# **4.1.3 Changes in travel speeds**

<mark>Unconditional comparisons of mean travel speeds as presented in</mark> Table <mark>2 suggest that speeds of some public transport modes increase during rain and floods. However this unexpected result is skewed for two reasons: (1) in contrast to the dry network, only a fraction of the wet network was surveyed so that different route samples prevent from direct comparisons, and (2) wet network routes that were surveyed substantially differ from dry network routes. They had different origin and destination coordinates and took different trajectories. In fact, when locating OD points, GoMetro reported that only 30 out of 130 wet network routes can be matched with a corresponding dry network route. Even on the matched routes, trajectories might differ substantially (for examples of routes considered as matched see</mark> Figure 8 <mark>). For these reasons, we could not rely on unconditional comparisons of average travel speeds on transit networks to understand the impact of heavy rain on travel speeds.</mark> <mark>In this section, we estimated speed effects only for the overlapping parts of the surveyed wet and dry networks and net of rerouting. We matched and compared travel speeds under dry and wet conditions locally (within a 100-meter radius) and conditional on the mode of transport.</mark><sup>10</sup> <mark>Relying on a bootstrap approach</mark> (Efron & Tibshirani, 1993 <mark>), we then</mark> showed that differences in travel speeds pointed in the expected direction and are significant. They were also larger on route segments situated in flood-prone areas, though the effect was estimated with low precision. Therefore, <mark>in the next section, we rely on dry route trajectories to build a representative wet network, but adjust travel speeds to wet conditions using estimates provided here. This was an important part of the analysis as few papers previously inferred the impact of flooding and rain on travel speeds in developing countries such as the DRC. Former research focused on developed countries where an abundance of public transit data facilitates the estimation. Further, the estimation of travel speeds under wet conditions is crucial in our simulation study that provides accessibility and economic loss estimates due to floods (sections 4.2 and 4.4).</mark>

# _<mark>Matching procedure</mark>_

<mark>The following lays out in detail the different steps involved in the matching procedure, illustrated in Figure 9. It also explains how significance tests were constructed, relying on the bootstrap methodology. We denote the set of all route segments: R = {R</mark> d <mark>, R</mark> w <mark>}. A route segment r is defined as part of a route extending from one stop point to the next. Data on route segments can directly be obtained from the GTFS</mark> dd ww <mark>feeds. First, each route segment is transformed into a set of support points by resampling points every d meters along the route (Figure 9, panel B). Denote the resulting set of support points P r ∈ P has coordinates sdi r. Hence the assumed travel speed at</mark> dd<sup><mark>. Each point</mark></sup> ii dd <mark>point r vd</mark><sup><mark>and inhabits all other attributes from route segment</mark></sup> <mark>i r, denoted v</mark> ii<sup><mark>, denoted, is identical to the speed on route segment</mark></sup> dd<sup><mark>.</mark>11</sup>

> 10 <mark>In robustness checks we further control for the time of the day but obtain similar results. The results are available on request.</mark>

> 11 This implies that we do not correct for effects of acceleration and deceleration between two stops. We do not have information available to compute such a correction. Further, since travel speeds on route segments are usually low (around 25 km/h, see Table 2), we expect acceleration and deceleration effects to be negligible for our analysis.

11