Ride-Sharing Companies Make Traffic Worse Instead of Better in San Francisco

Fig. 1 The p.m. peak period roadway level-of-service (LOS) in San Francisco (30). (A) 2009 conditions; (B) 2017 conditions. LOS grades roadways by vehicle delay, from LOS A representing free flow to LOS F representing bumper-to-bumper conditions. Data and an interactive mapping tool are available at congestion.sfcta.org.

Popular ride-sharing companies such as Uber and Lyft were the biggest contributors to increased traffic congestion in San Francisco from 2010 to 2016, according to a new study of traffic patterns in the city. The findings, which contradict claims that these services alleviate road congestion, could be of interest to transportation planners and policy makers grappling with how to regulate ride-sharing services. The rise of transportation network companies (TNCs) such as Uber has revolutionized the landscape of transportation in urban areas. TNCs have grown rapidly in recent years – ridership doubled between 2014 and 2016 in New York – and now represent a popular alternative to operating private vehicles.

Fig. 2 Daily TNC pickups and drop-offs for an average Wednesday in fall 2016 (1). Darker colors represent a higher density of TNC activity. Data and an interactive mapping tool are available at tncstoday.sfcta.org.

Although these services are often billed as solutions to traffic congestion, research into their effects on roadway conditions has yielded mixed or inconclusive results. To answer this question, Gregory Erhardt and colleagues gathered data on TNC volumes, pick-ups and drop-offs from the two largest TNCs and modeled their effects on travel times and roadway conditions in San Francisco from 2010 to 2016. Their analysis yielded a surprising finding: far from reducing traffic congestion, TNCs were the largest contributor to growing traffic in the city. Specifically, they found that weekday vehicle hours of delay – the difference in travel time in congested vs. free-flow conditions – increased by 62% and that average speeds decreased by 13% in the time period. In contrast, in a simulated model without TNCs, weekday vehicle hours of delay increased by only 22% and average speeds decreased by 4% in the city.

Interestingly, TNCs were also linked to less reliable travel times, which led travelers to buffer their estimated transit times if they wish to arrive at their destination on-time. Erhardt et al. say that future studies should examine the impacts of TNCs on traffic in other cities or less dense environments, as well as tease out the effects of uncontrolled factors such as tourism.

Fig. 3 Speed (mph) difference between 2016 scenario with TNCs and a counterfactual 2016 scenario without TNCs. Data represent four times of day: (A) 6 to 9 a.m.; (B) 9 a.m. to 3:30 p.m.; (C) 3:30 to 6:30 p.m.; and (D) 6:30 p.m. to 3:00 a.m.
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