Uber Rides Dataset for Data Analysis and Visualization

Columns:

• START_DATE : Date and time when the ride started
• END_DATE : Date and time when the ride stopped
• CATEGORY : business/personal
• START : start location
• STOP : destination
• MILES : distance covered
• PURPOSE : purpose of the ride

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 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 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.

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.

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

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.

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 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.

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?

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.

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.

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 2024, Uber Technologies generated more than ** billion U.S. dollars in revenue from its mobility segment, which includes its ride-sharing operations. The delivery segment, which includes Uber Eats operations, generated around ***** billion U.S. dollars in revenues that year. Market leadership in food delivery Uber's delivery service 'Uber Eats' has been able to build on the boost it received during the COVID-19 pandemic. The segment's revenue more than doubled in size between 2020 and 2021, growing by an additional ** percent in the following two years. Uber Eats has been able to establish itself as the global leader in food delivery services, generating around *** billion U.S. dollars more in annual revenues than its closest rival, Delivery Hero. Reaching profitability Uber has been able to establish itself as the global market leader in both of its core business segments, food delivery and ride-hailing. In the ride-hailing sector, Uber holds an even stronger position than in the food delivery business, controlling around a quarter of the global market. Its strong market position and favorable operating environment in 2023, allowed Uber to generate a net profit of *** billion U.S. dollars in 2023. This was the first time Uber had been profitable since 2018, following several years of net losses reaching as high as *** billion U.S. dollars in 2022.

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 connects consumers with independent providers of ride services for ridesharing services; and connects riders and other consumers with restaurants, grocers, and other stores with delivery service providers for meal preparation, grocery, and other delivery services. The company operates through three segments: Mobility, Delivery, and Freight. The Mobility segment provides products that connect consumers with mobility drivers who provide rides in a range of vehicles, such as cars, auto rickshaws, motorbikes, minibuses, or taxis. It also offers financial partnerships, transit, and vehicle solutions offerings. The Delivery segment allows consumers to search for and discover local restaurants, order a meal, and either pick-up at the restaurant or have the meal delivered; and offers grocery, alcohol, and convenience store delivery, as well as select other goods. The Freight segment connects carriers with shippers on the company's platform and enable carriers upfront, transparent pricing, and the ability to book a shipment, as well as transportation management and other logistics services offerings. 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.

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.

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

This statistic illustrates the distribution of Uber employees in the United States from 2017 to 2020, sorted by ethnicity. In 2020, 37.2 percent of U.S. Uber's employees were of Asian ethnicity. The majority of employees were white.

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 ---