This thesis investigates how private vehicle traffic affects public transport performance using large-scale multimodal simulations in SUMO. Road network data from OpenStreetMap (OSM) and public transport schedules from GTFS are integrated to create realistic models for four medium-sized cities: Debrecen, Pécs, Szeged, and Brest. Traffic demand is generated through Origin–Destination (OD) matrices with zone-based modeling to replicate real commuting patterns. Public transport delays are measured using two methods (Trip Info and Stop Info) across 45 traffic levels and hundreds of simulations. The results show that all cities exhibit power-law delay growth and sharp congestion thresholds, revealing that network design quality—not city size—is the main factor determining resilience. The thesis provides quantitative insights that can support urban planning, traffic management, and public transport optimization