Weekday travel times in the Golden Gate city increased a shocking 62 percent between 2010 and 2016, and a growing number of studies are finding that ride sharing services are chiefly to blame. The latest study ran simulated traffic models with and without ride shares to determine how much impact they make.
San Francisco is by no means an enormous city, with a population approaching 900,000 it's only around the 13th largest in America, but it's by far the most future-forward spot in the United States, and the birthplace of Uber, Lyft and any number of other ride-share companies looking to disrupt the status quo.
With some 45,000 ride-sharing cars on the road making 170,000 trips per day (2017 figures), it's also an excellent test bed to see the effect that such services have on traffic flow. Ride sharing advocates would point to the numbers of single-occupant cars on the road, and claim that ride sharing – and Uber Pool-style multi-passenger ride splitting – makes more efficient use of a vehicle.
On the other hand, you're also radically lowering the bar to get into the taxi business, and flooding the roads with tens of thousands of vehicles purchased mainly to be used as ride share services, who spend as much as 20 percent (in SF) to 50 percent (in New York City) rolling about waiting for a job with no passenger on board.
And according to a growing number of studies, including one recently published in Science Advances, that's having a huge effect on traffic. The study tracked traffic congestion in San Francisco, finding that weekday hours of delay have grown by 62 percent between 2010, when there was negligible ride-sharing traffic, and 2016, by which time ride share services had exploded to 12 times the popularity of taxi services and represented an enormous 15 percent of all intra-San Francisco trips.
The study uses the SF-CHAMP travel demand model, which is described as "a regional travel demand model that is used to assess the impacts of land use, socioeconomic, and transportation system changes on the performance of the local transportation system." The model is used in all kinds of applications, particularly where transit decisions need to be made, and it's a complex model that takes all modes of transport into account, as well as things like multi-modal transport, demographics and land usage.
A University of Kentucky Team took actual transport data and ran it through the system using two calibration settings – one to simulate the transport mix as it was back in 2010 with no ride sharing, and one to represent the way things are with Uber, Lyft and co playing its part. It also used data from the ride share companies themselves, showing the activity of their car fleets both during and between ride shares, as well as a bunch of archived speed data from a company called Inrix.
As part of the data analysis, the team looked at the effect of pickup/drop off areas where foot traffic meets road traffic, and lanes may be blocked for a short time as passengers jump in and out of their ride shares. It also looked at major roadworks projects around the city and attempted to remove their effects from the results.
The study found that higher levels of ride sharing raise a bunch of different congestion factors. Vehicle miles traveled rose by 13 percent with ride sharing as opposed to by 7 percent without. Vehicle hours traveled rose by 30 percent with ride sharing, but would only have risen by 12 percent without it. Vehicle hours delayed was up 62 percent with ride sharing, but would only have been up by 22 percent without it. And the average speed across a journey went down by 13 percent with ride sharing, but would only have dropped 4 percent without.
It goes without saying, ride sharing has been one of the most disruptive transport technologies of the last several decades, rising from obscurity to near ubiquity in a matter of a couple of years and consigning traditional taxi services to the dustbin with generally better pricing, better availability, better fleet management and what's often a better customer experience.
"There's a clear benefit for the person in the car," said Greg Erhardt, assistant professor of civil engineering at the University of Kentucky and lead author of the study. "They're getting a better experience, or they wouldn't do it. But there is a negative impact on everyone else: on the road system, other drivers and the people on the bus who also have to wait in traffic."
One proposal to help bring these suffocating congestion markers down is to simply slap a congestion pricing model on urban transport, charging people more for driving in heavy traffic areas or during peak periods. Indeed, says Erhardt, companies like Uber tend to be in favor of such measures, betting that the cars that congestion taxes take off the road can lead to more ride shares.
With most folk predicting a future where tens of thousands more autonomous robocabs begin to hit the streets offering even cheaper rides without any drivers at all, there's a good chance traffic might get a lot worse before it starts to get better. At least you'll have both hands free to play with your phone while you wait.
Source: University of Kentucky
