This study explores the impact of bicycle-sharing infrastructure on urban transportation. Accounting for selection bias in a matching framework, we estimate a causal effect of the Capital Bikeshare on traffic congestion in the metropolitan Washington, DC, area. We exploit a unique traffic dataset that is finely defined on a spatial and temporal scale. Our approach examines within-city commuting decisions as opposed to traffic patterns on major thruways. Empirical results suggest that the availability of a bikeshare reduces traffic congestion upwards of 4% within a neighborhood. In addition, we estimate heterogeneous treatment effects using panel quantile regression. Results indicate that the congestion-reducing impact of bikeshares is concentrated in highly congested areas.
Key findings
- A causal effect exists between the introduction of bike-sharing programs and traffic congestion. Findings show a reduction in DC traffic congestion upwards of four percent that can be attributed to the presence of a Capital Bikeshare station.
- A secondary finding is that congestion reductions are concentrated in areas of high congestion.
- The interactions among bicycle infrastructure and other modes of transit are only going to become more relevant as Washington, DC, and other cities expand their bike-sharing programs.
