Simple pre-post analysis overestimates the impacts of new public transit services on ridership: Evidence from a quasi-experimental study of new bus rapid transit in Columbus, Ohio, USA

Abstract

This paper empirically demonstrates the value of quasi-experimental study designs to evaluate the direct impacts of new public transit services on ridership within its corridor. Using a new bus rapid transit (BRT) service, CMAX, in Columbus, Ohio, USA, as an example, we compare its impact on ridership based on a pre-post and quasi-experimental analysis framework. We conduct the pre-post analysis using a ridership space-time cube exploring a massive Automatic Passenger Counter (APC) database. Differences in total passenger counts before and after the BRT intervention indicate a 36% increase in ridership within its corridor. However, this patronage increase may not be attributable solely to the new public transit service. Potential confounding effects include systemwide ridership trends and a new unlimited transit pass program for downtown workers. To address these issues, we adopt a quasi-experimental study design with a difference-in-differences (DiD) identification strategy. We use propensity score matching (PSM) to match a counterfactual control group with the treatment group when implementing DiD model. After accounting for confounding effects, we find a less than 5% increase but not statistically significant impacts of CMAX on ridership. Our results support the argument that a simple pre-post analysis ignoring confounding effects can lead to a misleading evaluation of a new public transit service’s direct impact on ridership.

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