Do transportation network companies increase or decrease transit ridership? Empirical evidence from San Francisco 

Abstract
Transportation Network Companies (TNCs), such as Uber and Lyft, have been hypothesized to both complement and compete with public transit. Existing research on the topic has produced mixed results and been limited by a lack of detailed data on the timing and location of TNC trips (1–4).

This study overcomes that limitation by using data scraped from the Application Programming Interfaces (APIs) of two TNCs (5), combined with Automated Passenger Count (APC) data on transit use and other supporting data. Specifically, we compared San Francisco Muni bus and light rail ridership in 2010 before the introduction of TNCs to ridership in 2015 when TNCs were widespread. We estimated panel data regression models of transit ridership as a function of TNCs, controlling for household and employment growth, transit service changes and other relevant factors. To validate the panel model findings, we also estimated time-series models of bus ridership over the same period. We applied the estimated coefficients to the observed change in each variable to calculate its net contribution transit ridership change between 2010 and 2015.

The panel model estimation results indicate that TNC use has a negative and significant effect on bus ridership, and a positive but insignificant effect on light rail ridership. The time series model estimation results show that the number of years since TNCs entered the market is negatively and significantly correlated with bus ridership. Table 1 summarizes these model application results for common variable categories. The models show that TNCs result in net bus ridership decreases of 8.6% to 10.8%. Both models show that transit service increases result in about 7% more bus ridership. The effect of households and employment in the time-series model is greater than the effect of accessibility in the panel model. The remaining factors have a modest effect on bus ridership in either direction. Of the 13.1% rail ridership increase, we estimate that 2.1% is attributable to higher accessibility, 0.3% to increased rail service, and 2.0% from more regional transit transfers. The model suggests that 3.8% of the ridership increase is attributable to TNCs, although this estimate is statistically insignificant. There is something driving the rail ridership increase beyond what we can measure with this model: 4.2% of the growth remains unexplained.

Given this evidence, we conclude that in 2015, TNCs reduced bus ridership in San Francisco by about 10%. We do not find a statistically significant relationship between TNCs and Muni light rail ridership. While these results are specific to San Francisco, cities throughout the US have experienced bus ridership declines of 12% to 18% since TNCs have emerged (6), raising questions about how widespread the effect may be. These results provide better understanding of the competitive relationship between TNCs and transit that is important as cities aim to provide a transportation system that is sustainable and equitable.