<mark>estimated speed differences also differ substantially from an unconditional comparison of speeds as presented in T</mark> able 2 <mark>, emphasizing the importance of a local comparison.</mark> <mark>The speed reduction is most pronounced and significant for the Esprit de Mort transport network, which operates under wet conditions on average with speeds about 4 km/h slower than under dry conditions. For the Esprit de Vie and the Taxi Jaune network average speeds are estimated to lie about 2 km/h and 0.5 km/h below dry condition speeds respectively, but effects are not significant.</mark> <mark>Considering the characteristics of the three different transport modes the estimated effects appear reasonable: The Esprit de Mort transport network is provided through old vehicles that are most likely to be affected through rain, mud, and wet conditions. On the Esprit de Vie network more modern mini-buses are operational. Taxi Jaune is the most flexible type of public transport infrastructure in Kinshasa and it is likely that the small vehicles can circumvent traffic jams due to flooding and easily reroute, hence obtaining speeds similar to dry conditions.</mark> <mark>Additional results from the DDD regressions suggest that travel speeds are more affected on route segments that are more likely to be flooded. When considering for example the Esprit de Mort network under wet conditions, average travel speeds are about 3.7 km/h below dry condition speeds on all route segments outside the 10-year flood plain. Within the 10-year-flood plain, the speed reduction is more pronounced, and speeds are on average an additional 6.1, i.e. 9.8 km/h in total, lower than during dry conditions. However, this effect cannot be estimated with sufficient precision to be significant, most likely due to the small sample size: only 5.5% of the mapped Esprit de Mort network lie within the 10-year flood plain.</mark> Table 4: Effects of rain and wet conditions on travel speeds by network type.

||**Esprit de M**<br>|**ort**<br>|**Esprit d**<br>|**e Vie**<br>|**Taxi Ja**<br>|**une**<br>|
|---|---|---|---|---|---|---|
||Estimate|p-value|Estimate|p-value|Estimate|p-value|
|Speed difference [km/h]<br>Mean<br>Median<sup>12</sup>|-3.892<br>-3.197|0.004<br>0.022|-1.667<br>-1.985|0.271<br>0.192|-0.574<br>-1.573|0.315<br>0.192|
|<br><br>|DDD estimates u<br>|sing 10-year r|eturn period flood<br>|map|||
|% wet network within flood plain<br><br>|5.50%||1.10%||9.23%||
|β0(baseline speed reduction)<br>|-3.700|0.004|-1.652|0.234|-0.812|0.299|
|β1 (additionaleffectin flood plain)|-6.136|0.248|-2.400|0.657|0.987|0.657|
|Route segments wet|401 (615 km)||91 (130 km)||325 (774 km)||
|Route segments dry|436 (725 km)||261 (354 km)||1646 (2254 km)||
|Percentage wet network matched|76.30%||80.86%||96.04%||
|_Notes: All p-values are based on 500 _|_bootstrap samples_||||||

# **4.2 Accessibility**

<mark>In this study, we measure workers’ accessibility to jobs, and how it is threatened by urban flooding. The first step to accessibility calculation is understanding flood exposure levels on the road network, an important step to flood risk evaluation (</mark> Hall et al., 2003; Pregnolato, Ford, Wilkinson, et al., 2017 <mark>). We quantified network exposure to the 10-year flood by spatially overlaying the maximum mosaic of pluvial and fluvial flood maps with the multi-modal transportation network. As a result, we were able to calculate the inundation level for each road segment in the study area. Next, we adjusted travel speeds on road segments based on inundation depth. After engaging with local transportation planning officials in Kinshasa, we came to the understanding that most vehicles that operate on the road networks in Kinshasa were relatively small and</mark> 12 Median speed differences are calculated through the matching methodology conceptually in the same manner as mean differences, but by replacing in the calculation of averages through median operators. 14 Δvv