About this Dataset Data retrieved from Uber Movement, (c) 2017 Uber Technologies....

Objectives 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.

Selling-General-and-Administrative Time Series for Uber Technologies Inc. Uber Technologies, Inc. develops and operates proprietary technology applications in the United States, Canada, Latin America, Europe, the Middle East, Africa, and the Asia Pacific. It operates through three segments: Mobility, Delivery, and Freight. The Mobility segment connects consumers with a range of transportation modalities, such as ridesharing, carsharing, micromobility, rentals, public transit, taxis, and other modalities; and offers riders in a variety of vehicle types, as well as financial partnerships products and advertising services. The Delivery segment allows consumers to search for and discover restaurants to grocery, alcohol, convenience, and other retails, as well as order a meal or other items, and either pick-up at the restaurant or have it delivered; and provides Uber direct, a white-label delivery-as-a-service for retailers and restaurants, as well as advertising services. The Freight segment manages transportation and logistics network, which connects shippers and carriers in digital marketplace, including carriers upfronts, pricing, and shipment booking; and offers on-demand platform to automate logistics end-to-end transactions for small-and medium-sized business to global enterprises. The company was formerly known as Ubercab, Inc. and changed its name to Uber Technologies, Inc. in February 2011. Uber Technologies, Inc. was founded in 2009 and is headquartered in San Francisco, California.

Total-Stockholder-Equity Time Series for Uber Technologies Inc. Uber Technologies, Inc. develops and operates proprietary technology applications in the United States, Canada, Latin America, Europe, the Middle East, Africa, and the Asia Pacific. It operates through three segments: Mobility, Delivery, and Freight. The Mobility segment connects consumers with a range of transportation modalities, such as ridesharing, carsharing, micromobility, rentals, public transit, taxis, and other modalities; and offers riders in a variety of vehicle types, as well as financial partnerships products and advertising services. The Delivery segment allows consumers to search for and discover restaurants to grocery, alcohol, convenience, and other retails, as well as order a meal or other items, and either pick-up at the restaurant or have it delivered; and provides Uber direct, a white-label delivery-as-a-service for retailers and restaurants, as well as advertising services. The Freight segment manages transportation and logistics network, which connects shippers and carriers in digital marketplace, including carriers upfronts, pricing, and shipment booking; and offers on-demand platform to automate logistics end-to-end transactions for small-and medium-sized business to global enterprises. The company was formerly known as Ubercab, Inc. and changed its name to Uber Technologies, Inc. in February 2011. Uber Technologies, Inc. was founded in 2009 and is headquartered in San Francisco, California.

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 ---

Days-of-Sales-Outstanding Time Series for Uber Technologies Inc. Uber Technologies, Inc. develops and operates proprietary technology applications in the United States, Canada, Latin America, Europe, the Middle East, Africa, and the Asia Pacific. It operates through three segments: Mobility, Delivery, and Freight. The Mobility segment connects consumers with a range of transportation modalities, such as ridesharing, carsharing, micromobility, rentals, public transit, taxis, and other modalities; and offers riders in a variety of vehicle types, as well as financial partnerships products and advertising services. The Delivery segment allows consumers to search for and discover restaurants to grocery, alcohol, convenience, and other retails, as well as order a meal or other items, and either pick-up at the restaurant or have it delivered; and provides Uber direct, a white-label delivery-as-a-service for retailers and restaurants, as well as advertising services. The Freight segment manages transportation and logistics network, which connects shippers and carriers in digital marketplace, including carriers upfronts, pricing, and shipment booking; and offers on-demand platform to automate logistics end-to-end transactions for small-and medium-sized business to global enterprises. The company was formerly known as Ubercab, Inc. and changed its name to Uber Technologies, Inc. in February 2011. Uber Technologies, Inc. was founded in 2009 and is headquartered in San Francisco, California.

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.

The Uber Ride Dataset for New York City contains detailed information about every Uber ride in the city. The dataset includes the TLC license number of the HVFHS base or business, the TLC Base License Number of the base that dispatched the trip, the date and time of the trip pick-up and drop-off, the TLC Taxi Zone in which the trip began and ended, the base number of the base that received the original trip request, and the date and time when the passenger requested to be picked up.

The dataset also provides information about the total miles for the passenger trip, the total time in seconds for the passenger trip, the base passenger fare before tolls, tips, taxes, and fees, the total amount of all tolls paid in the trip, the total amount collected in the trip for the Black Car Fund, the total amount collected in the trip for NYS sales tax, the total amount collected in the trip for NYS congestion surcharge, and the airport fee of $2.50 for both drop off and pick up at LaGuardia, Newark, and John F. Kennedy airports.

Moreover, the dataset includes the total amount of tips received from the passenger, the total driver pay (not including tolls or tips and net of commission, surcharges, or taxes), the flag indicating whether the passenger agreed to a shared/pooled ride and whether the passenger shared the vehicle with another passenger who booked separately at any point during the trip.

The dataset also includes information about whether the trip was administered on behalf of the Metropolitan Transportation Authority (MTA), whether the passenger requested a wheelchair-accessible vehicle (WAV), and whether the trip occurred in a wheelchair-accessible vehicle (WAV). This comprehensive dataset can be used for a variety of research and analysis purposes, including traffic patterns, fare analysis, and more.

The datasets are broken down by month and formatted in parquet. To use the parquet formatted files in pandas, there is an example in my notebook in the code section. If you need more details, look at the pdfs in the datasets. The data is originally from https://www.nyc.gov/site/tlc/about/tlc-trip-record-data.page

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 21 November 2021.

--- 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 ---

Cost-of-Goods-Sold-Including-Depreciation-and-Amortization Time Series for Uber Technologies Inc. Uber Technologies, Inc. develops and operates proprietary technology applications in the United States, Canada, Latin America, Europe, the Middle East, Africa, and the Asia Pacific. It operates through three segments: Mobility, Delivery, and Freight. The Mobility segment connects consumers with a range of transportation modalities, such as ridesharing, carsharing, micromobility, rentals, public transit, taxis, and other modalities; and offers riders in a variety of vehicle types, as well as financial partnerships products and advertising services. The Delivery segment allows consumers to search for and discover restaurants to grocery, alcohol, convenience, and other retails, as well as order a meal or other items, and either pick-up at the restaurant or have it delivered; and provides Uber direct, a white-label delivery-as-a-service for retailers and restaurants, as well as advertising services. The Freight segment manages transportation and logistics network, which connects shippers and carriers in digital marketplace, including carriers upfronts, pricing, and shipment booking; and offers on-demand platform to automate logistics end-to-end transactions for small-and medium-sized business to global enterprises. The company was formerly known as Ubercab, Inc. and changed its name to Uber Technologies, Inc. in February 2011. Uber Technologies, Inc. was founded in 2009 and is headquartered in San Francisco, California.

The Measurable AI UberEats E-Receipt Dataset is a leading source of email receipts and transaction data, offering data collected directly from users via Proprietary Consumer Apps, with millions of opt-in users.

We source our email receipt consumer data panel via two consumer apps which garner the express consent of our end-users (GDPR compliant). We then aggregate and anonymize all the transactional data to produce raw and aggregate datasets for our clients.

Use Cases Our clients leverage our datasets to produce actionable consumer insights such as: - Market share analysis - User behavioral traits (e.g. retention rates) - Average order values - Promotional strategies used by the key players. Several of our clients also use our datasets for forecasting and understanding industry trends better.

Coverage - Asia (Taiwan, Japan, Australia) - Americas (United States, Mexico, Chile) - EMEA (United Kingdom, France, Italy, United Arab Emirates, AE, South Africa)

Granular Data Itemized, high-definition data per transaction level with metrics such as - Order value - Items ordered - No. of orders per user - Delivery fee - Service fee - Promotions used - Geolocation data and more

Aggregate Data - Weekly/ monthly order volume - Revenue delivered in aggregate form, with historical data dating back to 2018. All the transactional e-receipts are sent from the UberEats food delivery app to users’ registered accounts.

Most of our clients are fast-growing Tech Companies, Financial Institutions, Buyside Firms, Market Research Agencies, Consultancies and Academia.

Our dataset is GDPR compliant, contains no PII information and is aggregated & anonymized with user consent. Contact business@measurable.ai for a data dictionary and to find out our volume in each country.

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.

The Measurable AI Amazon Consumer Transaction Dataset is a leading source of email receipts and consumer transaction data, offering data collected directly from users via Proprietary Consumer Apps, with millions of opt-in users.

We source our email receipt consumer data panel via two consumer apps which garner the express consent of our end-users (GDPR compliant). We then aggregate and anonymize all the transactional data to produce raw and aggregate datasets for our clients.

Use Cases Our clients leverage our datasets to produce actionable consumer insights such as: - Market share analysis - User behavioral traits (e.g. retention rates) - Average order values - Promotional strategies used by the key players. Several of our clients also use our datasets for forecasting and understanding industry trends better.

Coverage - Asia (Japan) - EMEA (Spain, United Arab Emirates) - Continental Europe - USA

Granular Data Itemized, high-definition data per transaction level with metrics such as - Order value - Items ordered - No. of orders per user - Delivery fee - Service fee - Promotions used - Geolocation data and more

Aggregate Data - Weekly/ monthly order volume - Revenue delivered in aggregate form, with historical data dating back to 2018. All the transactional e-receipts are sent from app to users’ registered accounts.

Most of our clients are fast-growing Tech Companies, Financial Institutions, Buyside Firms, Market Research Agencies, Consultancies and Academia.

Our dataset is GDPR compliant, contains no PII information and is aggregated & anonymized with user consent. Contact business@measurable.ai for a data dictionary and to find out our volume in each country.

Context

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

Download

kaggle API Command !kaggle datasets download -d ahmedshahriarsakib/uber-eats-usa-restaurants-menus

Content

The dataset has two CSV files -

1. restaurants.csv (40k+ entries, 11 columns)

   • id (Restaurant id)
   • position (Restaurant position in the search result)
   • name (Restaurant name)
   • score (Restaurant score)
   • ratings (Ratings count)
   • category (Restaurant category)
   • price_range (Restaurant price range - $ = Inexpensive, $$ = Moderately expensive, $$$ = Expensive, $$$$ = Very Expensive) - Source - stackoverflow
   • full_address (Restaurant full address)
   • zip_code (Zip code)
   • lat (Latitude)
   • long (Longitude)

2. restaurant-menus.csv (3.71M entries, 5 columns)

   • restaurant_id (Restaurant id)
   • category (Menu category)
   • name (Menu Name)
   • description (Menu description)
   • price (Menu price)

Acknowledgements

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.

Cover Image -

Photo by eggbank on Unsplash

Disclaimer

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.

Inspiration

• How many Restaurants are around the USA?
• What are the Most Popular/Highly Rated Restaurants and menus?
• Is there any relationship between the price level and the popularity of a restaurant?
• Which menus are more expensive?
• Which menus are very common in the USA?

Update Frequency

1. The dataset will be updated weekly

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

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.

Insights from City Supply and Demand Data This data project has been used as a take-home assignment in the recruitment process for the data science positions at Uber.

Assignment Using the provided dataset, answer the following questions:

1. Which date had the most completed trips during the two week period?
2. What was the highest number of completed trips within a 24 hour period?
3. Which hour of the day had the most requests during the two week period?
4. What percentages of all zeroes during the two week period occurred on weekend (Friday at 5 pm to Sunday at 3 am)? Tip: The local time value is the start of the hour (e.g. 15 is the hour from 3:00pm - 4:00pm)
5. What is the weighted average ratio of completed trips per driver during the two week period? Tip: "Weighted average" means your answer should account for the total trip volume in each hour to determine the most accurate number in whole period.
6. In drafting a driver schedule in terms of 8 hours shifts, when are the busiest 8 consecutive hours over the two week period in terms of unique requests? A new shift starts in every 8 hours. Assume that a driver will work same shift each day.
7. True or False: Driver supply always increases when demand increases during the two week period. Tip: Visualize the data to confirm your answer if needed.
8. In which 72 hour period is the ratio of Zeroes to Eyeballs the highest?
9. If you could add 5 drivers to any single hour of every day during the two week period, which hour should you add them to? Hint: Consider both rider eyeballs and driver supply when choosing
10. True or False: There is exactly two weeks of data in this analysis
11. Looking at the data from all two weeks, which time might make the most sense to consider a true "end day" instead of midnight? (i.e when are supply and demand at both their natural minimums) Tip: Visualize the data to confirm your answer if needed.

Data Description To answer the question, use the dataset from the file dataset_1.csv. For example, consider the row 11 from this dataset:

Date Time (Local) Eyeballs Zeroes Completed Trips Requests Unique Drivers

2012-09-10 16 11 2 3 4 6

This means that during the hour beginning at 4pm (hour 16), on September 10th, 2012, 11 people opened the Uber app (Eyeballs). 2 of them did not see any car (Zeroes) and 4 of them requested a car (Requests). Of the 4 requests, only 3 complete trips actually resulted (Completed Trips). During this time, there were a total of 6 drivers who logged in (Unique Drivers)

Context

Well,the data is taken form the machine hack site.It leads us to the problem of finding the traffic problems in the metro cities. It is also about how to regulate the movement of the cabs so as to get control over the traffic problems.

Content

Modern cities are changing. The rise of vehicular traffic has been changing the design of our cities. It is very important to know how traffic moves in a city and how it changes during different times in a week. Hence it is very important to analyse and gain insights from traffic data. We invite data scientists, analysts and people from all technical interests to analyse the traffic data from Bengaluru. The data gives us some information about how traffic moves from source to destination under various circumstances. The data is sourced from Uber Movement. Uber Movement provides anonymized data from over two billion trips to help urban planning around the world.

Acknowledgements

1. Machine Hack

Inspiration

1. How can we manage day to day traffic ? 2 .How the moments of cabs to be regulated ?
2. Awareness about the Use of public transport.

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.

Description of the INSPIRE Download Service (predefined Atom): It is a simple development plan which has been set up in accordance with the simplified procedure pursuant to Section 13 of the Building Code. The content of the plan was merely the expansion of a public road traffic area and modification of its intended purpose by a "footw - The link(s) for downloading the data sets is/are dynamically generated from Get Map calls to a WMS interface".