This statistic shows the average hourly earnings of Uber drivers in the United States from 2017 to 2018. During the 2019 survey, Uber drivers in the U.S. earned on average ***** U.S. dollars per hour before expenses.

Introduction

Uber vs Taxi Statistics: The year 2024 brought major disruptions in terms of shifting the ride-hailing mix further toward a new ride-hailing environment at the expense of the old. Uber has almost three-quarters of the market in the U.S., putting taxis on the brink of extinction. Globally, the valuation of ride-hailing services amounts to nearly US$271 billion, and it is expected to grow further.

This article thus presents some key Uber vs Taxi statistics in 2025 - revenue, usage, driver counts, and future projections that you can have a clear quantitative analysis.

This statistic shows the rideshare vehicle of choice among Uber and Lyft drivers in the United States as of February 2018. During the survey, **** percent of the respondents stated that they drive a Toyota vehicle.

Introduction

Uber Statistics: Uber, the greatest player in ride-hailing, was able to maintain its control of the mobility and food delivery industry in 2024. Uber operates in over 70 countries and more than 10,000 cities, providing services that comprise ride-sharing, food delivery (Uber Eats), freight, and even autonomous vehicle initiatives.

With the surge of new rivals from regional ride-hailing platforms and regulatory turbulence, Uber has, against all odds, held its own as the trailblazer of this gig economy. This article aims to illuminate Uber statistics with respect to the metrics that matter, like revenue, user growth, ride numbers, driver earnings, and so on.

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?

Launched three years after Uber, Lyft was originally a long-distance car-pooling business, launched by Logan Green and John Zimmer. While Zimride, named after the transportation culture in Zimbabwe...

In 2020, ** percent of respondents believed that Uber had either done enough or gone above and beyond for drivers and delivery personnel during the COVID-19 pandemic. In the same period, ** percent of respondents were not aware of anything done by Uber.

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)

In 2020, respondents in the United States were most dissatisfied with the dependability of earnings of working as an Uber driver or delivery person. In the same period, ** percent of respondents were most dissatisfied with app performance and features.

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

ZEV drivers on Uber’s app in Q1 2025 are up over 60% from a year ago.

This statistic shows the driver satisfaction with UberPOOL in the United States in 2017 and 2018. During the survey in 2018, **** percent of the respondents agreed that they are somewhat satisfied with UberPOOL.

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.

The datasets contain three parts: 1) Inputs;2)Outputs;3) README(data dictionary and instructions)

1. Inputs:
- uber_imgs.zip file: Contains screenshots collected from Uber drivers around Birmingham, AL. [will leave it to Mostafa for more details]
- excel_files folder: Contains annotations done by students. Each student was assigned about 60 images to annotate.
- instructions.pdf file: Contains instructions on how to annotate an Uber-trip's screenshot.
2.Outputs:
- MATSim: (https://www.matsim.org/)
- experiments folder:
- experiment_no_uber folder: This experiment simulate traffic without uber driver. Only cars and public transit.
It contains inputs files needed to execute the experiment and "output_events.xml.gz" which contains the output events.
Use "network.xml" (same for all experiments) and "output_events.xml.gz" to visualize the traffic using Via. (https://simunto.com/via/)
- experiment_uber_200 folder: This experiment simulate traffic with 200 uber drivers.
- experiment_uber_400 folder: This experiment simulate traffic with 400 uber drivers.
- experiment_uber_800 folder: This experiment simulate traffic with 800 uber drivers.
- visualization folder:
- D3 folder: (https://d3js.org/)
- demo.html: a demo to run D3 visualization in broswer. It shows uber trips origin-destination from different Zip Codes.
- vis-uber.zip: contains the source code and instructions how to run it.
- Folium folder: (https://python-visualization.github.io/folium/)
- Trajectory_Viz.html: shows some Uber trajectories using Folium heatmap.

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 order to seek stakeholder engagement in a Public Passenger Vehicle Industry study (report linked below in Featured Content) it commissioned in late 2020, the Department of Business Affairs and Consumer Protection conducted an online survey of public chauffeurs (taxi, livery, and ride hail drivers). The survey was open for responses from 3/10/2021 to 4/1/2021.

The 7,021 self-reported responses received are shown in this dataset. Personally identifiable information (PII) written by a responder in a free-text response field was redacted to protect the responder’s identify. Otherwise, self-reported answers are presented as submitted and unedited.

Please e-mail questions or comments regarding the PPV Study or the Public Chauffeur Survey to BACPPV@cityofchicago.org.

In 2020, ** percent of respondents were either very satisfied or somewhat satisfied with their experience as an Uber driver or delivery person during the COVID-19 pandemic. In comparison, a combined ** percent of respondents were left unsatisfied by their experiences.

Graph and download economic data for Employed full time: Wage and salary workers: Taxi drivers and chauffeurs occupations: 16 years and over: Women (LEU0254735400A) from 2000 to 2019 about taxi, occupation, females, full-time, salaries, workers, 16 years +, wages, employment, and USA.

The Australia Taxi Market report segments the industry into By Service Type (Ride Hailing, Ridesharing), By Booking Type (Online Booking, Offline Booking), By Vehicle Type (Hatchbacks, Sedans, SUVs/MPVs), and Country (New South Wales (NSW), Victoria, Queensland, Western Australia, Rest of Australia). Five years of historical data and five-year forecasts are provided.

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.