Climate change is intensifying coastal floods and increasing the risks of traffic disruption in lowlying, coastal communities. Efforts to understand the differential impacts of traffic disruption on communities have led to the concept of traffic resilience which captures the degree to which a traffic system can recover from disruption. Existing proxies of traffic resilience are focused on quantifying travel time delays but lack the important dimension of road safety. In this study, we quantify traffic resilience in terms of the change in non-highway car and pedestrian accident rates during the 5-10 am period as a result of coastal flooding in the San Francisco Bay Area for the 2020-2040 period. We use a regional traffic model to simulate traffic patterns under a range of coastal flood water levels. We use regressions that relate traffic volumes to historical accident rates to estimate accidents rates in the presence of flooding. Our results show that the flooding of highways forces commuters onto local roads passing through residential communities, causing a spike in accident rates. Unlike delays which increase sharply at the higher water levels considered in this study, we project that region-wide peak-hour accident rates may increase substantially at lower water levels.