A ‘Shared Ride’ May Be A Public Burden
Ride-sharing companies sometimes tout their services as a “greener” option, reducing the need for individually-owned personal cars. However, the introduction of “transportation network companies” like Uber and Lyft hasn’t led to an actual decrease in the number of cars on the road—and, in cases where people might have once chosen to walk, bike, or take mass transit, hailing a car instead actually increases car use.
A new study published this week in the journal Science Advances found that in traffic simulations, cars from services like Uber and Lyft are the biggest contributor to growing traffic congestion in San Francisco. Greg Erhardt, a civil engineer at the University of Kentucky and one of the authors of the new study, says that the bulk of the ridership in these companies occurs in urban centers where other types of travel are viable options—and that two-thirds of the rides documented in the study appeared to be “new rides,” that wouldn’t have involved a car had the ride-hailing apps not been in operation. He joins Ira to talk about the pros and cons of network transportation companies and what could be done to make transportation greener.
Greg Erhardt is an assistant professor in the Department of Civil Engineering at the University of Kentucky in Lexington, Kentucky.
IRA FLATOW: And now it’s time to play Good Thing, Bad Thing.
Because every story has a flip side. And this week, Uber became a publicly traded company in a tumultuous day of trading. Their IPO comes the same week as a strike by their drivers. Ride sharing companies like Uber sometimes tout their services as a greener option, reducing the need for individually owned, personal cars. But a study out this week questions the results of those good intentions.
Joining me now is Greg Erhardt. He’s an Assistant Professor in the Department of Civil Engineering, University of Kentucky in Lexington, and one of the authors of a study in the journal Science Advances looking at the effects those ride hailing app companies have had on the city of San Francisco. Welcome to Science Friday.
GREG ERHARDT: Thank you. It’s a pleasure to be here, Ira.
IRA FLATOW: Oh, you’re welcome. So those ride hailing app companies say, it will reduce the number of cars. Is that accurate from your study?
GREG ERHARDT: No, it’s not. What we find, in fact, is that most users– about 2/3 of the vehicles on the trip– are actually new cars on the road that otherwise would not be here, and that includes people who are switching from transit, switching from walking or biking, as well as the drivers driving around deadheading, as we call it– or looking for passengers with an empty vehicle.
IRA FLATOW: And also the number of rides. Not just the number of cars, but the number of rides went up. Why was that?
GREG ERHARDT: The number of rides went up because what happens is people are– it’s a more convenient way to get there, but the total delay and the total traffic congestion caused by this goes up as well. Between 2010 and 2016, we found the traffic congestion in San Francisco measured by vehicle hours of delay– or sort of how much extra time it spends in order to get to my destination– went up by about 60%, and Uber and Lyft responsible for about 2/3 of that increase.
IRA FLATOW: So do we have these drivers just hanging out on the roads waiting to be hailed, and that congests the roads also?
GREG ERHARDT: Yes. That’s correct. They spend somewhere between 20% and 30% of their miles driven driving around with no passenger looking for a ride.
IRA FLATOW: Hmm. How did you get the data for this study?
GREG ERHARDT: So that was a challenge. We live in an era where everyone wants to talk about big data, but the reality is most big data is controlled by a particular company. Uber and Lyft have plenty of it themselves, but they have no interest in sharing with researchers like us or with the transportation authority in San Francisco.
So what we did is we teamed with some partners who wrote sort of a computer program that simulates what your smartphone does in talking to their servers, and it tells you where each of the nearest closest drivers are. We had them collect that information every second for a period of six weeks in a grid across San Francisco, and you can get a trace of where the driver is driving around. And then when it disappears from the map, shows up a few minutes later somewhere else, we can infer that there’s a trip in between.
IRA FLATOW: So you were able, basically, to map all those little cars we see on the app–
GREG ERHARDT: Exactly.
IRA FLATOW: –where they were driving.
GREG ERHARDT: That’s exactly right. You open up your app, you see the cars, and that’s actual, real data showing where real drivers are. And so if you collect enough of it and put that together, we can paint a picture of where all those vehicles are in the city. And what we find is that they’re actually concentrated in the most congested part of the city. Very concentrated in downtown and concentrated in the most congested times of day during the AM rush hour and the afternoon rush hour.
IRA FLATOW: And so how much of an impact did the presence of these companies have on that congestion? Traffic is congested anyhow. Could you tweeze out how much Uber and Lyft account for the congestion?
GREG ERHARDT: Exactly. So it’s about 2/3 of the increase between 2010 and 2016 is our metric. And that increases about 60% in vehicle hours of delay, so it’s quite a bit. And the reason for that is because traffic is sort of non-linear, if you will. Adding a few cars to the road in the middle of a night when no one else is around makes very little difference in travel time, but adding a small number of cars to the road in the middle of rush hour makes a very big difference, and it’s very concentrated in places that are already congested.
IRA FLATOW: Fascinating. Thank you. Thank you for that work.
GREG ERHARDT: Thank you. Greg Erhardt. He is Assistant Professor in the Department of Civil Engineering at the University of Kentucky.