Modeling the impacts of physical vertical deflection traffic calming measures on speed, traffic volume, noise, emissions, and delay – A systematic review

Successful physical vertical deflection traffic calming projects have demonstrated positive impacts on speed, traffic volume, noise, and emissions. Most articles in the literature rely on observational data, limiting decision-makers’ ability to estimate effects before implementation. The current study uses a systematic review to synthesize findings from peer-reviewed articles published (2000–2024) using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework. Key speed metrics, such as spot speed at the TCM and the midpoint between TCMs, were predominantly modeled using linear and logistic regression. Shorter spacing between TCMs reduced speed variability, while larger spacing between TCMs diminished effectiveness. Road characteristics and posted speed limits (PSL) also influenced outcomes. Traffic volume analysis revealed traffic redistribution to alternative routes, particularly on higher-volume roads. Noise levels were impacted by TCM size and acceleration after TCM, with heavier vehicles contributing disproportionately more to noise levels. Emissions, especially nitrogen oxides (NOx) and carbon monoxide (CO), increased due to traffic volume, frequent braking, and acceleration around TCMs, underscoring trade-offs between speed, traffic volume, noise, emissions, and delay. Inconsistent methodologies in pre- and post-TCM data collection due to variations in timing, location, and traffic volume produced significant variations in the models used to evaluate TCM impacts. Linear regression models, commonly used for simplicity, often fail to capture complex, non-linear relationships between TCMs and transportation outcomes and can lead to unrealistic extrapolations and study-specific results, reducing transferability to other geographic areas and traffic conditions. The current study highlights the need for standardized and robust methodologies to improve the accuracy, comparability, and generalization of TCM evaluation models. Incorporating more advanced modeling techniques and consistent data collection practices will enhance predictive tools for decision-makers, enabling better estimation of TCM effects before implementation.