1 ∆ = 15 − (20 + 20 + 22 + 22 + 24) = −6.6 [km/h] 5 dd <mark>The two central parameters in the matching procedure are the resampling distance</mark> vv <mark>d and the matching distance δ. The resampling distance d determines how many support points are generated. With decreasing d the number of support points increases which makes results more precise. But it also increases the computational costs of the matching procedure. When the matching distance δ is increased counterfactual dry speeds can be calculated for a larger share of support points. But at the same time the validity of the matches might suffer. For all results presented in the following a resampling distance d and a matching distance δ of 100 meters were chosen, finding a trade-off between computational costs and matching accuracy.</mark>

# _<mark>Bootstrap significance tests</mark>_

<mark>Using the matching method outlined above, an estimated speed difference can be attributed to most route segments mapped under wet conditions (see T</mark> able 4 <mark>for matching percentages by network type). To test whether the speed differences inferred are also significant, the bootstrap method was used. Under the null hypothesis, speeds under dry and wet conditions are the same. Under the alternative hypothesis, speeds under wet conditions are smaller than speeds under dry conditions: H</mark> 0 <mark>: ∆v = 0 vs. H</mark> 1 <mark>: ∆v < 0 We bootstrap the distribution of ∆v under the null hypothesis by randomly assigning the dry or wet attribute to route segments. This corresponds to reshuffling R</mark> w <mark>and R</mark> d <mark>such that route segments which were</mark> w d w d <mark>originally element in R (or R ) can be reassigned to either</mark> ww dd <mark>R or R since this assignment shouldn’t matter under the null hypothesis. For each bootstrap iteration</mark> ww dd <mark>b the assignment is reshuffled and</mark> ww dd <mark>∆v</mark> ) <mark>is calculated. Out of B bootstrap iterations one can then obtain an estimated p-value of the outlined test as</mark> (bb ∑ =1 <u>�</u> < ∆ ( <u>�</u> + 1 b = BB + 1 bb) bb II ∆vv vv bbww _<mark>Estimation of heterogeneous effects for flood prone areas</mark>_ pp BB <mark>After speed differences were estimated for each route r ∈ R</mark> w <mark>, it was analyzed whether routes that are likely to be flooded during the rainy season show larger speed reductions than routes that are unlikely to be</mark> ww <mark>flooded. Similarly, to a diff-in-diff-in-diff (DDD) method, ∆v whether the flood depth in a 10-year flood would be positive:</mark> dd<sup><mark>was regressed on a dummy, indicating</mark></sup> <mark>∆v = β</mark> 0 <mark>+ β</mark> 1<sup><mark>I</mark></sup> <mark>FD > 0) + ε Here, FD</mark> dd <mark>r in a 10-year flood as indicated through the Fathom flood</mark> dd dd<sup><mark>(</mark></sup> <mark>maps, β</mark> 0<sup><mark>is the average speed difference on routes that are unlikely to be flooded and</mark></sup> <mark>β</mark> 1<sup><mark>is the additional</mark></sup> dd<sup><mark>is the maximum flood depth on route</mark></sup> <mark>speed difference on routes likely to be flooded. As before p-values for β</mark> 0<sup><mark>and</mark></sup> <mark>β</mark> 1<sup><mark>were calculated using the</mark></sup> <mark>bootstrap methodology, allowing for potential correlation within the error term ε</mark> _<mark>Results</mark>_ dd<sup><mark>.</mark></sup> <mark>Results of the matching procedure outlined previously are presented in T</mark> able 4 <mark>. They indicate that travel speeds of different public transport modes are lower during wet conditions than during dry conditions. The</mark> 13