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However, there are other mechanisms by which ride-sharing might increase traffic congestion. The most obvious contributor is deadheading or the out-of-service period a vehicle has to spend with no passenger. Studies estimate that deadheading is responsible for 50% of TNC traveled miles in New York and 20% in San Francisco. It is also reasonable to believe that many people who would have used public transportation, walked, traveled by bike, or would have made no movement at all are now contributing to traffic congestion by using TNCs because it is so convenient. Finally, TNCs contribute to congestion during the frequent pickups and drop-offs that they have to make — for instance, this behavior causes similar effects to those seen in areas that traditionally rely heavily on taxis.

Researchers at the University of Kentucky and the San Francisco County Transportation Authority wanted to address this debate by comparing traffic congestion in San Francisco with and without the presence of ride-sharing apps in the city. To this aim, they scraped data from the application programming interfaces of Uber and Lyft, along with observational travel times. This allowed the authors to gauge the effect of TNCs on San Francisco’s traffic between 2010 conditions when TNC activity is negligible and 2016 conditions when it is not.

Daily TNC pickups and drop-offs for an average Wednesday in fall 2016 ( Darker colors represent a higher density of TNC activity). Credit: Science Advances.

Daily TNC pickups and drop-offs for an average Wednesday in fall 2016 (
Darker colors represent a higher density of TNC activity). Credit: Science Advances.

In order to exclude other non-TNC factors that contribute to traffic congestion, the authors turned to San Francisco’s travel demand model (SF-CHAMP), which produces estimates of traffic volumes on all roads in San Francisco and is sensitive to changes in population and demographics, employment, transportation networks, and congestion. The version of the model used in this study was calibrated for 2010 conditions, providing a counterfactual case where ride-sharing doesn’t exist.

The results suggest that Uber and Lyft are helping drive more traffic congestion rather than unclogging it. The researchers found that ride-sharing services made delays 62% worse, compared to a 22% increase in travel delays in the scenario where there are no TNCs. The average speed of on-road vehicles decreased by 13% due to TNCs but only 4% in the counterfactual model. Finally, commuters now have to use a longer buffer time to make sure they arrive at their destination on time because travel duration is less reliable overall. According to the findings, this buffer is now 15% higher compared to the natural 6% increase where Uber and Lyft don’t exist.

“The results show some substitution between TNCs and other car trips, but that most TNC trips are adding new cars to the road. The estimated models show that TNC vehicles stopping at the curb to pick up or drop off passengers have a notable disruptive effect on traffic flow, especially on major arterials,” the authors wrote in Science Advances

The authors say that their findings should be of interest to policy makers and transportation planners who are interested in regulating TNCs in the best interest of the general public of San Francisco. Some solutions include allocating curb spaces and right-of-ways for ride-sharing vehicles and integrating new mobility services.