Quantifying Causal Effects of Road Network Capacity Expansions on Traffic Volume and Density via a Mixed Model Propensity Score Estimator

• 成果类型: Article
• 署名作者: Graham, Daniel J.; McCoy, Emma J.; Stephens, David A.
• 署名单位: Imperial College London; Imperial College London; McGill University
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1080/01621459.2014.956871
• 发表日期: 2014
• 页码: 1440-1449
• 关键词: MARGINAL STRUCTURAL MODELS; induced travel demand; highway capacity; inference; GROWTH

摘要

Road network capacity expansions are frequently proposed as solutions to urban traffic congestion but are controversial because it is thought that they can directly induce growth in traffic volumes. This article quantifies causal effects of road network capacity expansions on aggregate urban traffic volume and density in U.S. 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 effectively for time-invariant unobserved confounding via random effects (RE). Our empirical results indicate that network capacity expansions can cause substantial increases in aggregate urban traffic volumes such that even major capacity increases can actually lead to little or no reduction in network traffic densities. This result has important implications for optimal urban transportation strategies. Supplementary materials for this article are available online.