In the fourth quarter of 2023, Uber's ridership worldwide totaled *** billion trips. This compares to *** billion trips in the first quarter of 2022, representing an increase of ** 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 ** times in ten years, up from *** billion U.S. dollars in 2014 to **** billion U.S. dollars in 2023. Uber Technologies reported being profitable for the first time since 2018, posting a net profit of roughly *** 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 *** million U.S. dollars into research and development globally, which has been scaled back in recent years. Lyft generated *** billion U.S. dollars in global revenue during 2023.

Context

UBER collects thousands of data points on each and every ride but it only shares a tiny part of this data with its drivers. A driver can get weekly statements from his/her personal dashboard on UBER's website. This way a driver has an opportunity to check each ride that has been made with detailed info on the earnings.

Content

Not only each weekly statement includes a unique ID and exact time of each ride but it also shows a complex structure of driver fares. Besides basic components like time, distance, and tips, driver's fares can also include promotions, surge charges, long pick-up fees, reimbursements, and many more. You can see detailed descriptors of each feature below.

Inspiration

My initial research question was simple: "Is there a difference in riders' tipping behavior like tip size and frequency on different weekdays?" But after spending some time digging into this problem it became clear that tipping is a much broader scientific field with lots of research. However, most researches were made way before services like Uber appeared and were mainly focused on tipping behavior in restaurants which obviously differs a lot. Here's one of the recent researches by former Uber and Lyft employees on tipping behavior: "The Driver’s of Social Preferences: Evidence from a Nationwide Tipping Field Experiment" by Chandar, et. al (2019)

Other research question could be: Why certain people tip and others don't? Are riders more likely to tip as the fare of the trip increses? Is that true that late-night rides are tipped more often? Can we predict the size or frequency of the tip based on the fare data only?

Analysis of ‘UBER Stock Data’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/varpit94/uber-stock-data on 21 November 2021.

--- Dataset description provided by original source is as follows ---

What is UBER?

Uber Technologies, Inc., commonly known as Uber, is an American technology company. Its services include ride-hailing, food delivery (Uber Eats and Postmates), package delivery, couriers, freight transportation, and, through a partnership with Lime, electric bicycle and motorized scooter rental. The company is based in San Francisco and has operations in over 900 metropolitan areas worldwide. It is one of the largest firms in the gig economy. Uber is estimated to have over 93 million monthly active users worldwide. In the United States, Uber has a 71% market share for ride-sharing and a 22% market share for food delivery. Uber has been so prominent in the sharing economy that changes in various industries as a result of Uber have been referred to as uberisation, and many startups have described their offerings as "Uber for X".

Information about this dataset

This dataset provides historical data of Uber Technologies, Inc. (UBER). The data is available at a daily level. Currency is USD.

--- Original source retains full ownership of the source dataset ---

Analysis of ‘My Uber Drives’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/zusmani/uberdrives on 13 February 2022.

--- Dataset description provided by original source is as follows ---

Context

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.

Content

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.)

Acknowledgements & References

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.”

Past Research

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

Inspiration

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?

--- Original source retains full ownership of the source dataset ---

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.

What is UBER?

Uber Technologies, Inc., commonly known as Uber, is an American technology company. Its services include ride-hailing, food delivery (Uber Eats and Postmates), package delivery, couriers, freight transportation, and, through a partnership with Lime, electric bicycle and motorized scooter rental. The company is based in San Francisco and has operations in over 900 metropolitan areas worldwide. It is one of the largest firms in the gig economy. Uber is estimated to have over 93 million monthly active users worldwide. In the United States, Uber has a 71% market share for ride-sharing and a 22% market share for food delivery. Uber has been so prominent in the sharing economy that changes in various industries as a result of Uber have been referred to as uberisation, and many startups have described their offerings as "Uber for X".

Information about this dataset

This dataset provides historical data of Uber Technologies, Inc. (UBER). The data is available at a daily level. Currency is USD.

About of Uber dataset

This dataset coming from mobility startup that lets any user to book a ride to from any point A to any point B within the city using a smartphone. Ride value is calculated at the time of request automatically by the app, considering distance, estimated travel time, and current car availability (demand / offer balance).

Once the ride ends, we charge passenger's credit card, and transfer X% of this value to the driver's bank account. Finally, before the passengers gets picked up, the ride can be cancelled by either the driver or the passenger.

A descriptive data analysis: ○ how many? (e.g: vehicles, riders, drivers)
○ when? (e.g: journeys/price/cost per time period, are the journeys quick?
○ what? (e.g: reservations/asap, vehicle type)
○ where? (e.g: origin map, best origins)
○ who? (e.g: worst riders, best drivers)
○ any question you consider interesting

Disclaimer: The dataset come from HR technical interview, so the company is real but I don't know if values are real or not. But is good start point to understand how collect some values the carsharing companies as well.

This data is all about Uber/Careem rides of a user. This is basically for analysing and carrying out any specific relations in this data.

Uber rides🚗🚗 🚗🚗

Travel Time - Uber Movement 🚗🚗🚗🚗

DESCRIPTION❤️❤️

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.

Note😃😃😃😃

• This data is for training how using data analysis 🤝🎉

• Please appreciate the effort with an upvote 👍 😃😃

Thank You ❤️❤️❤️

Context

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.

Content

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.)

Acknowledgements & References

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.”

Past Research

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

Inspiration

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?

All trips in 2023-2024 reported by Transportation Network Providers (sometimes called rideshare companies) to the City of Chicago as part of routine reporting required by ordinance. For earlier and later trips, see the links in the Featured Content section below. This version of the datasets contains three new columns, marked in their column descriptions.

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.

App Reviews

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F1842206%2Fd4a6033b6bd31af45d5175d02e697934%2FAPPLEAPPS2.png?generation=1700357122842963&alt=media" alt="">

1. uber-request-a-ride-us- 73787 rows
2. waze-navigation-live-traffic-us- 26260 rows
3. facebook-us- 24200 rows
4. spotify-music-and-podcasts-us- 15580 rows
5. netflix-us- 11760 rows
6. pinterest-us- 10860 rows
7. X-us- 8160 rows
8. tiktok-us- 2542 rows
9. tinder-dating-chat-friends-us- 1060 rows
10. instagram-us- 300 rows

These reviews are from Apple App Store

Usage

This dataset should paint a good picture on what is the public's perception of the apps over the years. Using this dataset, we can do the following

1. Extract sentiments and trends
2. Identify which version of an app had the most positive feedback, the worst.
3. Use topic modelling to identify the pain points of the application.

(AND MANY MORE!)

Note

Images generated using Bing Image Generator

Context

The aim of analysis is to identify the root cause of the problem (i.e. cancellation and non-availability of cars) and recommend ways to improve the situation. As a result of your analysis, we will be able to present to the client the root cause(s) and possible hypotheses of the problem(s) and recommend ways to improve them. .

Content

We may have some experience of travelling to and from the airport. We have used Uber or any other cab service for this travel? Did you at any time face the problem of cancellation by the driver or non-availability of cars?

Well, if these are the problems faced by customers, these very issues also impact the business of Uber. If drivers cancel the request of riders or if cars are unavailable, Uber loses out on its revenue. Let’s hear more about such problems that Uber faces during its operations.

Dataset

There are six attributes associated with each request made by a customer:

Request id: A unique identifier of the request

Time of request: The date and time at which the customer made the trip request

Drop-off time: The drop-off date and time, in case the trip was completed

Pick-up point: The point from which the request was made

Driver id: The unique identification number of the driver

Status of the request: The final status of the trip, that can be either completed, cancelled by the driver or no cars available

Data to reproduce figures from the paper:>Cats, O., Kucharski, R., Danda, S. R., & Yap, M. (2021). Beyond the Dichotomy: How Ride-hailing Competes with and Complements Public Transport. arXiv preprint arXiv:2104.04208.### fig_1_data.csv* each row denotes a single histogram line with 7 percentile values (in columns), each row is named after city and attribute name### fig_2_data.csv* each row denotes a single histogram line with 7 percentile values (in columns), each row is named after city and attribute name### maps.zip* each map has its own csv file named hexdata_city_attribute_level.csv.* each row in .csv file denotes single hex and contains: hex_id (https://github.com/uber/h3-py), hex geometry (in WKT format) and attribute value (used as a coloring scale in the maps)### fig. 6* each dot in the file is stored in the .csv as the x,y,value in respective lines in rows

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 *** 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 ** percent among smartphone users in tier * 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 *** destination.