Uber, Lyft and Open Science
My co-authors and I are pleased that our article, “Do transportation network companies decrease or increase congestion?” was published today in Science Advances. In this study, we examine the growth in traffic congestion in San Francisco between 2010 and 2016 and find that TNCs, such as Uber and Lyft, are the biggest contributor to that growing congestion. This happens because many TNC users switch from walk, bike or transit, because deadheading adds traffic, and because curbside pick-ups and drop-offs disrupt traffic flow. These effects more than offset the number of users who instead switch from a private car and the limited number of users that share a TNC with another passenger. Overall, we find that about 2/3 of TNCs are new vehicle trips that otherwise would not have been on the road, and that those vehicle trips are concentrated in the most congested parts of the city at the most congested times of day.
Representatives of Uber and Lyft have criticized our study. We welcome the feedback, and would like to take the opportunity to respond.
Claim: “The study ignores the growth of tourism and urban freight deliveries since 2010, both key drivers of urban congestion.”
In fact, we address both topics in our paper:
Tourism: “SF-CHAMP does include a visitor model, with visitor travel representing 4.5% of intra-San Francisco person trips in 2010. The visitor model is influenced primarily by the number of hotel rooms in the city, which have not increased significantly over this period. Data from the San Francisco Council on Economic Development show that the number of visitors to San Francisco grew by 22% between 2010 and 2016, with some of those visitors staying in lodging options such as Airbnb, which are not reflected in our visitor model. When this growth is applied to the base of 2010 visitor trips, it might generate up to 1% more intra-San Francisco person trips, beyond what is already included in the background growth. However, these visitor trips only add to congestion if they are in a vehicle, with transit, walk, and TNC being the most commonly used modes among visitors. 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. For comparison, TNCs are 15% of intra-San Francisco vehicle trips in 2016.”
Urban freight deliveries: “Our analysis reflects growth in truck travel associated with growing employment, but it does not account for structural changes such as a large shift from in-person to online shopping. Such a shift could increase delivery truck volumes but decrease personal shopping trips (50). The net effect of this trade-off is not clear and depends on factors such as how efficiently the delivery vehicle can chain multiple deliveries together, what TOD the different trips would occur, and whether the deliveries are to commercial locations in the downtown area or to less congested residential areas. Unfortunately, we lack the commercial vehicle data necessary to evaluate that effect.”
Claim: “Other peer reviewed research released recently argues that, overall, TNC adoption is actually helping grow transit ridership.”
Our study looks at the effects of worsening congestion in San Francisco, not changes in transit ridership. We will be reporting on the effects of TNCs on transit ridership in San Francisco later this year, but it is important to note that these are separate questions, and even if transit ridership increases, TNCs still may cause more congestion.
The study cited showing TNCs as complementary to public transportation is based on data that stops in 2015. However, the steepest declines in transit ridership have occurred between 2015 and 2018, corresponding to a period of high TNC growth. A study that considers more recent data found that Uber’s entry into a market is correlated with declining transit ridership.
In addition, our Science Advances paper notes that “Whether a trip made by TNC adds traffic to the road also depends on which mode would have been used for the trip if TNC was not avail- able. Between 43 and 61% of TNC trips substitute for transit, walk, or bike travel or would not have been made at all (10, 11, 14, 15).”
Claim: Our model has not been shown to match with observed data, or has been insufficiently validated.
Our analysis is based on two models: 1) a travel demand model, SF-CHAMP, that estimates the change in background traffic, and 2) a statistical model that in which the change in observed travel times are modeled as a function of the change in background volume and TNC volume.
SF-CHAMP has been in use in San Francisco for about 15 years, and tested extensively. It uses a best-practice approach for travel demand models, and one that serves as the basis for major transportation decisions throughout the US and the world.
Our statistical model (Table 2) shows that the coefficient on the background traffic volume is close to 1. This means that in places and times where there is not much TNC traffic, SF-CHAMP does a very good job of replicating the observed travel time changes. However, in places where there is a lot of TNC traffic, SF-CHAMP alone under-estimates the increase in travel times.
The statistical model is applied to generate the overall congestion statistics for the 2010, 2016 no TNC and 2016 with TNC scenarios reported in Table 3. Table 3 also shows that the model matches the observed data quite well for the two scenarios that we have observations. As a separate test, we applied this model to 2012 conditions where TNC use was still negligible, but where there was still substantial population and employment growth. In this test, the model predicted only a small increase in congestion between 2010 and 2012, and then a larger increase between 2012 and 2016. This larger increase in congestion aligns with the growth of TNCs, as well as the timing of observed speed changes, as found from the congestion management program, and reported below.
The Value of Open Science
We are strongly committed to the value of open science, meaning that we want to promote external review and transparency. This is why we found it important to go through the peer review process at Science Advances, and this is why we are making our data available for download along with our article. We encourage others to re-analyze our data, or to add to it as new data become available, and we will be happy to work with those who wish to do so. At the same time, we challenge our critics at Uber and Lyft to subject their own claims to external review and to provide the data necessary to support their claims.
Greg Erhardt, Assistant Professor of Civil Engineering, University of Kentucky
(This article may be updated in response to additional claims.)
