This study empirically investigates the causes of urban traffic congestion to bring into focus the variety of beliefs that provide support for policy interventions to mitigate traffic congestion in USt cities. We use a structural equation modeling (SEM) framework and position the analysis at the meso-scale (i.e., neighborhood shapes, sizes, density, land-use mix, street design, distribution of open space) to better align with policy and planning decisions and strategies. The analysis is carried out on 100 metropolitan areas in the USA, with three complementary metrics of urban traffic congestion and 25 factors representing the structural, socioeconomic, and behavioral aspects of urban areas. SEM results demonstrate that congestion is a complicated phenomenon where indirect effects are pathways powerful enough to offset direct effects under certain circumstances. We find that, beyond the role of urban population size, income and employment agglomeration lead to further traffic congestion. In contrast, the most influential tempering effects come from congestion’s own self-regulation impact, non-car mode choice behaviors, adequate highway transportation, focused community structures, urban density, and socioeconomic factors like car ownership. The article discusses the policy implications of this meso-scale empirical analysis.
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