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.

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.

In the fourth quarter of 2024, *** million people used the Uber app at least once per month. This is a ** percent increase compared to the fourth quarter of 2023. Uber is one of the most popular ride-sharing apps in the world. Based in San Francisco, their global net revenue amounted to ***** billion U.S. dollars in 2023. Contributing to their revenue is the 9.4 billion rides that were delivered via the Uber app that year. In 2022, Uber generated ***** billion U.S. dollars in gross bookings worldwide. U.S. ride-sharing market The ride-sharing market has experienced a giant surge in recent years. The ride-sharing market allows for consumers in need of a ride to instantly call for one via their smartphone and GPS satellites. This is comparable to a taxi service but can in some cases be significantly cheaper. However, drivers for these apps do not usually hold the same licensing requirements as taxi drivers. Uber and Lyft are the two largest companies in this sector, although Uber continues to outperform Lyft. In 2023, Uber's reported global revenue was more than eight times that of Lyft, which recorded *** billion U.S. dollars in revenues.

In 2024, Uber Technologies generated over ** billion U.S. dollars in revenue from its operations in the United States and Canada. The company's revenue has grown in all regions, but the Europe, Middle East, and Africa region has experienced particularly strong year-on-year growth. The mobile transportation network company had more than 171 million monthly users all over the world at the end of that year. Uber leads global ride-hailing market As of 2022, Uber has a ** percent market share for ride-hailing globally, making it the largest player ahead of competitors such as Lyft. This dominance is reflected in its financial performance, particularly in its mobility segment. Uber Technologies generated a revenue of approximately ** billion U.S. dollars from its mobility segment, which includes its ride-sharing operations, which constructs the biggest portion of the company’s revenue. The company’s growth is a part of a trend in the ride-sharing market, which is projected to grow by more than ** percent from 2023 to 2028, reaching an estimated market value of *** billion U.S. dollars. Uber tops U.S. mobility service brand awareness Furthermore, the San Francisco-based company is the most well-known mobility service provider in the United States. Uber is known by ** percent of respondents in the United States. Another California-based company, Lyft, comes in ****** place on this list.

Ridesharing Industry statistics: The ridesharing industries are different companies that include transportation networks and ride-hailing services that provide one-way transportation commonly termed as e-taxis or app-taxis. The well-known and biggest ride-sharing companies are Uber and Lyft. The overall market share of the ridesharing industry in 2022 has accounted for around $95.09 billion to $100.55 billion and is expected to reach a CAGR of 17.2% by the end of 2029 with $305 billion. Currently, ridesharing applications are mostly used across the world, especially in urban areas and almost 36% of Americans are using these apps in their daily life. The following Statistics from several aspects will provide light on why Ridesharing Industry is becoming so popular. Editor’s Choice In the United States, almost 36% of people are the part of Ridesharing Industry in 2022. The top two companies in this industry are Uber and Lyft in the U.S. The Ridesharing market size of North America increased by 68% by the end of 2022 with $13.6 billion. In the U.S. 2022, the share of sales rideshare market of Uber was 71% and Lyft's was 29%. By the end of 2026, the global market share of ridesharing is expected to be $185.1 billion. The monthly services of ridesharing applications were around 26%. This industry mainly includes the Taxi segment and Ride-hailing transportation sector. As of 2023, this U.S. industry has projected to reach $71.78 billion and expects annual growth of 1.07% by the end of 2027 with a $74.91 billion market volume. Currently, 28.1% is the user penetration of this industry in the U.S. As of January 2022, the average sales per customer of Uber were $72 and Lyft was $66.

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.

Ridesharing platform, Uber has been increasing the gender diversity of its workforce, which was 56.5 percent male and 43.5 percent female as of 31 December 2023. The above figures only refer to staff that are employed directly by Uber, and do not include drivers.

We'll customize a Uber Eats dataset to align with your unique requirements, incorporating data on restaurant types, menu items, pricing, delivery times, customer ratings, demographic insights, and other relevant metrics.

Leverage our Uber Eats datasets for various applications to strengthen strategic planning and market analysis. Examining these datasets enables organizations to understand consumer preferences and delivery trends, facilitating refined menu offerings and optimized delivery strategies. Tailor your access to the complete dataset or specific subsets according to your business needs.

Popular use cases include optimizing menu offerings based on consumer insights, refining marketing strategies through targeted customer segmentation, and identifying and predicting trends to maintain a competitive edge in the food delivery market.

Uber Movement provides anonymized data from over two billion trips to help urban planning around the world.

About this Dataset

Data retrieved from Uber Movement, (c) 2017 Uber Technologies, Inc.,https://movement.uber.com

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.

Background

https://d3i4yxtzktqr9n.cloudfront.net/web-movement/static/pdfs/Movement-TravelTimesMethodology-76002ded22.pdf

Monthly report including weekly total dispatched trips and unique dispatched vehicles by base tabulated from FHV Trip Record submissions made by bases. Note: The TLC publishes base trip record data as submitted by the bases, and we cannot guarantee or confirm their accuracy or completeness. Therefore, this may not represent the total amount of trips dispatched by all TLC-licensed bases. The TLC performs routine reviews of the records and takes enforcement actions when necessary to ensure, to the extent possible, complete and accurate information.

App-based ridesharing services (RSSs), exemplified by platforms like Uber, play a pivotal role in modern transportation by offering convenient and on-demand services. The exploration of RSSs necessitates a comprehensive consideration of the inherent spatiotemporal variability within the data. Prior research, however, has tended to analyze the spatial and temporal dimensions separately, with many studies omitting the temporal aspect. This study addresses the gap by using geovisualization techniques to illustrate emerging hot spot analysis in New York City in 2022, derived from space–time data mining. Overall, despite temporal variations in overall RSSs ridership, certain taxi zones maintain distinct ridership patterns. Across the five New York City boroughs (Manhattan, Bronx, Queens, Brooklyn, and Staten Island), Midtown Manhattan and the Brooklyn areas adjacent to Queens exhibit saturated intensifying hot spots, signaling a notable increase in RSSs ridership throughout 2022, surrounded by sporadic hot spots. Conversely, peripheral areas of New York City reveal diminishing cold spots, indicating a decrease in their intensity as cold spots. Furthermore, the study conducts separate spatial and temporal profiling. By presenting the spatiotemporal trends of RSSs, this research complements existing literature and provides valuable insights for more informed interventions. The study also highlights certain limitations that could be addressed in future endeavors.

Granular transaction-level receipt data for ride hailing industry across key players such as Uber, Lyft, Grab, Didi, 99, Gojek, etc. particularly focusing on the emerging markets : South East Asia, Latin America, Middle East, India, and Japan.

Metrics included such as : distance travelled, average order value, frequency of rides, service fee, tips, promotional discounts, geolocation data etc.

Most of our clients consist of the key ride hailing players in the industry, as well as financial institutions, market research agencies, consultancies, academia.

Feel free to refer to our blog on our website to see what type of consumer insights can be extracted from our dataset.

This dataset includes the locations of businesses that pay taxes to the City and County of San Francisco. Each registered business may have multiple locations and each location is a single row. The Treasurer & Tax Collector’s Office collects this data through business registration applications, account update/closure forms, and taxpayer filings. The data is collected to help enforce the Business and Tax Regulations Code including, but not limited to: Article 6, Article 12, Article 12-A, and Article 12-A-1. http://sftreasurer.org/registration

In the fourth quarter of 2023, Uber reported over *** billion trips completed by their mobility and delivery services worldwide. The number of trips completed by Uber has been on an upward trend over the past two years and the total number of trips completed in 2023 reached more than *** billion.

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.

Context

This provides information about Crime (as measured by NIBRS) and Uber entry by city.

Acknowledgements

Weber, Bryan (2019), “Data for: Uber and Urban Crime”, Mendeley Data, V1, doi: 10.17632/4vfygpw58y.1

By far the most common method to access the ride-sharing platform Uber in the United States is via smartphone; around 17.7 million U.S. adults had accessed Uber via smartphone as of April 2017 – significantly higher than the next most popular platform, desktop computers, with 6.8 million users.

The U.S. ride sharing market

Uber is the largest ride-sharing platform in the United States, accounting for just under 70 percent of the total market as of October 2018. However Lyft, the next-largest ride sharing platform in the U.S., has seen significant growth in ridership over the last five years, narrowing the gap between it and Uber.

The global ride sharing market

While Lyft may be gaining some ground on Uber in the U.S., globally the distance between Uber and Lyft is much larger. This is due both to the fact that Lyft currently only operate in North America, and the strong global growth that Uber has seen over the last few years. Uber’s global number of bookings per quarter more than doubled from the fourth quarter of 2016 to the fourth quarter of 2018, leading to their net revenue almost doubling from 2016 to 2018.

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.

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.

Return-On-Assets 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.