Table 5: Comparison of one-hour job accessibility share under dry and wet conditions.

|**Network Type**|**Dry Condition Accessibility**|**Wet Condition Accessibility**|
|---|---|---|
|Walk|9.1%|8.9%|
|Drive|84.7%|73.7%|
|PublicTransit (meanwaiting time)|20.4%|15.4%|

# **4.3 Criticality**

<mark>Transport criticality analysis generally refers to the rank-ordering process of transportation network components based on their contribution to certain aspects of overall network performance. The output of such analysis is useful in providing guidance to road administrations in their prioritization of maintenance and repair of roads (</mark> Jenelius et al., 2006 <mark>). Currently, a variety of metrics have been shown to be useful in understanding network criticality</mark> (Jafino et al., 2020; Mattsson & Jenelius, 2015 <mark>) including betweenness, change in average network efficiency, k-connectivity, minimum link cut centrality, etc.</mark> Table <mark>6 provides a summary of network criticality metrics used in the existing literature. In this study, we choose betweenness centrality as a measure of road criticality due to its ability to provide a holistic view of the importance of road segments with respect to overall travel patterns.</mark> <mark>Betweenness centrality is first defined by Freeman (1977, 1978) to understand node criticality/importance in social networks. It measures the number of times that a node or edge in a network acts as a ‘bridge’ in allpairs shortest paths.</mark><sup>16</sup> <mark>Usually, a ‘bottleneck’ node or edge in a transportation network will have higher betweenness than other peripheral nodes or edges. The conventional betweenness centrality derived from network theory is applied to the multi-modal network model in Kinshasa. We also adapted this metric to one that is able to capture network flows based on simulated travel trajectories leveraging the 8,866 trip OD information provided in the JICA commuters travel survey.</mark> Table 6: Summary table of criticality metrics (adapted from Jafino et al. 2020).

|**No**|**Metric name**|**Description**|**Function**|**Scale**|
|---|---|---|---|---|
|1|Change in weighted total<br>travel cost|Increase in total travel cost (distance and traffic flow) among all<br>origin-destination pairs due to disruptions on an element|Travel cost|Global|
|2|Change in expected user<br>exposure|The average impact of disruptions experienced by all users in the<br>transport system|Travel cost|Global|
|3|Change in worst-case user<br>exposure|The maximum impact of disruptions among all users in the<br>transport system|Travel cost|Global|
|4|Change in unweighted<br>total travel cost|Increase in total travel cost (only distance) among all origin-<br>destination pairs due to disruptions on an element|Travel cost|Global|
|5|Change in region-based<br>unweighted total travel<br>cost|Increase in total travel distance among all origin-destination<br>pairs within a certain sub-area where the element is located due<br>to disruptions on an element|Travel cost|Local|
|6|Change in weighted<br>accessibility|Decrease in weighted (by transport demand/flow) accessibility<br>due to disruptions of an element. The weight is determined by<br>the socioeconomic activities|Accessibility|Global|
|7|Change in unweighted<br>daily accessibility|Decrease in unweighted, topological-based accessibility due to<br>disruptions of an element|Accessibility|Global|
|8|Traffic flow|Empirical traffic flow of the transport network|Travel cost|Local|
|9|Traffic density|Traffic volume over capacity. Normally used as an<br>approximation of congestion|Travel cost|Local|
|10|Weighted betweenness<br>centrality|The traffic flow among the economic centroids. The traffic flow<br>is determined by the socioeconomic profiles|Travel cost|Local|

16All-pairs shortest paths asks for the shortest path from every possible source to every possible destination.

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