<mark>old models with exhaust pipes approximately 20 cm above the road surface. This means that when the flood water accumulates to 20 cm on roads, most vehicles would not be able to properly function. Among the 67,752 road segments in our study area, 802 had flood inundation depth greater than 0.2 cm.</mark> <mark>Next, we evaluated the impact of flooding on transport accessibility in Kinshasa by comparing the difference in travel time for commuters to reach their jobs. Generally speaking, the disturbance mechanism of floods on transportation network includes the following: 1) changes in headways, 2) road closure or rerouting, and 3) reduction in travel speed. These disturbance patterns are considered interdependent i.e. they can affect each other and the compounding effect of a combination of these patterns is greater than the sum. For example, reductions in travel speeds and blockages of streets due to flooding can lead to rerouting. Rerouting might allow higher travel speeds on alternative routes but can also lead to congestion. In turn, the overall impacts of flooding and rain on the public transport network are uncertain.</mark> <mark>We carefully examined the impact of flooding on each disturbance pattern and ‘perturbed’ the original multimodal network to create a network model that is able to capture all the changes in headways, road closure and travel speed reduction under a 10-year flood scenario. As only a fraction of the network under wet conditions was surveyed and we could not distinguish unsurveyed routes from routes non-operational due to flooding, we relied on the dry transport network to obtain a representative wet network. In particular, we obtained the wet network used for our accessibility simulations through the following modifications of the dry network:</mark>

- 1) <mark>We removed all Transco bus connections, as Transco busses were observed to be non-operational during heavy rain and flood.</mark>

- 2) <mark>We adjusted headways for each route conditional on network type as reported in section 4.1.1.</mark>

- 3) <mark>We reduced travel speeds on each route conditional on network type using the mean travel speed effects quantified in section 4.1.3: 4 km/h and 2 km/h speed reduction for Esprit de Mort and Esprit de Vie respectively.</mark>

- 4) <mark>We removed all route segments that are inundated in a 10-year flood</mark>

<mark>Building on the resulting wet network, we then ran travel simulations for a total of 8,866 commuters</mark><sup>13</sup> in <mark>Kinshasa using the origin and destination location information provided in the JICA commuter travel survey under both dry and wet conditions for five flood return periods.</mark> <mark>The left panel of F</mark> igure 10 <mark>shows the results of travel time simulations for 8,866 daily commuter trips under dry conditions. On average, the estimated daily commute time from home to work is 43.25 minutes. The right panel of F</mark> igure 10 <mark>shows the results from hot spot analysis of the travel time estimations with blue points showing clusters of low values and red points showing clusters of high values. We observe clusters of commuters who live in the suburban neighborhoods take longer to travel to work, whereas commuters who live in areas near the center of Kinshasa city take shorter time. F</mark> igure 11 <mark>compares the results from the travel time hot spot analysis with commuter personal income hot spots and highlights areas where these plots show diverging results. The southeastern and northeastern suburban areas show clusters of low-income values yet high travel time while the northern region near the Congo river show opposite patterns. This reveals socio-spatial equity issues, as certain low-income areas are systematically disadvantaged in terms of</mark>

> 13 The 8,866 commuters are a subset of the people who were surveyed who declared their travel origin and destination to work.

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