This thesis evaluates whether staggered start times for K–12 schools can mitigate morning peak congestion and improve transportation equity in Queens and Brooklyn, New York City. It develops a reproducible agent-based simulation workflow that links public data preprocessing, ACS- and PUMS-based synthetic population generation, proxy student-to-school assignment, baseline weekday schedule construction, scenario design, MATSim simulation, and post-simulation evaluation. Four scenarios are compared: a uniform 08:30 baseline, a two-wave staggered schedule, a three-wave demand-balanced schedule, and an equity-prioritized three-wave schedule. The results show that staggered school start times alone produce modest congestion e!ects under the study’s assumptions. The demand-balanced three-wave scenario performs best among the interventions for overall travel-time outcomes, while the equity-prioritized scenario provides limited distributional advantages for some school-need groups, but introduces a small e”ciency cost. The thesis contributes an auditable framework for evaluating school scheduling policies through both congestion and equity lenses.