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.

As of October 2020, there were around 120,000 Uber drivers in Chile. This is 41 percent more than the 85,000 Uber drivers registered in January 2019. After a decline following the COVID-19 outbreak, Uber's revenue in Latin America has increased in the third quarter of 2020.

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 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.

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.

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.

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?

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)

This statistic shows the number of drivers on the Uber platform versus the Cabify platform in Chile in 2017. By the end of 2017, Uber had employed a total of ****** drivers, whereas Cabify counted approximately ****** drivers to provide this service.

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

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

In the fourth quarter of 2023, the San Francisco-based ride-sharing service, Lyft, had over ** million active riders, an increase of about ten percent compared to the fourth quarter of 2022. An active rider is defined as a user that books at least one ride in a quarter.

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.

As of October 2020, Uber had *** million registered users in Chile. This is ** percent more than the * million users recorded in January 2020. Likewise, the number of Uber drivers in the South American country has also increased.

Platform companies disrupt not only the economic sectors they enter, but also the regulatory regimes that govern those sectors. We examine Uber in the United States as a case of regulating this disruption in different arenas: cities, state legislatures, and judicial venues. We find that the politics of Uber regulation does not conform to existing models of regulation. We describe instead a pattern of disrupted regulation, characterized by a consistent challenger-incumbent cleavage, in two steps. First, an existing regulatory regime is not deregulated but successfully disregarded by a new entrant. Second, the politics of subsequently regulating the challenger leads to a dual regulatory regime. In the case of Uber, disrupted regulation takes the form of challenger capture, an elite-driven pattern, in which the challenger has largely prevailed. It is further characterized by the surrogate representation of dispersed actors—customers and drivers—who do not have autonomous power and who rely instead on alignment with the challenger and incumbent. In its surrogate capacity in city and state regulation, Uber has frequently mobilized large numbers of customers and drivers to lobby for policy outcomes that allow it to continue to provide service on terms it finds acceptable. Because drivers have reaped less advantage from these alignments, labor issues have been taken up in judicial venues, again primarily by surrogates (usually plaintiffs’ attorneys) but to date have not been successful.

This dataset includes accidents with Google, Uber, Tesla, and Waymo autonomous cars. Accordingly, the inventory of accidents involvs highly automated vehicles. The database comprises 40 accidents from all over the world, which occurred between 2016 and 2021, and consists of the following fields: • year: year of accident 1..2100; • month: month of accidents (1..12); • day: day of the accident (1..31) • hour: hour of the accident (0:00..23:59) • period of the day: hour of the accident (0,00..23,59) • country of the accident: e.g. USA, China, etc. • GPS coordinates: e.g. 39°18′N 116°42′E • state: e.g. Florida • state: e.g. Florida • description: e.g. 23-year-old Gao Yuning was killed when his Tesla, with Autopilot mode engaged, slammed into the back of a stationary road sweeping truck parked at the edge of the road. • death: number of fatal injuries of the accident • • Serious injury: number of seriously injured persons related to the accident • slight injury: number of slight injury related to the accident • Uber driver: number of Uber driver involved • Total number of vehicles: number of vehicles involved • Environment: flat, elevating, mountain • Environment code: flat-1, elevating-2, mountain-3 • Period of the day :Day, evening, night • visibility: 1 clear, 0 not clear • Weather condition: rainy, snow, sunny, haze • season: summer-1, spring-2, autumn-3, winter-4 • season rate: summer-1/4, spring-2/4, autumn-3/4, winter-4/4 • speed limit: the regular speed limit at the location of the accident • speed condition of highly automated vehicle: the actual velocity of the investigated highly automated vehicle • Normalization of speed: normalized value of speed condition • Model of highly automated vehicle: the name of the model of the investigated highly automated vehicle • Autopilot mode:yes-1, no-0 • Age of highly automated vehicle: number of years from manufacturing the highly automated vehicle • Type of accident: frontal, rear-end collision, sideswipe collisions, chain-reaction collision • curvature: straight, in curve • Total number of vehicles: number of vehicles involved • Technical reasons: brief description, introducing the causes • Sources: web link

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.

Studying how human drivers react differently when following autonomous vehicles (AV) vs. human-driven vehicles (HV) is critical for mixed traffic flow. This dataset contains extracted and enhanced two categories of car-following data, HV-following-AV (H-A) and HV-following-HV (H-H), from the open Lyft level-5 dataset.

As of December 2019, Uber had ** thousand registered drivers in the Dominican Republic, while over *** thousand passengers used the famous transportation app in the previous three months. In the last quarter of 2019, the number of Uber rides worldwide amounted to approximately *** billion trips.

The pr segmentation of the taxi market includes:Vehicle TypeMotorcyclesCarsOther vehiclesService TypeRide-hailingRidesharing Recent developments include: March 2023: Uber said that it would pay $3.1 billion to acquire Careem, the top ride-hailing service in the Middle East and North Africa. The purchase will give Uber a strong footing in the area, which is one of the regions for ride-hailing that is expanding the quickest., October 2021: A new alliance between Uber and the rental car company Hertz was announced. By 2023, Uber's ride-hail drivers will have the opportunity to rent 50,000 Tesla Inc. vehicles thanks to this partnership between the two businesses. Starting on November 1, Los Angeles, San Francisco, San Diego, and Washington DC will be the first places in the US where Uber drivers can rent a Tesla through Hertz., October 2021: Ola purchased GeoSpoc, a provider of geospatial services, to create the next generation of location technology, which will include vector, three-dimensional, and real-time maps., January 2021: Grab and Panasonic together announced plans to employ Panasonic air purifiers in cabs to improve passenger experience.. Key drivers for this market are: Rising urban populations: The increasing number of people living in cities is fueling the demand for convenient and reliable transportation.

Technological advancements: Mobile apps and GPS tracking have made taxis more accessible and efficient.. Potential restraints include: HIGH COST OF CHARGE AIR COOLER AND HEAT EXCHANGER TECHNOLOGIES 46, INTEGRATION CHALLENGES FOR AUTOMOTIVE CHARGE AIR COOLER AND HEAT EXCHANGER 47. Notable trends are: The Market is Driven by Online Bookings.