An Empirical Bayes Approach to Quantifying the Impact of Transportation Network Companies (TNCs) on VMT
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2020-01-01
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Edition:Final Report
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Abstract:Assessing the impacts of new and disruptive technologies on automobile usage and the modal split is emerging as a key issue for transportation planners and policymakers. This study offers a new approach to quantifying the impact of transportation network companies (TNCs) such as Uber and Lyft on vehicle‐miles traveled (VMT). The approach is based on a simple idea from counterfactual theory, which is to compare 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 therefore more difficult to estimate. The study develops and demonstrates the Empirical Bayes (EB) measurement model for obtaining the counterfactual VMT estimates. The EB method is widely used and accepted for traffic safety assessment. The approach proposed for estimating VMT changes is analogous to the quasi‐experimental EB procedure for estimating crash reduction if a particular traffic safety treatment is applied to a roadway location. In this study, we reinterpret the traffic safety treatment as being akin to the introduction of TNCs and the estimation of crash reduction as analogous to the resulting change in VMT. This study develops an EB measurement model for the VMT in Atlanta and San Louis Obispo regions as a proof‐of‐concept. Counterfactual VMT estimate is obtained by combining two VMT estimates from 1) the cross‐sectional models that estimated using data from comparative peer regions to Atlanta and San Luis Obispo regions and 2) the time-series models based on longitudinal data from Atlanta and San Luis Obispo regions. We measure the difference between the counterfactual VMT estimate and the current VMT estimate as an indicator of TNC impact. We find that the VMT estimate in a counterfactual scenario without TNCs is lower than the current VMT estimate over the period between 2012 and 2017. Also, the counterfactual VMT estimate shows a lower average annual growth rate compared to the current VMT estimate over the same period. The findings provide insight into the need to better integrate TNCs into the existing transportation system so that they don’t increase VMT. We expect the approach to be useful in other research such as estimating effects of connected and automated vehicles (CAV) introduction on VMT in the future.
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