When floods hit the road: Resilience to flood-related traffic disruption in the San Francisco Bay Area and beyond

Abstract

As sea level rises, urban traffic networks in low-lying coastal areas face increasing risks of flood disruptions. Closure of flooded roads causes employee absences and delays, creating cascading impacts to communities. We integrate a traffic model with flood maps that represent potential combinations of storm surges, tides, seasonal cycles, interannual anomalies driven by large-scale climate variability such as the El Niño Southern Oscillation, and sea level rise. When identifying inundated roads, we propose corrections for potential biases arising from model integration. Our results for the San Francisco Bay Area show that employee absences are limited to the homes and workplaces within the areas of inundation, while delays propagate far inland. Communities with limited availability of alternate roads experience long delays irrespective of their proximity to the areas of inundation. We show that metric reach, a measure of road network density, is a better proxy for delays than flood exposure.

Integrating urban traffic models with coastal flood maps to quantify the resilience of traffic systems to episodic coastal flooding

“…The first input, the origin-destination commute demand data, indicates the number of commuters who perform trips from a given origin to a given destination. Prior traffic studies based in the U.S. have used the Census Bureau's Longitudinal Employer Household Dynamics Origin Destination Employment Statistics (LODES) dataset [1] regarding locations of homes and workplaces at the census block level [2] , [3] , [4] . Since the LODES dataset does not incorporate information about the time or mode of commute, we augment the data with the American Community Survey [5] that provides estimates for the distribution of commuters by time and mode of commute.…”

“…Simplifications include aggregating local road segments into artificial roads, replacing curved road segments with straight segments, and removing data associated with vertical position, such as the elevation of bridges, highway overpasses, and tunnels. While the simplifications in road geometry and elevation cause minimal errors in stand-alone traffic simulations, they may introduce biases when estimating flood impacts [3] .…”

“…The study by Kasmalkar et al. [3] identified two sources of bias in the spatial overlay approach that may lead to an incorrect identification of flooded roads. First, the simplification of curved road segments into straight road segments may cause road segments to incorrectly intersect with areas of flooding.…”

The Impact of Flooding on Urban Transit and Accessibility: A Case Study of Kinshasa

“…In the last few decades, studies have shown an increase in the likelihood of extreme events, such as intense rainfall due to climate change (IPCC, 2014;Kasmalkar et al, 2020). The complex hydrological processes at the Congo Basin are sensitive to these climatic changes, resulting in an increase in the frequency of pluvial and fluvial flooding, which could lead to serious disruptions in socio-economic networks (Tshimanga et al, 2016).…”

“…Both cases could lead to severe localized traffic congestion that later propagates to regional levels (Mao et al, 2012;Rentschler et al, 2019). Kasmalkar et al, (2020) studied the impact of coastal flooding with respect to traffic disruption in the San Francisco Bay Area and showed that flooding would render road segments impassable and lead to significant delays in commuters' travel times. In some areas, the delay was more than double the average travel time within the region of study.…”