In this section
@article{McCoy:2014:10.1080/01621459.2014.956871,
author = {McCoy, EJ and Graham, DJ and Stephens, DA},
doi = {10.1080/01621459.2014.956871},
journal = {Journal of the American Statistical Association},
title = {Quantifying causal effects of road network capacity expansions on traffic volume and density via a mixed model propensity score estimator},
url = {http://dx.doi.org/10.1080/01621459.2014.956871},
volume = {109},
year = {2014}
}
TY - JOUR
AB - Road network capacity expansions are frequently proposed as solutions to ur-ban traffic congestion but are controversial because it is thought that theycan directly ‘induce’ growth in traffic volumes. This paper quantifies causaleffects of road network capacity expansions on aggregate urban traffic volumeand 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-responserelationship between capacity and volume but suspect confounding from bothobserved and unobserved characteristics. Analytical results and simulationsshow that a longitudinal mixed model PS approach can be used to adjust ef-fectively for time-invariant unobserved confounding via random effects. Ourempirical results indicate that network capacity expansions can cause substan-tial increases in aggregate urban traffic volumes such that even major capacityincreases can actually lead to little or no reduction in network traffic densi-ties. This result has important implications for optimal urban transportationstrategies.
AU - McCoy,EJ
AU - Graham,DJ
AU - Stephens,DA
DO - 10.1080/01621459.2014.956871
PY - 2014///
SN - 1537-274X
TI - Quantifying causal effects of road network capacity expansions on traffic volume and density via a mixed model propensity score estimator
T2 - Journal of the American Statistical Association
UR - http://dx.doi.org/10.1080/01621459.2014.956871
UR - https://www.tandfonline.com/doi/full/10.1080/01621459.2014.956871
UR - http://hdl.handle.net/10044/1/18990
VL - 109
ER -
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