# **1 Introduction**

Transportation networks provide critical socio-economic services at local and regional scales. However, demographic and economic growth, rapid urbanization, increasing infrastructure interdependency, and natural hazards – due to the worsening effects of climate change – are putting physical transportation systems under increasing pressure (Chen & Wang, 2019; Schreider et al., 2000). This is particularly evident in urban areas where transport networks are especially vulnerable to weather-related hazards (Pregnolato, Ford, Glenis, et al., 2017; Singh et al., 2018; Yin et al., 2016). Improving their resilience to natural hazards and extreme weather events has become a vital aspect of governing and managing an economically-viable and livable city (Pregnolato, Ford, Wilkinson, et al., 2017).

Flooding, especially flash flood events due to extreme precipitation, is the predominant cause of weatherrelated disruption to the transport sector (DfT, 2014). Floods reduce transportation network capacity, either directly through physical destruction rendering roads unusable or through floodwater accumulation on the road surface rendering the road impassable. 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. These types of disruptions would cause significant socio-economic costs in the form of increased travel distances and times for commuters (B. Kim et al., 2018).

<mark>In the past decades, many studies have focused on understanding the economic impact of flooding on urban transportation networks using various types of methods, such as widely used input-output (I-O) models (</mark> T. J. Kim et al., 2002; Okuyama, 2007 <mark>), the dynamic REMI TranSight model by</mark> <u>Stewart (2006</u> <u><mark>), or the spatial</mark></u> <mark>computable general equilibrium (SCGE) model by T</mark> atano & Tsuchiya (2008 <mark>). Many of these models require high-quality traffic data, abundant travel supply and demand information and accurate hazard mapping, making it difficult to apply these methods in developing country cities where such information is scarce. This study overcomes these challenges and conducts a comprehensive assessment of the effect of flooding on transportation networks in Kinshasa, Democratic Republic of the Congo (DRC). This assessment is made possible through the use of state of the art transportation modeling techniques and by combining three data sets: 1) An innovative General Transit Feed Specification (GTFS) data set collected for the purpose of this analysis under normal (dry) and flooded (wet) conditions, 2) a travel survey data set with travelers’ socioeconomic attributes and Origin-Destination (OD) information, as well as 3) a set of high-resolution global flood maps that capture both the extent and depth of pluvial and fluvial floods. This study provides insights into the following research questions: 1) How does flooding impact multiple modes of transportation in Kinshasa in terms of headways, travel speed, travel itinerary, public transit service frequency, etc.? 2) How does flooding impact job accessibility and commuting times for different income groups and what are the corresponding opportunity costs? 3) Which road segments should be prioritized for flood protection and mitigation plans in terms of network criticality and vulnerability to pluvial and fluvial flooding?</mark>

# **2 Characterizing the Case Study Area**

<mark>Kinshasa, the capital of the DRC, is situated by the Congo River. Once a site of fishing and trading villages, Kinshasa is now a megacity with an estimated population of more than 14 million (</mark> “Kinshasa,” 2020), 2