,
  "date" TIMESTAMP,
  "active_vehicles" BIGINT,
  "trips" BIGINT
);

Uber Raw Data Apr14

@kaggle.fivethirtyeight_uber_pickups_in_new_york_city.uber_raw_data_apr14

2.97 MB
564,516 rows
4 columns

CREATE TABLE uber_raw_data_apr14 (
  "date_time" TIMESTAMP,
  "lat" DOUBLE,
  "lon" DOUBLE,
  "base" VARCHAR
);

Uber Raw Data Aug14

@kaggle.fivethirtyeight_uber_pickups_in_new_york_city.uber_raw_data_aug14

3.98 MB
829,275 rows
4 columns

CREATE TABLE uber_raw_data_aug14 (
  "date_time" TIMESTAMP,
  "lat" DOUBLE,
  "lon" DOUBLE,
  "base" VARCHAR
);

Uber Raw Data Janjune 15

@kaggle.fivethirtyeight_uber_pickups_in_new_york_city.uber_raw_data_janjune_15

70.15 MB
14,270,479 rows
4 columns

CREATE TABLE uber_raw_data_janjune_15 (
  "dispatching_base_num" VARCHAR,
  "pickup_date" TIMESTAMP,
  "affiliated_base_num" VARCHAR,
  "locationid" BIGINT
);

Uber Raw Data Jul14

@kaggle.fivethirtyeight_uber_pickups_in_new_york_city.uber_raw_data_jul14

3.85 MB
796,121 rows
4 columns

CREATE TABLE uber_raw_data_jul14 (
  "date_time" TIMESTAMP,
  "lat" DOUBLE,
  "lon" DOUBLE,
  "base" VARCHAR
);

Uber Raw Data Jun14

@kaggle.fivethirtyeight_uber_pickups_in_new_york_city.uber_raw_data_jun14

3.31 MB
663,844 rows
4 columns

CREATE TABLE uber_raw_data_jun14 (
  "date_time" TIMESTAMP,
  "lat" DOUBLE,
  "lon" DOUBLE,
  "base" VARCHAR
);

Uber Raw Data May14

@kaggle.fivethirtyeight_uber_pickups_in_new_york_city.uber_raw_data_may14

3.29 MB
652,435 rows
4 columns

CREATE TABLE uber_raw_data_may14 (
  "date_time" TIMESTAMP,
  "lat" DOUBLE,
  "lon" DOUBLE,
  "base" VARCHAR
);

Uber Raw Data Sep14

@kaggle.fivethirtyeight_uber_pickups_in_new_york_city.uber_raw_data_sep14

4.73 MB
1,028,136 rows
4 columns

CREATE TABLE uber_raw_data_sep14 (
  "date_time" TIMESTAMP,
  "lat" DOUBLE,
  "lon" DOUBLE,
  "base" VARCHAR
);