Road network capacity expansions are frequently proposed as solutions to ur-
ban traffic congestion but are controversial because it is thought that they
can directly ‘induce’ growth in traffic volumes. This paper quantifies causal
effects of road network capacity expansions on aggregate urban traffic volume
and density in US cities using a mixed model propensity score (PS) estimator.
The motivation for this approach is that we seek to estimate a dose-response
relationship between capacity and volume but suspect confounding from both
observed and unobserved characteristics. Analytical results and simulations
show that a longitudinal mixed model PS approach can be used to adjust ef-
fectively for time-invariant unobserved confounding via random effects. Our
empirical results indicate that network capacity expansions can cause substan-
tial increases in aggregate urban traffic volumes such that even major capacity
increases can actually lead to little or no reduction in network traffic densi-
ties. This result has important implications for optimal urban transportation
strategies.