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Uberdata: The Hidden Cost of Cabs

What up humans?! Bradley Voytek here bringing the #uberdata back! The last post was a nice warm-up.

This time I’m not pulling any punches. It’s about to get real. This is the post I’ve wanted to do since I first started here.

You see, most of the time we here in the uberoffices are pretty happy. We’re happy because we’re building something that makes all of you happy. But every now and then there’s a darkness that comes upon us when someone on the interwebs complains about our cost.

Which got me to thinking. We do cost more than a cab. This is because we are offering more than a cab. In fact, we offer a lot more: reliability, customer support, and, of course, style and comfort. Importantly, we also reduce frustration.

But how does Uber really compare to cabs?

Uber Uberdata Bradley Voytek - Used with permission from Microsoft

Seriously, when was the last time someone from a cab company helped you out after you tweeted about a bad cab ride? How many times have you called a cab company to request a car, only to have it never show up?

So a few weeks ago I started digging a bit. We all know that Uber is quick and reliable, but we didn’t have a quantitative comparison between our performance and that of cabs.

What I found was pretty amazing.

tl;dr version: Cabs are slow, unreliable, and frustrating. Even when you’re trying to flag one down. Yes, Uber may cost more, but if you value your time and peace of mind even in the slightest, the benefits of Uber far outweigh the extra costs.

 

In this post I explore the hidden cost of cabs and weigh those costs against our performance data.

To start, let’s look at some numbers for cab dispatch times. According to the City and County of San Francisco Office of the Controller 2005 Taxi Commission Survey Report, for dispatched cabs:

  • 27% of cabs show within 15 minutes
  • 63% of cabs show within 30 minutes

Now it shouldn’t come as much surprise that only 34% of the people surveyed in the report said that they could “almost always” get a cab in San Francisco “in a reasonable amount of time”. (43% said they “sometimes” could, and 23% said “usually not”.)

How do those cab pick up times compare against Uber’s? Let’s take a look:

Uber Uberdata Bradley Voytek

What’s that?! 94.62% of our cars show up within 15 minutes? And 99.98% show up within 30? Cabs are only at 27% and 63%, respectively? I’m sorry, but that’s just sad.

For the flip-side comparison, 27% of our cars show up within 5.4 minutes and 63% within 8.5 minutes.

Those are our data based upon tens of thousands of rides. But the nice thing about being a data-oriented company is that we can get a lot more detailed.

Here’s our data broken down by minutes until car arrival:

Uber Uberdata Bradley Voytek

But it gets even better. The data I’m showing you from Uber are for all rides since our founding. But like Travis said in the recent Wired article about us:

“At the end of each day, Uber creates charts to analyze how accurately it was able to predict demand for rides throughout the city versus how high demand actually turned out to be. Using these charts, the company refines the prediction algorithm, so Uber gets better and better at estimating how many cars will be needed in the city on certain days and at certain hours.”

Watch me back that statement up with math.

Here’s a chart showing the percentage of our cars that show up in under 10 minutes, broken down by month:

Every month we get better at making sure you get a car faster.

If you rode an Uber in March (which thousands of you did), you had an 80% chance of getting picked up within 10 minutes.

 

“But Uber,” you say, “what about hailing down a cab? Sure your cars are faster than dispatched cabs, but it’s so easy when I can just go out to the corner and flag a cab down!”

Sure, that sounds reasonable. But is it really that easy?

According to the report, as one would expect, the numbers for flag downs are better than dispatch:

  • 59% of cabs show within 15 minutes
  • 88% of cabs show within 30 minutes

But those numbers still don’t compare favorably to ours!

Uber Uberdata Bradley Voytek

Good job guys!

Apparently, however, only 35% of all cab rides taken in SF were flag downs, so most of you don’t bother trying to flag down a cab anyway!

Also, keep in mind that the numbers in that report don’t take into account the fact that you can book an Uber from work, home, the bar, or wherever before you even have to step outside and look for a cab to hail.

The data from that report are somewhat in contrast to the San Francisco Municipal Transportation Agency 2006 Taxi Availability Study (PDF). They found that:

  • 64% of cabs show within 10 minutes
  • 82% of cabs show within 15 minutes
  • 99% of cabs show within 30 minutes

But those numbers are deceptive, because they’re only for cabs that actually showed up after dispatch promised a car. The SFMTA report found that 35% of dispatched cabs just never arrive!

35%.

Every third time you call, the cab they say is coming to pick you up just straight up won’t.

It gets even worse. That report breaks data down by specific days and times. Weekend nights? Horrible times to try and get a cab:

  • 12% of cabs show within 10 minutes
  • 22% of cabs show within 15 minutes
  • 27% of cabs show within 30 minutes
  • 72% (!!!) of cabs never show up

Holy. Crap. The vast majority of the time you call dispatch (if you can even get someone to answer the phone), your cab’s just not ever going to come. And if it does, it’s going to take well over 30 minutes.

Gonna say that again.

On weekend nights, 72% of cabs just don’t show up.
Even for normal work days, 35% of cabs never show.

 

(Although according to a report from the Goldman School of Public Policy at Berkeley in 2007, titled “San Francisco’s Taxi Dispatch Service: Improving Reliability and Response”, the numbers reported in the SFMTA report are probably not statistically sound… But they do give us a good place to start. Nevertheless, please note that we’re very grateful for the hard work done by the SFMTA to collect these data. Without their diligent work we would not be able to revel in the irony of using their data like this.)

What’s wild is that Uber doesn’t even really have a concept of “no-shows”. If you open our app and we don’t have a car available in your immediate area, then we tell you that. We don’t make promises we can’t keep!

But to be fair… sometimes taxis get waylaid by crazy assassins. So you know, they’ve got that to deal with.

Okay, okay. Enough rubbing it in. What does all of this mean for you?

First of all, with the numbers from these reports and with those from our ride database I can run some simulations. That is, I can simulate millions of imaginary Uber vs. cab rides using the statistics at hand.

The idea is simple: given our knowledge about how long it takes cabs to show up (and how often they don’t), and given our knowledge of the statistical distribution of our own pick up times, how much time would you have saved, on average, if you’d used an Uber instead of a cab?

Uber Uberdata Bradley Voytek

The median time saved by taking an Uber instead of a cab in San Francisco is:

  • ~4.8 minutes compared to hailing a cab
  • ~16.2 minutes compared to calling dispatch
  • ~54 (FIFTY. FOUR.) minutes compared to calling dispatch on a Friday or Saturday between 6pm and midnight

Unreal. Seriously I’m not making these numbers up. It’s the actual data collected by the SFMTA compared to our actual pick-up times. Even on Friday and Saturday nights we still manage to pick up 90% of our riders in 13.1 minutes. And hell, the median length of Uber ride during weekend nights is only 10.2 minutes. That means:

You save an average of 54 minutes by taking an Uber versus a cab on weekend nights. You can request an Uber and get to your destination two and a half times in the amount of time it takes for the average cab to show up.

 

Here are some fun things you can do with 54 minutes:

  • Fly from San Francisco to LA
  • Walk across 1/3 of San Francisco
  • Run 6-8 miles
  • Watch an episode of Lost
  • Read about 13,500 words (a short story)
  • Listen to The Dark Side of the Moon
  • Build a cake
  • Get a massage
  • NOT WAIT AROUND FOR A DAMN CAB

Even on normal days, you still have a 38% chance of getting an Uber and getting to your destination before a cab would have showed up. The cab numbers are pretty bad, but my guess is that most of us have had a “good enough” view of them for a long time. It’s not until you experience how much better the process can be with Uber that you realize how bad it can be with cabs.

So yeah, Uber costs twice as much as a cab ride. But the median Uber ride only costs $24. That means you’re paying $12 more than you would for a cab.

How much free time is $12 worth to you? 30 minutes? An hour?

How much frustration are you willing to accept to save $12? What if you’ve got an important meeting, but there’s only a 65% chance that the cab you call will even show up?

What if you’ve got a date on Saturday night, but there’s only a 28% chance a cab will show up?

Forget about the amenities and style that we bring. Those are great. But we’re also giving you some time back, freeing up your day just a little more. Getting rid of just a little bit of hassle and frustration. How much is your time and stress worth to you?

If you do a straight “time = money” calculation, if you earn the median per capita income for San Francisco ($44,373), you would have earned $19.20 in those 54 minutes you wasted waiting for a cab.

$12 doesn’t look so bad now, does it?

Questions or comments on this? Leave them below. For anything else, hit me up on the twitters, my blag (Oscillatory Thoughts), the interwebs, or the emails.

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StefanKarpinski 5 pts

You're just in the wrong city. Title should be amended to "SF Cabs". In New York it's just a matter of stepping up to the curb and raising your hand ;-)

ologhaiofmordor 5 pts

 StefanKarpinski  That's true only in Manhattan south of Harlem and at the airports. Elsewhere in NYC, millions of people can't hail a cab and make do with phoning for a livery vehicle.

areyoukiddingme2012 5 pts

Ironic, the picture above show's Five People (in an Uber Cab?)---the rules stipulate AT NO TIME can more than 4-people ride in Uber....once again, more "smoke" behind the curtain of OZ.

hubelau 6 pts

I'd love to see you post these statistics in Chicago... especially beacuse it appears that your drivers here tend to stay localized downtown despite riders needing to picked up from points wider spread throughout the city

Have to calculated a "Uber is worth it IF they can pick you up in less than X minutes" metric yet?

sgSF86 5 pts

Great analysis. One small issue: comparing the $12 difference in fare to the $19 you'd earn instead of waiting. Wages (from your source) are pre-tax, expenditures are post-tax. That means that, given your 40 hr/wk assumption and a 35% effective tax rate (in CA), you'd roughly break even in 54 minutes of work. This is probably counter-balanced by the fact that the median income of taxi-takers is higher than the median income of the population.

verbal 5 pts

the sfmta data used were surveys, which usually contain skewed data. the people who took the time to fill out the survey probably has had either really bad or really good experiences with cabs. I'm betting on the former. if this post and it's conclusions (54 mins) are to be mathematically sound, how can you base your analysis on a survey?

bradleyvoytek 5 pts

verbal excellent points. Does this mean you're rejecting my paper for publication? :P Seriously though, I was concerned about the underlying distribution of the data. Not necessarily about the bias in responders, but definitely the distribution. First of all, we work with the data we've got to the best of our ability. If the SFMTA isn't adhering to correct polling or statistics practices, I can't do anything about that. They're basing business and legislative decisions on these kinds of data though, so I *hope* they're using best practices. That said, what's *not* in the post (for the sake of not boring everyone except the math nerds to death) is that, in my simulations, I used a variety of underlying distributions. For example, our ride times are not normally distributed, but rather have an approximately gamma distribution. I used this to fit a variety of plausible distributions based on the means and times given in the SFMTA data (where "mean" is a poor metric, of course). It's against those simulated distributions that I compared our results.

verbal 5 pts

bradleyvoytek verbal

its not really about the distribution, its just the general "survey as data" i have a problem. the whole article is constructed to convince the reader that they are actually saving more money by using uber, because their time is valuable.

there are two problems i see with that:

1) the numbers quoted for money and time saved are likely incorrect. well, you just dont know.

2) converting the time saved with uber into a monetary value is like the MPAA and RIAA counting every pirated download as lost revenue. there are only a few jobs where you can earn money on a weekend evening on the side of the street and none of my friends who use uber is in that line of work (that i know of).

having said that, i use uber, love uber, but as data analysts we owe it to the internet to not invoke the power of math unless it is sound. my own experiences with uber has been that the service is better than cabs and they generally come faster. it is, however, a luxury service that as Francisco Dans said, "is not for everyone".

do you guys have a presentation on the cab prediction algorithm? would love to get a peek at it. keep up the good work over there!

bradleyvoytek 5 pts

verbal ah, I see what you're saying. Well, if you read the report I link to, you'll find the methods of data collection:

"A total of 636 dispatch calls were made for the dispatch survey. The sample of locations was distributed across 9 geographic regions of the city specified by the Taxi Commission. The results were weighted to correct for oversampling in areas of the city representing a lower volume of actual dispatch service, and to correct for oversampling during peak periods (Friday and Saturday evenings). For the flag down survey, surveyors made a total of 300 attempts to flag down taxis in 42 locations with heavy foot traffic, including Fisherman's Wharf, North Beach, the Financial District/Embarcadero, Union Street, the Marina, the Geary Corridor, South of Market to SBC Park, the Mission, Downtown, West Portal, Laurel Heights, UCSF, and the Richmond District. The attempts were made throughout the week and times of day, and the results were not weighted. To determine availability at hotel taxi stands, a total of 120 observations of taxi activity at 12 hotel stands were conducted. Observations took place throughout the week during morning, afternoon, evening, and late night hours (Friday and Saturday only)."

Not a survey. It's a proper random sampling. No responder bias there.

verbal 5 pts

bradleyvoytek ah, thank you for clarifying. my apologies. i didn't know they had observers. from name of the report, it sounded like they asked people how long it took them to get a taxi.

i don't know how much this effects the results without doing some math, but do you think that having "observers" flag down taxis themselves would be a problem, as they are adding to the behavior they are trying to observe. thanks for taking the time to respond. I'm not trying to be difficult, but am just interested in this stuff.

bradleyvoytek 5 pts

verbal seriously, no worries! I don't think you're being difficult. We're just working through the problem :)

So the possibility that the observers were behaving differently can't be ruled out. But one would hope that with enough observers any behavioral differences would be "smoothed out" unless there was a systematic bias in the way the observers were instructed. But this goes back to my first point which was that we can really only do so much with the data at hand and hope that those who went before us used appropriate techniques (as flawed as that assumption may be).

And I forgot to respond to your first point about our prediction algorithm! Sorry! We may very well share those data at some point in the near future. Maybe open source the code. If we do we'll definitely let people know on our blog.

verbal 5 pts

bradleyvoytek no one is going to read the code, just put up some slides highlighting the ideas that other people would want to steal :)

also, i just realized something. i guess depending on how you model it, but i dont think flagging down cabs are independent events since once someone takes a cab, it is unavailable for someone else. it may only be dependent if you are measuring something specific to one cab though.

bradleyvoytek 5 pts

verbal haha ok. We'll do a post about that soon enough!

Francisco Dans 5 pts

I've just come across this company via Tim Ferriss, and I really love the concept and the innovation in it (and that includes your pricing as well). But I think you should admit that the service is not for everyone and that whoever wants the commodity of a private driver has to pay for it. Trying to please everyone hardly ever works. I believe it doesn't help you a lot either having to compare yourselves to a cab company, and I don't think it's in your interests that your clients have the idea of being in an expensive cab.

Congratulations for an impressive idea. I would love to see it functioning here in London.

dansd.com

bradleyvoytek 5 pts

Francisco Dans fair enough. But this post wasn't meant to compare our *service* to cabs. It's more of a way of contextualizing the cost. Like HenryBryant says below you, there are many intangibles, but those require actually being in one of our cars.

HenryBryant 5 pts

In addition to style and reliability, the drivers are on a consistently different level than cab drivers. I've never had an Uber driver heinously break traffic laws and then complain about other drivers, complain to me about his job like he's being forced to do it, make racist or sexist remarks, or kick me out of the car after learning that I want a ride to Bernal Heights.

bradleyvoytek 5 pts

HenryBryant It would be nice if I could factor that into my analyses. That said, those kinds of intangibles really contribute to that "ooohhhh... *now* I get it" experience you have after you first try Uber out.

plankers 6 pts

As a regular visitor to SF I second all of this. Outside of touristy areas, and after midnight on weeknights, cabs are a real pain to use. Especially on the return trip to a hotel, and especially once MUNI is done running or on infrequent schedules. There have been many nights I've spent those 54 minutes walking the 1/3 of the way across the city, after I gave up on being ignored by cabbies blasting past me or never showing up when called. For me you're generally positioned against a rental car, because if you offer predictable transportation in a finite amount of time you're a hell of a lot less hassle than a car or cabs. And with you I don't have to worry about having a couple beers.

Anyhow, good job with the data. I think all my regression lines will be labeled "math" from now on. :)

bradleyvoytek 5 pts

plankers If only I could get away with graphs like that in my scientific publications...

DavidKnox 5 pts

nice. as an early uber supporter, and one who did a pretty good job at justifying the 2x cost on his own, your breakdown empirically proves what i've loved about you guys since the beginning - you fucking rock.

i "think" i had four ubers last weekend, which brings my total to - oh, who cares.

take care guys.

your #1 fan - uber dave knox. your first testimonial, and still a big supporter of your biz model.

l8r,

dave

Uber 5 pts

DavidKnox Dave you're SO, OG! Keep rockin dude.

MattOesterle 5 pts

Really enjoying the "MATH" arrow. Great post.

Uber 5 pts

MattOesterle Thanks for the Uber Love Matt.

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