An empirical Bayes approach to quantifying the impact of transportation network companies (TNCs) operations on travel demand

Assessing the impacts of new and disruptive technologies on the transportation system is crucial for planners and policymakers. This study offers an innovative method for estimating the impact of transportation network company (TNC, e.g., Uber and Lyft) operations on travel demand. Among various measures of travel demand, vehicle‐miles traveled (VMT) is tested due to its broad applications in evaluating changes in transportation policy. The evaluation is based on counterfactual theory, which compares VMT estimates after the TNCs introduction to a region to what the VMT would have been without the TNCs. The latter of the two is a counterfactual, and this study develops and demonstrates the empirical Bayes (EB) approach for obtaining counterfactual VMT estimates. The EB approach is widely used to estimate crash frequency after a particular traffic safety treatment is applied to a roadway location. We reinterpret the traffic safety treatment as being akin to the introduction of TNCs in the Atlanta region and the estimation of crash frequency as analogous to the resulting change in VMT. Since the crash experience of a roadway site may be affected by several factors, like the VMT of a region, a robust counterfactual estimate is necessary for conducting a before-after study. A counterfactual VMT estimate is obtained by combining two VMT estimates from 1) the cross‐sectional analysis for Atlanta and its regional peers and 2) the time-series analysis based on the longitudinal trend from the Atlanta region. We measure the difference between the counterfactual VMT estimate and the reported VMT estimate as an indicator of TNC impact. We find that the VMT estimates in a counterfactual scenario without TNCs are lower than the actual VMT estimates between 2012 and 2018. We estimate that the TNCs may be accounting for extra 0.6 percent average annual growth in VMT. The findings may support future TNC-related planning and policymaking. We expect the approach to be useful in estimating the effects of other disruptions, such as connected and autonomous vehicles (CAVs) introduction and lasting impact of the pandemic, on VMT.