Integrating Microscopic Traffic Simulation and Machine Learning for Evaluating Road Diet Impacts on Traffic Performance and Intersection Safety

Road diets represent a cost-effective strategy for enhancing roadway safety; however, comprehensive frameworks integrating operational assessment with surrogate safety evaluation remain limited. This study develops an end-to-end microscopic simulation and machine learning framework to quantify operational and safety impacts of an urban road diet. A 0.22 mi segment of Frazier Avenue in Chattanooga, Tennessee, U.S.—recently converted from a four-lane undivided cross-section to a two-lane facility with a center left-turn lane—is modeled in SUMO using multi-month GridSmart trajectory counts, speed measurements, annual average daily traffic, and OpenStreetMap-derived geometry. A one-way analysis-of-variance-based sensitivity analysis identifies nine influential driver and vehicle parameters, which are calibrated via a genetic algorithm that minimizes speed root mean square error; validation against turning-movement counts using the Geoffrey E. Havers statistic satisfies FHWA acceptance criteria. Three 24 h scenarios are simulated (pre-diet, post-diet, and post-diet geometry with pre-diet demand), complemented by a demand sensitivity analysis up to +30% average daily trips. Corridor-wide, the road diet yields modest mobility penalties—average travel time increases by 4.3%, average speed decreases by 5.2%, and average waiting time increases by 40.7%—with more pronounced degradation westbound and intersection-specific queue growth. Surrogate safety measures at the major signalized intersection indicate substantial safety gains: total vehicle conflicts with time-to-collision (TTC) <3 s decrease by 45.7%, and critical conflicts with TTC <1 s decrease by 62.3%, with conflict hotspots markedly attenuated. Finally, an XGBoost regressor using pre-diet queue length, density, travel time, and speed predicts post-diet waiting times with R² = 0.905, demonstrating the potential of simulation-driven predictive models to support proactive corridor management and design of future road diet projects.