Another study says Lyft and Uber are ‘biggest contributors’ to SF traffic congestion

On Wednesday, the peer-reviewed journal Science Advances published research by the University of Kentucky’s college of engineering and others concluding that ride-hailing companies like Lyft and Uber—”TNCs,” (Transportation Network Companies) as academic and City Hall deem them—are behind new traffic woes in San Francisco.

Gregory Erhardt, an assistant professor of civil engineering at the University of Kentucky, who was one of the principles on the project, tells Curbed SF that much of the data was presented to the city in late 2018 and included in a San Francisco County Transportation Authority study.

Erhardt said that “public-facing report” was based on the paper published this week. However, the study in Science Advances has been subject to peer review, and the two reports draw from some different data sets, making the topic worth revisiting.

Researchers relied largely on “an activity-based travel demand microsimulation model” to predict what driving conditions in San Francisco probably would have looked like between 2010 and 2016 without companies like Lyft and Uber, then compared those results to what actually happened.

“The model predicts the typical weekday travel patterns for approximately 7.5 million San Francisco Bay Area residents, including choices of vehicle availability, activity participation, destinations, travel modes, and travel times,” according to Erhardt et al.

The resulting assessment holds, in part:

• The model uses Lyft and Uber data to expedite its conclusions: “Existing research has produced conflicting results and has been hampered by a lack of data. Using data scraped from the application programming interfaces of two TNCs, combined with observed travel time data, we find that contrary to their vision, TNCs are the biggest contributor to growing traffic congestion in San Francisco.”
• Over six years, congestion in San Francisco rose 40 percent compared to what it might have been without Lyft and Uber: “Between 2010 and 2016, weekday vehicle hours of delay increased by 62 percent compared to 22 percent in a counterfactual 2016 scenario without TNCs. [...] Average speed decreases from 25.6 mph in 2010 to 22.2 mph in 2016, and the vehicle hours of delay increase by 63 percent over the same period.”
• It is possible for ride-hailing to reduce traffic the way companies say it’s designed to do: “If TNCs are shared concurrently, a service known as ride-splitting, they could reduce traffic [...] and there is some evidence to suggest that a small portion of travelers may use TNCs in this way. Some have speculated that by providing a convenient alternative to owning a car, TNCs could incentivize people to own fewer cars.” However, researchers contend that few trips happen in these critical ways.
• The paper cautions that there are limits to what this kind of modeling can account for: “While the predicted background traffic changes account for several important control variables, there remains a risk that our results are confounded by another factor.” For example, the simulation added in the amount of extra freight traffic that results from the city’s growing population and employment, but doesn’t consider whether or not residents became more likely to shop online and thus drive up delivery traffic.
• The research doesn’t take fully tourism into account, either: “The visitor model is influenced primarily by the number of hotel rooms in the city, which have not increased significantly over this period. [...] Thus, due to the growth in tourism, the total vehicle trips in 2016 may be a fraction of a percent higher than we estimate in the background traffic volumes.”

In an aside, Ernhardt and colleagues also add that “the current system is commonly viewed as a bridge technology that may be replaced by fleets of self-driving cars if and when that technology is ready.”

Both Lyft and Uber criticized the research in response.

In addition to noting that “the study is old,” Lyft spokesperson Lauren Alexander tells Curbed SF that the data used for the models is too incomplete to be conclusive and says that “Lyft is actively working with cities on solutions backed by years of economic and engineering research, such as comprehensive congestion pricing.”

A spokesperson for Uber struck the same note, arguing, “While studies disagree on causes for congestion, almost everyone agrees on the solution,” again calling for congestion pricing and promoting the use of public transit, though researchers say that TNCs are more likely to undermine mass transit use in San Francisco.