Climate change is intensifying coastal floods and increasing the risks of traffic disruption in low-lying, 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.

气候变化加剧了沿海洪灾，并增加了低洼沿海社区交通中断的风险。努力了解交通中断对社区的不同影响，导致了交通弹性概念，该概念反映了交通系统可以从中断中恢复的程度。现有的交通适应性代理集中在量化旅行时间延迟上，但是缺乏道路安全性的重要方面。在本研究中，我们根据2020-2040年期间旧金山湾区沿海洪水在5-10 am期间非高速公路车辆和行人事故发生率的变化来量化交通弹性。我们使用区域交通模型来模拟一系列沿海洪水水位下的交通模式。我们使用将交通量与历史事故发生率相关联的回归来估计洪水泛滥时的事故发生率。我们的结果表明，高速公路的洪水迫使通勤者穿过居民社区进入当地道路，从而导致事故率激增。与本研究中考虑的较高水位时延误急剧增加不同，我们预计在较低水位时全区域范围的高峰时段事故发生率可能会显着增加。