Uber Trips Between Airport and City

Posted on Feb 25, 2021

(photo source: Uber's website)

Shiny App

Github Repository

Uber and its Problem

Uber is a very popular alternative to traditional taxi companies. With a few taps on a phone screen, drivers easily connect with riders. A type of trip for which car services are often needed is the trip to or from airports. People usually do not leave their own cars at airports, so they rely on car services for transportation. Due to the great demand, Uber can potentially gain a lot of revenue from rides to and from airports. 

The issue is that this potential revenue is not actualized. Uber has a problem that hurts its income. This problem is that very often people who request rides to or from the airport are let down by Uber; there are no drivers available and/or drivers cancel on them. What this means is that Uber is making much less money than it has the potential to. What this also means is that people may become less inclined to use Uber in the future, after experiencing it as an unreliable company or hearing about it as so.

Luckily, there are ways to help reduce the problem; and pinpointing the problem is helpful to come up with more specific, targeted solutions. To pinpoint where and when the problem mainly lies, data from Uber can be analyzed. Based on what the data shows, solutions can be targeted, and therefore as effective as possible.

The Dataset

The dataset used for analysis is a masked data set which is similar to what data analysts at Uber handle. The source of the dataset is a company called upGrad. It contains 6,745 observations of trips between city and airport during regular weekdays of a week in July 2016. So, it is used as a sample set representing Uber trips between city and airport when it is not a holiday or weekend. The data looks at trip requests and tells us whether a request was accepted and completed, accepted and then cancelled by the driver, or was unaccepted due to no cars being available.

Taking a look at the Overall Loss

From the bar graph, we see that out of all requested Uber trips, more trips don't end up happening than do end up happening. Uber could be completing over 100% more trips in this category of trips than it does. This means that Uber is missing out on a lot of revenue. This also means that most of the time, Uber can't be relied upon for trips to and from the airport, which is detrimental to Uber's reputation.

From comparing the amount in the "cancelled" category to the amount in "no cars available" category, we see that the bigger issue is the nonavailability of cars. This means that Uber has to focus its efforts into getting more drivers and getting its current drivers to be available more often.

When looking at the "cancelled" category, we see that cancellations by drivers is mainly an issue when the pickup point is the city area. This can be for a number of reasons, although we don't have the data on why the drivers cancel. When looking at the category of "no cars available", we see that nonavailability of cars is a significant problem in both the airport and city pickup points. But in terms of count, it is a bigger problem by the airport pickup point.

In conclusion, both cancellations and nonavailability of cars are quite common. Nonavailability of cars is Uber's top issue, particularly by the airport. When analyzing requests with city as the pickup point, cancellations and nonavailability are both big issues. When analyzing requests with airport as the pickup point, the problem of cancellations pales in comparison to the problem of nonavailability.

When and Where the Problems are most Prevalent

Here are two bar graphs displaying a closer look at the categories "cancelled" and "no cars available". These graphs incorporate time and place so that we know both where and when the issues are happening most.

We see here that when the day is divided into six timeframes of equal length, for the city, the main timeframe during which requests aren't completed is 4:00 AM-11:59 AM. In particular, cancellations are more common than nonavailabilities, but they both have pretty high counts, which is a finding consistent with our previous analysis on the more generic bar chart. For the airport, the main time of day during which requests aren't completed is 4:00 PM-11:59 PM. Nonavailabilities has a significantly higher count than cancellations, which makes sense based on the previous generic analysis.

We see that Uber would greatly benefit from more drivers completing trips in the city area during the early mornings until noon and in the airport area during the evenings after 4:00 PM.

We also see, from examining the total requests, including completed trips, that those particular times of day in those particular locations have the highest amounts of requests. Therefore, we can conclude that in general, many drivers are needed during mornings in the city and evenings in the airports.

Here is a visualization of the "supply-demand" gap:

"Supply V. Demand"
"Supply V. Demand"

In both charts we see that throughout the day, besides for the 12:00 AM - 3:59 AM timeframe, a pretty similar amount of requests are completed during each time interval. We see that the supply barely correlates with the demand, as a similar amount of requests are completed when the demand is huge as when the demand is small. It would be beneficial for Uber to emphasize when and where the demand is high to its current drivers and potential new drivers. The supply-demand gap should be more consistently small throughout the day so that a greater percentage of requests in each timeframe throughout the day will be fulfilled. This way, Uber would be closer to gaining as much revenue as possible, and it would be known as more reliable.

Uber's Reliability

As noted earlier, it is very important for Uber to have a reputation for being reliable. If Uber is known to be unreliable for trips between airport and city, people may turn to other car service options before turning to Uber. Additionally, if people are let down by Uber often enough, they may not try Uber again in the future for other types of trips.

Through displaying the proportions of request statuses per timeframe, these pie charts illustrate Uber's reliability for trips between city and airport. We can see that 12:00 PM - 3:59 PM is the only timeframe during which more than 50% percent of requests are completed. This means that for the majority of the day, Uber is pretty unreliable, as less than 50% of requests get completed. Most people end up unsatisfied with Uber during most of the day for this category of trips. It would be greatly beneficial for Uber to increase its reliability. Having frustrated customers is never good for the growth of a company, as the company can lose its customers and not gain new ones.

Using the Analysis for Company Benefit and Growth

This data and analysis can be used by Uber for internal discussion, and also can be used by companies similar to Uber, as it tells companies when drivers are most needed at the airports and when drivers are most needed to bring people to airports from cities. The data and analysis can also provide insights for drivers. (As noted earlier, this analysis applies to weekdays.)

Uber can provides incentives for drivers to fulfill requests in the cities in the morning hours and airports in the evening hours, like bonuses or salary boosts. Uber can also impose penalties on drivers for accepting and then cancelling requests, unless the cancellation is done for a very legitimate reason. Cancellations can frustrate customers even more than no cars being available. It is more of a let-down for a customer to think they have a ride and then be cancelled on, than for them to know right away that there are no cars available. It is possible that by now, the year 2021, Uber has implemented new policies regarding cancellation. Perhaps there are already, or if not there should be, policies about drivers getting penalties after cancelling a certain percentage of rides they accept. Drivers do need the right to cancel, since they can encounter a customer with a very low rating or a customer that they can't find and get in touch with. The point is that they shouldn't do it often enough that it can hurt Uber's reputation and revenue.

Uber should also try to attract new drivers and create incentives for them to join the company, and particularly incentives to accept trips between airport and city. Uber can attract new drivers by showing that they have so many requests that aren't being fulfilled. People can be attracted to join the company if they know that they're basically guaranteed to get driving jobs.

A solution to improve Uber's reputation for reliability is to promote bookings in advance. Uber actually introduced a feature in 2016 that allows for scheduling rides in advance. When people know that scheduling a car in advance is a choice that they can choose, they can see Uber as more dependable. If they choose to do book in advance, the frustrating scenario of no cars being available, which can turn people away from relying on Uber in general, can be avoided.

Further Analysis Ideas

It would be interesting if there was data on why drivers cancel. It would be beneficial to analyze why drivers cancel; and based on the reasons, come up with solutions to stop these cancellations from happening.

It would also be interesting to analyze how weekend and holiday data differ from weekday data. I would want to know if weekends and holidays have the same or different problems as weekdays and how the magnitudes of the problems compare.

Additionally, I would like to point out that now it is 2021. This analysis is mainly beneficial for the Uber company in 2016 and soon after. I think it would be interesting to analyze Uber's data since then to see if trends have shifted, and if those trends shifted after the introduction of ideas like those I mentioned and/or other changes in the company.

About Author

Leave a Comment

No comments found.

View Posts by Categories


Our Recent Popular Posts


View Posts by Tags

#python #trainwithnycdsa 2019 airbnb Alex Baransky alumni Alumni Interview Alumni Reviews Alumni Spotlight alumni story Alumnus API Application artist aws beautiful soup Best Bootcamp Best Data Science 2019 Best Data Science Bootcamp Best Data Science Bootcamp 2020 Best Ranked Big Data Book Launch Book-Signing bootcamp Bootcamp Alumni Bootcamp Prep Bundles California Cancer Research capstone Career Career Day citibike clustering Coding Course Demo Course Report D3.js data Data Analyst data science Data Science Academy Data Science Bootcamp Data science jobs Data Science Reviews Data Scientist Data Scientist Jobs data visualization Deep Learning Demo Day Discount dplyr employer networking feature engineering Finance Financial Data Science Flask gbm Get Hired ggplot2 googleVis Hadoop higgs boson Hiring hiring partner events Hiring Partners Industry Experts Instructor Blog Instructor Interview Job Job Placement Jobs Jon Krohn JP Morgan Chase Kaggle Kickstarter lasso regression Lead Data Scienctist Lead Data Scientist leaflet linear regression Logistic Regression machine learning Maps matplotlib Medical Research Meet the team meetup Networking neural network Neural networks New Courses nlp NYC NYC Data Science nyc data science academy NYC Open Data NYCDSA NYCDSA Alumni Online Online Bootcamp Online Training Open Data painter pandas Part-time Portfolio Development prediction Prework Programming PwC python Python Data Analysis python machine learning python scrapy python web scraping python webscraping Python Workshop R R language R Programming R Shiny r studio R Visualization R Workshop R-bloggers random forest Ranking recommendation recommendation system regression Remote remote data science bootcamp Scrapy scrapy visualization seaborn Selenium sentiment analysis Shiny Shiny Dashboard Spark Special Special Summer Sports statistics streaming Student Interview Student Showcase SVM Switchup Tableau team TensorFlow Testimonial tf-idf Top Data Science Bootcamp twitter visualization web scraping Weekend Course What to expect word cloud word2vec XGBoost yelp