Evaluating the Impacts and Effectiveness of Traffic Calming Measures with Roadway Network Models

A multi-pronged investigation into the impacts and effectiveness of traffic calming measures (TCMs) at the road network level was evaluated, combining a systematic literature review, advanced microsimulation modeling, and innovative calibration techniques using connected vehicle data. While TCMs such as speed humps, raised crosswalks, and road diets are widely adopted to reduce vehicle speeds and improve road safety, existing studies have focused mainly on localized impacts and are limited by sparse or outdated data, incomplete methodologies, and inconsistent modeling practices. To better understand current practice and limitations, peer-reviewed articles were synthesized through a systematic review in Chapter 2, identifying key empirical models and highlighting deficiencies in data standardization, methodological robustness, and model transferability. The results from the review underscore the need for consistent, predictive approaches to estimate the trade-offs between reduced speed, traffic volume redistribution, noise, and emissions. To combat methodological robustness and model transferability, the current study
uses microsimulation models to study the network-wide impact of TCMs using aggregated and disaggregated traffic data. A novel trip generation method is proposed in Chapter 3 to calibrate traffic models without relying on expensive origin-destination (O-D) pairs, achieving realistic simulations using sparse traffic count and speed data.
Results demonstrate significant traffic diversion effects and speed reductions, emphasizing the broader systemic consequences of localized interventions. The current study extends this analysis in Chapter 4 by incorporating disaggregated INRIX connected vehicle trajectory data, scaled against pneumatic tubes ground truth. The model achieves high calibration accuracy using Simultaneous Perturbation Stochastic Approximation (SPSA) and successfully replicates post-TCM implementation behavior, including speed distribution narrowing and volume shifts. The hybrid framework developed in the current study provides a scalable, cost-effective, and transferable methodology for evaluating TCMs in data-limited urban settings.Collectively, this work advances the state of knowledge in transportation engineering by offering a comprehensive, scalable framework for evaluating the network-wide impacts of TCMs. The methods developed herein inform better decision-making for urban planners, policymakers, and engineers by addressing data scarcity, enhancing model validity, and emphasizing the interconnected nature of urban mobility systems.