|11|Exposure to disaster|Overlay of natural disaster maps with the transport network. This<br>is often used for disaster preparedness studies|Connectivity|Local|
|---|---|---|---|---|
|12|Nearby alternative<br>elements|Number of other elements that are located within an x kilometer<br>distance from an element.|Connectivity|Local|
|13|Unweighted betweenness<br>centrality|Betweenness centrality calculated based on the shortest paths<br>among the network nodes.|Travel cost|Local|
|14|Change in network<br>average efficiency|Decrease in network efficiency due to disruptions of an element|Travel cost|Global|
|15|OD k-connectivity|Decrease in the number of distinct shortest paths among all<br>origin-destination pair due to a disruption of an element<br>|Connectivity|Global|
|16|Minimum link cut<br>centrality|The frequency of a link‚Äôs appearance in the cut sets of all<br>pairs of economic centroids|Connectivity|Global|
|17|Unsatisfied demand|Amount of transport activity that cannot take place due to<br>disruptions of an element|Connectivity|Global|

Figure 15 shows the results of the betweenness centrality analysis based on the topological network of the combined four transportation layers: Taxi Jaune, Esprit de Vie, Transco Bus, and Esprit de Mort. The pedestrian network is left out, as the additional nodes and edges from the pedestrian network will skew the betweenness calculation output, thus obscuring the betweenness measures of the public transportation networks. In the figure, line segments shown in red are indicators of roads that have high betweenness from a topological perspective.<sup>17</sup> Roads with relatively high betweenness centrality include the N1 expressway, Avenue Sergent Moke, Avenue O.U.A., Rte de Matadi, and Avenue de l'Université. It should be noted that this calculation method makes assumptions about the travel patterns in the transportation network which introduces errors in the measurement of critical links/road segments in reality. For example, it is possible that a road segment with high betweenness centrality connects regions with low population density. This measure fails to take into consideration the usage of road segments in reality. Therefore, in addition we adopted another adapted calculation method which is based on travel paths simulations using OD pairs from the JICA travel survey.

Figure 16 shows the results from the adapted betweenness centrality calculation. The main difference between this adapted approach and the conventional one (Figure 15) is the way travel paths are calculated and accounted for. The conventional approach calculates shortest paths between every possible pair of nodes within the network whereas the adapted approach simulates the travel trajectories based on the multimodal transportation model and accounts for public transport waiting times, road speed limits, and designated origin and destination pairs acquired from the JICA commuter travel survey. This adapted approach is able to reflect the actual travel patterns within the network and identify critical links/segments which are most commonly used in reality. Results show that Avenue Du 24 Novembre, east-west sections of the N1 expressway (including Boulevard Triomphal, Boulevard Sendwe, Boulevard Lumumba) have high betweenness centrality. This means that many people rely on these roads for daily commute to work. If there are disruptions along these road segments due to floods or traffic accidents then many commuters will likely experience higher levels of delay due to vehicle speed reduction, congestion, rerouting, etc.

> 17 The conventional betweenness measure focuses on the topology of the network itself and does not account for travel volume and patterns in the network.

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