We all know the nightmare of city traffic: sitting at a standstill, huffing exhaust fumes, possibly stressing over how delayed we will be. Urban congestion limits mobility, quality of life, environmental health, and even economic growth. A new study, “Categorizing Congestion: A Framework for Congestion Analysis across Urban Area Counties,” addresses this frustrating and complex issue, by proposing a system for measuring and predicting congestion. Focused on the limitations of existing congestion metrics, authors Meredith Raymer, Hani Mahmassani, and Jennifer Duthie have combined data sources that record different phenomena associated with congestion, such as low speed and high delay, to develop a more accurate measurement and prediction tool.
Grouping counties into different congestion categories based on patterns, the team’s framework synthesizes long-term and short-term urban congestion dynamics. The results serve as an early-warning system, allowing planners to identify traffic areas with a higher likelihood of future congestion. It also suggests how investing in active mobility infrastructure can mitigate congestion growth, one example being bike lanes, especially where there has been little bike lane density. Transportation engineers and planning professionals can gain from this updated approach to congestion analysis, enabling more targeted and effective interventions. Learn more about this research in the Journal of Transportation Engineering, Part A: Systems at https://ascelibrary.org/doi/10.1061/JTEPBS.TEENG-9370. The abstract is below.
Abstract
The study addresses two major gaps in the current understanding of congestion – lack of a nationally comparable metric that captures within-metro heterogeneity and lack of a unified predictive model that combines established congestion determinants with explicit urbanization and suburbanization dynamics. The congestion propensity index (CPI) overcomes these limitations as a predictive measure that allows for a ranked comparison of expected congestion development in cities across the United States. By transforming widely available data into more robust measures at the county level, heterogeneity within urbanized areas can be considered jointly with national trends. The model incorporates established relationships between variables such as population density, active modes of transportation, and spatial distribution of opportunities on congestion as well as unique measures of suburbanization and urbanization dynamics. These relationships can aid in policy decision-making by recognizing that policies should vary both across and within cities.
Find out more about the congestion propensity index in the ASCE Library: https://ascelibrary.org/doi/10.1061/JTEPBS.TEENG-9370.
