progress shields us from the terrors of an uncertain future
SAN FRANCISCO — Uber’s decision to
bring self-driving taxis to the streets of Pittsburgh this week is
raising alarms among a swath of safety experts who say that the
technology is not nearly ready for prime time.
The unprecedented
experiment will launch even though Pennsylvania has yet to pass basic
laws that permit the testing of self-driving cars or rules that would
govern what would happen in a crash. Uber is also not required to pass
along any data from its vehicles to regulators.
Meanwhile,
researchers note, autonomous cars have been thrown off by bridges, a
particular problem in Pittsburgh, which has more bridges than any other
major U.S. city.
“They are essentially making the commuters the
guinea pigs,” said Joan Claybrook, a consumer-protection advocate and
former head of the National Highway Traffic Safety Administration. “Of
course there are going to be crashes. You can do the exact same tests
without having average citizens in your car.”
The small London side street reverberates with the growl of motorbikes and the shriek of horns. A young man yells hoarsely into a megaphone: “They’re gonna be disrupted, yeah! Because your lives are being disrupted, yeah! This is the money you need to live!” Dozens of men in T-shirts and jeans cheer and pump the horns on their motorbikes, scooters and bicycles. Many have pieces of A4 paper taped to their backs that say: “We are people, not Uber’s tools!”
[…]
Drivers for Uber’s ride-hailing app, of which there are about a million around the world, are subject to similar algorithmic control. They choose when to work but once they log on to the app, they only have 10-20 seconds to respond to “trip requests” routed to them by the algorithm. They are not told the customer’s final destination until they have picked them up. If drivers miss three trip requests in a row, they are logged out automatically for two minutes. Uber sends drivers a weekly report including their confirmation rate and average customer rating (out of 5).
new yorker personal anecdote time, feel free to ignore: as an astoria, queens resident who works in manhattan and flies every so often, my transit breakdown tends to be:
subway - home->work->home
yellow cab - out late in manhattan -> home
yellow cab - airport -> home
green cab - astoria/lic -> home
uber - home -> airport
uber - out late in brooklyn -> home
uber really replaced calling livery cab services highly rated on yelp for me; curious what others do in nyc or other cities?
It has been a big summer for transportation open data. First, the TLC released a dataset with every taxi and green-cab trip taken since 2014 (a release which I had advocated for in my talk on TED). This is a great step forward and will likely save the TLC much time in filling requests for their data. I’d love to see other government agencies follow suit.
And another exciting development happened on the Open Data front, and it was not something the government did this time- it was FiveThirtyEight. You see, the folks there ran a couple of pieces on Uber and Taxis based on some data they FOILed from the government. Usually, this is where newsrooms move on to the next project. But, instead, the Upshot posted the raw FOILed data on github for us all to analyze. That is big news and I’d love to see more data journalists follow suit for two main reasons:
Releasing your data along with your work amplifies your messaging. It allows others to follow up on what you do, and add on to it
More importantly, reproducibility is an important part of the scientific method. If “data science” is truly a science, it should be meeting basic tenets of the scientific method. Releasing data allows others to verify and follow up on your work.
What happens when data journalists don’t release their data? You get silly back and forths like the one between the Wall Street Journal and Bratton over the Times Square Pedestrian Plaza. We, as citizens, have to sit on the sidelines as neither has backed up their positions with any data releases. If the WSJ posted the data they used, we could cut through the rhetoric and see what is really going on.
With all that being said, I made a few quick observations which I’ll put on the blog this week. In the battle between Uber and Lyft, Uber is obviously dominant with about 7 times more rides being given. But, it seems that Lyft has made some serious inroads on the late night scene:
Surprisingly, at 2AM the ratio is nearly evenly split between the two.
In fact, a quick look at the hours that each service provides the most rides shows incredibly different shapes- Uber maxing out at the evening rush and Lyft maxing out at around Midnight.
The drastically different shapes point to a very different clientele - I always assumed that Lyft’s pink mustaches might appeal more to a younger crowd, but the data backs that up. So if you are wondering what the difference is between driving for Uber and Lyft, its night and day.
This small insight was made possible by FiveThirtyEight’s release of FOILed data, and I can only imagine how many more data insights will flow if other journalists follow their lead. In New York City, FiveThirtyEight, The Upshot and WNYC seem to be setting the data-release trend. Will your favorite news agency be next?
There’s a useful high-tech concept called the
Technology Readiness Level that helps explain why Uber pounced when it
did. NASA came up with this scale to gauge the maturity of a given field
of applied science. At Level 1, an area of scientific inquiry is so new
that nobody understands its basic principles. At Level 9, the related
technology is so mature it’s ready to be used in commercial products.
‘‘Basically, 1 is like Newton figuring out the laws of gravity, and 9 is
you’ve been launching rockets into space, constantly and reliably,’’
says Jeff Legault, the director of strategic business development at the
National Robotics Engineering Center.
kreuzaderny