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.
In the fourth quarter of 2023, Uber's ridership worldwide totaled 2.6 billion trips. This compares to 2.1 billion trips in the first quarter of 2022, representing an increase of 24 percent year-on-year. A brief overview of Uber Technologies Uber Technologies Corporation started as a ridesharing company to disrupt the traditional taxi services industry. Having observed the global lucrativeness of the sharing economy in the upcoming years, Uber expanded its business profile to reshape the entire transportation industry, from food delivery and logistics to transport of people. As a result of strategic market positioning, the company experienced strong growth. The net revenue of Uber increased over 75 times in ten years, up from 0.5 billion U.S. dollars in 2014 to 37.3 billion U.S. dollars in 2023. Uber Technologies reported being profitable for the first time since 2018, posting a net profit of roughly 1.9 billion U.S. dollars during the fiscal year of 2023. Competition in the sharing economy Uber has been operating in a highly competitive environment since it introduced its first differentiated cab services. One of the major competitors of Uber Technologies is the San Francisco-based Lyft. Although Lyft is a latecomer into the ride-sharing business, Lyft progressively worked on weaknesses exhibited by Uber to strengthen its position against Uber and other competitors. Besides, Lyft is one of the major innovators in the sharing economy along with Uber Technologies. In 2022, Lyft Corporation invested nearly 556 million U.S. dollars into research and development globally, which has been scaled back in recent years. Lyft generated 4.4 billion U.S. dollars in global revenue during 2023.
This dataset ends with 2022. Please see the Featured Content link below for the dataset that starts in 2023.
All trips, from November 2018 to December 2022, reported by Transportation Network Providers (sometimes called rideshare companies) to the City of Chicago as part of routine reporting required by ordinance.
Census Tracts are suppressed in some cases, and times are rounded to the nearest 15 minutes. Fares are rounded to the nearest $2.50 and tips are rounded to the nearest $1.00.
For a discussion of the approach to privacy in this dataset, please see https://data.cityofchicago.org/stories/s/82d7-i4i2.
Open Database License (ODbL) v1.0https://www.opendatacommons.org/licenses/odbl/1.0/
License information was derived automatically
My Uber Drives (2016)
Here are the details of my Uber Drives of 2016. I am sharing this dataset for data science community to learn from the behavior of an ordinary Uber customer.
Geography: USA, Sri Lanka and Pakistan
Time period: January - December 2016
Unit of analysis: Drives
Total Drives: 1,155
Total Miles: 12,204
Dataset: The dataset contains Start Date, End Date, Start Location, End Location, Miles Driven and Purpose of drive (Business, Personal, Meals, Errands, Meetings, Customer Support etc.)
Users are allowed to use, download, copy, distribute and cite the dataset for their pet projects and training. Please cite it as follows: “Zeeshan-ul-hassan Usmani, My Uber Drives Dataset, Kaggle Dataset Repository, March 23, 2017.”
Uber TLC FOIL Response - The dataset contains 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
https://github.com/fivethirtyeight/uber-tlc-foil-response
1.1 Billion Taxi Pickups from New York -
http://toddwschneider.com/posts/analyzing-1-1-billion-nyc-taxi-and-uber-trips-with-a-vengeance/
What you can do with this data - a good example by Yao-Jen Kuo - https://yaojenkuo.github.io/uber.html
Some ideas worth exploring:
• What is the average length of the trip?
• Average number of rides per week or per month?
• Total tax savings based on traveled business miles?
• Percentage of business miles vs personal vs. Meals
• How much money can be saved by a typical customer using Uber, Careem, or Lyft versus regular cab service?
This dataset contains lists of Restaurants and their menus in the USA that are partnered with Uber Eats. Data was collected via web scraping using python libraries.
*This dataset is dedicated to the awesome delivery drivers of Uber Eats, hence the cover image
kaggle API Command
!kaggle datasets download -d ahmedshahriarsakib/uber-eats-usa-restaurants-menus
The dataset has two CSV files -
restaurants.csv (40k+ entries, 11 columns)
$
= Inexpensive, $$
= Moderately expensive, $$$
= Expensive, $$$$
= Very Expensive) - Source - stackoverflowrestaurant-menus.csv (3.71M entries, 5 columns)
Data was scraped from - - https://www.ubereats.com - An online food ordering and delivery platform launched by Uber in 2014. Users can read menus, reviews, ratings, order, and pay for food from participating restaurants using an application on the iOS or Android platforms, or through a web browser. Users are also able to tip for delivery. Payment is charged to a card on file with Uber. Meals are delivered by couriers using cars, scooters, bikes, or foot. It is operational in over 6,000 cities across 45 countries.
The data and information in the data set provided here are intended to use for educational purposes only. I do not own any of the data and all rights are reserved to the respective owners.
With the taxi sector booming exponentially in the country, the ride hailing industry has been the source of employment for a number of people across India. The market is dominated by two players, Uber and Ola. The number of employees in OlaCabs was over 500 thousand as of July 2016. This snowballing growth of the cab industry has been creating problems for local rickshaw and auto drivers with people opting to take a ride in an online taxi as opposed to an auto-rickshaw.
Battle of the Giants
Even after the arrival of the San-Francisco based Uber, it is the native company doing the heavy lifting in the market. Ola held the highest share of taxi apps installed across the country in 2017, whereas Uber suffered more de-installations in the same time frame.
A cab wherever you are
High penetration is presumably one of the major factors for the success of the native company. As opposed to its main competitor, OlaCabs had a reach of an additional 20 percent among smartphone users in tier 1 cities in 2017. The firm operates in more than 100 cities, twice more than its counterpart, leading to this development. Despite the differences in their services and revenue streams, both companies still seem to thrive for greater success with new developments in the now fast-moving economy of India. With the announcement of an outpost in Australia, the home-grown startup from India does not seem willing to stop at just one destination.
This is NOT a raw population dataset. We use our proprietary stack to combine detailed 'WorldPop' UN-adjusted, sex and age structured population data with a spatiotemporal OD matrix.
The result is a dataset where each record indicates how many people can be reached in a fixed timeframe (90 Mins in this case) from that record's location.
The dataset is broken down into sex and age bands at 5 year intervals, e.g - male 25-29 (m_25) and also contains a set of features detailing the representative percentage of the total that the count represents.
The dataset provides 76174 records, one for each sampled location. These are labelled with a h3 index at resolution 7 - this allows easy plotting and filtering in Kepler.gl / Deck.gl / Mapbox, or easy conversion to a centroid (lat/lng) or the representative geometry of the hexagonal cell for integration with your geospatial applications and analyses.
A h3 resolution of 7, is a hexagonal cell area equivalent to: - ~1.9928 sq miles - ~5.1613 sq km
Higher resolutions or alternate geographies are available on request.
More information on the h3 system is available here: https://eng.uber.com/h3/
WorldPop data provides for a population count using a grid of 1 arc second intervals and is available for every geography.
More information on the WorldPop data is available here: https://www.worldpop.org/
One of the main use cases historically has been in prospecting for site selection, comparative analysis and network validation by asset investors and logistics companies. The data structure makes it very simple to filter out areas which do not meet requirements such as: - being able to access 70% of the German population within 4 hours by Truck and show only the areas which do exhibit this characteristic.
Clients often combine different datasets either for different timeframes of interest, or to understand different populations, such as that of the unemployed, or those with particular qualifications within areas reachable as a commute.
Open Database License (ODbL) v1.0https://www.opendatacommons.org/licenses/odbl/1.0/
License information was derived automatically
Uber Movement provides anonymized data from over two billion trips to help urban planning around the world.
Data retrieved from Uber Movement, (c) 2017 Uber Technologies, Inc.,https://movement.uber.com
Over the past six and a half years, Uber has learned a lot about the future of urban mobility and what it means for cities and the people who live in them. Uber has gotten consistent feedback from cities they partner with that access to their aggregated data will inform decisions about how to adapt existing infrastructure and invest in future solutions to make our cities more efficient. Uber hopes Uber Movement can play a role in helping cities grow in a way that works for everyone.
RESULTS EXPECTED:
**Visually identify the most pressing problems for Uber.
1. Hint: Create plots to visualise the frequency of requests that get cancelled or show 'no cars available'; identify the most problematic types of requests (city to airport / airport to city etc.) and the time slots (early mornings, late evenings etc.) using plots
1.a. Find out the gap between supply and demand and show the same using plots.
2. Find the time slots when the highest gap exists
2.a. Find the types of requests (city-airport or airport-city) for which the gap is the most severe in the identified time slots
3. What do you think is the reason for this issue for the supply-demand gap? Write the answer in less than 100 words. You may accompany the write-up with plot(s).
4. Recommend some ways to resolve the supply-demand gap.
Understanding demand and supply is critical for businesses to optimize operations, maximize profits, and make informed decisions. Analyzing the demand and supply for cab ride businesses like Ola and Uber is one of the challenging use cases of demand and supply analysis.
Here is a dataset of the demand for rides and the supply of drivers in a particular city. Below are the features in the dataset:
1- Drivers Active Per Hour: Number of drivers active per hour. 2- Riders Active Per Hour: Number of Riders looking for rides. 3- Rides Completed: Number of rides completed.
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.