metadata: authors: 0: author: He,Yiyi
1: author: Thies,Stephan Fabian
2: author: Avner,Paolo
3: author: Maruyama Rentschler,Jun Erik
count: Congo, Democratic Republic of
docna: 0: docna: The Impact of Flooding on Urban Transit and
            Accessibility : A Case Study of Kinshasa
docty: Policy Research Working Paper
subtopic: Natural Disasters,Transport Services,Transport in Urban Areas,Urban Transport,Intelligent Transport Systems,Labor Markets
teratopic: Environment,Urban Development,Transport,Social Protections and Labor
abstracts: cdata!: Transportation networks underpin
            socioeconomic development by enabling the movement of goods
            and people. However, little is known about how flooding
            disrupts transportation systems in urban areas in developing
            country cities, despite these natural disasters occurring
            frequently. This study documents the channels through which
            regular flooding in Kinshasa, the Democratic Republic of
            Congo, impacts transport services, commuters' ability
            to reach their jobs, and the associated economic opportunity
            costs from travel delays. This assessment is based on
            transit feed specification data sets collected specifically
            for this analysis under normal and flooded conditions. These
            data sets were combined with travel survey data containing
            travelers' socioeconomic attributes and trip
            parameters, as well as a high-resolution flood maps. The
            results show that (1) flood disruptions cause increases in
            public transit headways and transit re-routing, decreases in
            travel speeds, and thus travel time delays, which translate
            into substantial economic costs to local commuters; (2)
            accessibility to jobs decreases under flooded conditions,
            hindering the establishment of an integrated citywide labor
            market; (3) there are spatial clusters where some of the
            poorest commuters experience among the highest travel
            delays, highlighting socio-spatial equity aspects of floods;
            (4) certain road segments are critical for the transport
            network and should be prioritized for resilience measures;
            and (5) the estimated daily cost of flood disruption to
            commuters’ trips in Kinshasa is $1,166,000. The findings of
            this assessment provide disaster mitigation guidance to the
            Office des Voiries et Drainage under the Ministry of
            Infrastructure, as well as strategic investment
            recommendations to the Ministry of Housing and Planning.
geo_reg_key: 517196,517191
repnme: repnme: The Impact of Flooding on Urban Transit and
            Accessibility : A Case Study of Kinshasa
display_title: The Impact of Flooding on Urban
            Transit and Accessibility : A Case Study of Kinshasa
listing_relative_url: /research/2020/12/32683033/impact-flooding-urban-transit-accessibility-case-study-kinshasa
topicv3: Transport m1326661 135,Mass Transit m1327372 1726,Flood m1503315 3216,Jobs m1503931 2670,Traffic Management m1583399 4231
publishtoextweb_dt: 2020-12-17T00:00:00Z
dois: 10.1596/1813-9450-9504
historic_topic: Environment,Urban Development,Transport,Social Protections and Labor
origu: Climate Change-D1-GFDRR-IBRD (GFDRR)
no_of_pages: 31
author_keys: 0: author_key: 1896944
1: author_key: 1815783
2: author_key: 1256219
3: author_key: 1217979
model_extractions: Policy Research Working Paper 9504 The Impact of Flooding on Urban Transit and Accessibility A Case Study of Kinshasa _Yiyi He Stephan Thies Paolo Avner Jun Rentschler_ Global Facility for Disaster Reduction and Recovery December 2020
Policy Research Working Paper 9504

# **Abstract**

Transportation networks underpin socioeconomic development by enabling the movement of goods and people. However, little is known about how flooding disrupts transportation systems in urban areas in developing country cities, despite these natural disasters occurring frequently. This study documents the channels through which regular flooding in Kinshasa, the Democratic Republic of Congo, impacts transport services, commuters’ ability to reach their jobs, and the associated economic opportunity costs from travel delays. This assessment is based on transit feed specification data sets collected specifically for this analysis under normal and flooded conditions. These data sets were combined with travel survey data containing travelers’ socioeconomic attributes and trip parameters, as well as a high-resolution flood maps. The results show that (1) flood disruptions cause increases in public transit headways and transit re-routing, decreases in travel speeds, and thus travel time delays, which translate into substantial economic costs to local commuters; (2) accessibility to jobs decreases under flooded conditions, hindering the establishment of an integrated citywide labor market; (3) there are spatial clusters where some of the poorest commuters experience among the highest travel delays, highlighting socio-spatial equity aspects of floods; (4) certain road segments are critical for the transport network and should be prioritized for resilience measures; and (5) the estimated daily cost of flood disruption to commuters’ trips in Kinshasa is $1,166,000. The findings of this assessment provide disaster mitigation guidance to the Office des Voiries et Drainage under the Ministry of Infrastructure, as well as strategic investment recommendations to the Ministry of Housing and Planning.

This paper is a product of the Global Facility for Disaster Reduction and Recovery. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at yiyi_he@berkeley.edu, pavner@worldbank.org, and jrentschler@worldbank.org..

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
The Impact of Flooding on Urban Transit and Accessibility — A Case Study of Kinshasa _Yiyi He, Stephan Thies, Paolo Avner, Jun Rentschler_

**<mark>Keywords</mark>** <mark>: flooding, multi-modal network model, travel disruption, infrastructure resilience, GTFS</mark>

**JEL Codes** : Q54, C63, R11, R14, R23

**Acknowledgments:** We would like to thank GoMetro for leading the public transport mapping, and in particular Bob Kabeya and Clayton Lane, as well as the enumerators that conducted these surveys. We thank Shohei Nakamura, Takaaki Masaki and Mervy Ever Viboudoulou Vilpoux for their help in accessing Kinshasa commuter and household surveys. We are grateful to Laurent Corroyer, Stephane Hallegatte and Kirsten Homman for their inputs and feedback throughout this project. We are also grateful to Catalina Ochoa for her review of an earlier draft of this paper and her thoughtful comments. This study was supported by the Global Facility for Disaster Risk Reduction and Recovery (GFDRR).
# **1 Introduction**

Transportation networks provide critical socio-economic services at local and regional scales. However, demographic and economic growth, rapid urbanization, increasing infrastructure interdependency, and natural hazards – due to the worsening effects of climate change – are putting physical transportation systems under increasing pressure (Chen & Wang, 2019; Schreider et al., 2000). This is particularly evident in urban areas where transport networks are especially vulnerable to weather-related hazards (Pregnolato, Ford, Glenis, et al., 2017; Singh et al., 2018; Yin et al., 2016). Improving their resilience to natural hazards and extreme weather events has become a vital aspect of governing and managing an economically-viable and livable city (Pregnolato, Ford, Wilkinson, et al., 2017).

Flooding, especially flash flood events due to extreme precipitation, is the predominant cause of weatherrelated disruption to the transport sector (DfT, 2014). Floods reduce transportation network capacity, either directly through physical destruction rendering roads unusable or through floodwater accumulation on the road surface rendering the road impassable. Both cases could lead to severe localized traffic congestion that later propagates to regional levels (Mao et al., 2012; Rentschler et al., 2019). Kasmalkar et al., (2020) studied the impact of coastal flooding with respect to traffic disruption in the San Francisco Bay Area and showed that flooding would render road segments impassable and lead to significant delays in commuters’ travel times. In some areas, the delay was more than double the average travel time within the region of study. These types of disruptions would cause significant socio-economic costs in the form of increased travel distances and times for commuters (B. Kim et al., 2018).

<mark>In the past decades, many studies have focused on understanding the economic impact of flooding on urban transportation networks using various types of methods, such as widely used input-output (I-O) models (</mark> T. J. Kim et al., 2002; Okuyama, 2007 <mark>), the dynamic REMI TranSight model by</mark> <u>Stewart (2006</u> <u><mark>), or the spatial</mark></u> <mark>computable general equilibrium (SCGE) model by T</mark> atano & Tsuchiya (2008 <mark>). Many of these models require high-quality traffic data, abundant travel supply and demand information and accurate hazard mapping, making it difficult to apply these methods in developing country cities where such information is scarce. This study overcomes these challenges and conducts a comprehensive assessment of the effect of flooding on transportation networks in Kinshasa, Democratic Republic of the Congo (DRC). This assessment is made possible through the use of state of the art transportation modeling techniques and by combining three data sets: 1) An innovative General Transit Feed Specification (GTFS) data set collected for the purpose of this analysis under normal (dry) and flooded (wet) conditions, 2) a travel survey data set with travelers’ socioeconomic attributes and Origin-Destination (OD) information, as well as 3) a set of high-resolution global flood maps that capture both the extent and depth of pluvial and fluvial floods. This study provides insights into the following research questions: 1) How does flooding impact multiple modes of transportation in Kinshasa in terms of headways, travel speed, travel itinerary, public transit service frequency, etc.? 2) How does flooding impact job accessibility and commuting times for different income groups and what are the corresponding opportunity costs? 3) Which road segments should be prioritized for flood protection and mitigation plans in terms of network criticality and vulnerability to pluvial and fluvial flooding?</mark>

# **2 Characterizing the Case Study Area**

<mark>Kinshasa, the capital of the DRC, is situated by the Congo River. Once a site of fishing and trading villages, Kinshasa is now a megacity with an estimated population of more than 14 million (</mark> “Kinshasa,” 2020), 2
<!-- Start of picture text --> NORTH ~ aS<br>| Congo river basin boundary a SS ay<br>i Kinshasa city iy e cS<br>—— Major river channels be. ey 4 \<br>EEE Waterbodies LAK <a Ee ES. EY.<br>Elevation pee Wi Kr Re! nie) <a a. bao?<br>Unit: meters fe r AS a<br>mom High: 4115 ) | NN Y x<br>=" Low:<br>0 6 i aN sigsPays<br>250 500 1,000 KM ee if YC<br><!-- End of picture text -->
<!-- Start of picture text --> Unit:Populationite Density by TAZ Travel connection map $400 - $499 _ egiesig,<br>people per sq.km by personal income levels $500 — $749 E> zi<br>—_] 0 - 3200 iy ail ee less than $25 750<br>[) 3201 - 12100 ; < Se $ — $$999<br>\ | | \ $25 — $100 $1000<br>(HY 12101 - 30300 \ A - $1499<br>[EE 30301 - 48100 @ \ Sf #100.=9198 —— $1500 - $1999<br>[ER | $200 - $299 — =<br>Lg 4810166701 -- 66700 90600 Nn\ ] || —— —— $3000ite andgu Above Aeae<br>HE 90601 - 144600 L ae<br>ah en ce<br>ak<br>d a AY Me EG I Say<br>» A af bay NS ST KATO \ \ ye \esg ips, oR i a Fe Su aaee :<br>fou wr NORTH Vag = Se NORTH<br>oes 5 10 KM brs 0 25 5 10 KM<br><!-- End of picture text -->
<!-- Start of picture text --> 350 120.00%<br>300 100.00%<br>250<br>80.00%<br>o 200<br>2<br>S 60.00%<br>oO<br>® 150<br>Ww<br>40.00%<br>100<br>50 20.00%<br>%<br>| | TT | MFrequency Cumulative<br>fe) TH | TT Ce - 0.00%<br>oe © Oe © es © ee Oe © ee 2 ee Oe Oe 2 ee 2 ee Oe Oe Oe Oe Oe oe Oe On On oe Oe Oe oe)<br>N TO WOa NTod A ATTAHO WONTNNNNNBO DW TOTMONNTMH MHBO MHWONTFT TFT TFTtT TFTO FTDW ODOwn<br>Daily average rainfall (mm)<br><!-- End of picture text -->
Table 1: Details of five modes of public transportation operating in Kinshasa.

|**Transco**|**Esprit de Vie**|**Esprit de Mort**|**Taxi Jaune**|**Moto Taxi**|
|---|---|---|---|---|
|The main public bus<br>service in Kinshasa. In<br>2020, there are more<br>than 40 bus routes<br>with a total of 986 bus<br>stops (Moovit, 2020).|Minibus taxis with a<br>capacity of up to 22<br>seats.|Bigger taxis which are<br>usually operated with<br>Mercedes Benz 207<br>models for inter<br>suburbs trips and long-<br>distance travels within<br>the city.|Cabs painted in yellow<br>with a capacity of four<br>passengers. They<br>usually operate<br>between neighboring<br>suburbs and complete<br>most of the first and<br>last mile services<br>within the city.|Informal moto taxi<br>that provides<br>connection to areas<br>that are enclaved and<br>not easily accessible<br>due the condition of<br>roads and congestion<br>levels. In other<br>instances, trips shorter<br>than three kilometers<br>are done using moto<br>taxi as a first and last<br>mile service.|

Table 2: Summary details of GTFS trip mapping under dry and wet conditions.<sup>5</sup>

||**Tran**<br>|**sco**|**Esprit**<br>|**de Mort**|**Esprit**<br>|**de Vie**|**Taxi J**<br>|**aune**|**Moto**<br>|**Taxi**|
|---|---|---|---|---|---|---|---|---|---|---|
||**_dry_**|**_wet_**|**_dry_**|**_wet_**|**_dry_**|**_wet_**|**_dry_**|**_wet_**|**_dry_**|**_wet_**|
|**Number of routes**|42|-|66|52|31|11|299|67|39|-|
|**Number of stops**|499|-|502|453|292|102|1945|392|97|-|
|**Number of route segments**|457|-|436|401|261|91|1646|325|58|-|
|**Total network length (km)**|623|-|725|615|355|130|2254|774|172|-|
|**Mean route segment length (km)**|1.36|-|1.66|1.53|1.36|1.43|1.37|2.38|2.97|-|
|**Mean travel speed (km/h)**|24.7|-|24.2|26.7|24.8|21.9|24.8|27.3|23.9|-|
|**Median travel speed (km/h)**|18.5|-|22.3|17.0|22.0|17.0|17.7|19.3|22.8|-|
|**Max travel speed**<sup>6</sup> **(km/h)**|178.3|-|101.6|99.0|96.2|106.6|148.2|89.6|80.5|-|

# **3.3 Open Street Map (OSM) Road Network Data Set**

<mark>In addition to the five public transportation networks, we also added the drive as well as the pedestrian network leveraging the Open Street Map data set to account for commuters who walk or drive to work. The OSMnx Python package was used to create topological graph structures for the drive and pedestrian network layers (</mark> Boeing, 2017 <mark>). These two network layers were later integrated with the five public transport layers to create the final multi-modal network</mark><sup>7</sup> <mark>model for Kinshasa.</mark>

> 5 There are no GTFS records for Transco bus and Moto taxi mapping under flooded (wet) conditions.

> 6 Unreasonably high travel speeds can occur due to small inaccuracies in the GTFS files, but are clear outliers. If two stop points along a route are very close (in reality or due to GPS inaccuracies) and a surveyor enters a travel time which is a few seconds off, resulting travel speeds might exceed 100 km/h, which is unreasonable in an urban public transport setting. In the entire data set there are fewer than 10 route segments (<0.3%) affected by such inaccuracies.

> 7 The multi-modal network is created based on both the GTFS feed and OSM data set, which provide speed limits for every road segment. For the pedestrian network, we assume 4.5km/h travel speed.

6
<!-- Start of picture text --> NORTH Ke<br>0 25 5 10 KM<br>-—+—_+—_+— y<br>—— Esprit de Vie<br>——— Moto Taxi &<br>XY k<br>— — — _ TranscoEsprit de Mort “R @ oe: ;<br>————~ Taxi Jaune inn<br>——— Drive ates|<br>ys<br>Pedestrian or<br>Q<br>% iS Gea oAw<br><!-- End of picture text -->
<!-- Start of picture text --> 10-year 50-year i 100-year at,<br>i po Jo jaf. _ fei<br>FTES > ‘se aa fs PAGERS _ Jian wes fF PERS _ are Wie<br>500-year _- 1000-year , be A<br>y tO 2 Oy NORTH<br>y Sie AEE: Flood depth (pluvial and fluvial)<br>ss Ya LY eed .<br>‘ a f eae Unit: meters<br>“ on? by yn 234 pa No flood<br>} FOREN BOCK FS | TECEERS ae wae<br>< fe gs aad WSS VeeS Pe SMES hes Ree yy 0.5-1.0<br>et BS PNP Da fe, ae ee este 2 Public transport network ———<br>BAXRKI pt Soe SORE ERC weeSAIS eae nS 0510 20KM<br><!-- End of picture text -->
<!-- Start of picture text --> Transport feed data Road network data Flood maps Household &<br>(GTFS) (Open Street Map) (Fathom) Commuter surveys<br>Public transport mapping Flood depths and Travel patterns and<br>under dry and wet Walk and drive networks extents for multiple travel modes +<br>conditions return periods Income data<br>Multimodal networks under dry<br>and wet conditions<br>Flood impacts Accessibility Critical road segments Economic costs due to<br>on transit patterns under dry and wet conditions identification travel delays<br><!-- End of picture text -->
<!-- Start of picture text --> . SS<br>— pra 2s clit<br>erick oe route oa ee<br>:<br>A Ty ti = RS SSS ms |<br>ih<br>Nie en Sie! ae ASSer hos exas=5 peSENT hg?<br>con eee aOR NG TS Knog<br>Wn 27d Fai Ay Ais Be)"<br>eymee‘Zaee diedmecx]ays == <ass| s=Ss_———s "SSbe Py issSe, |ae i a4VisesWar]a<br>ag SSS ES Ras<br>WBZ NZS<br>—= aa=aCNV NSHRESZENA TN HIN\\ at A!\\ esetINETWIAA/apsa SSSA|<br><!-- End of picture text -->

<!-- Start of picture text --> — tas | cd Mates: tag Se ese<br>== dry conditiition ro = ae a!<br>; maeShi o *<br>ee ute scofitiea e<br>RESi a<br>re ca ZO» meetsi eter<br>ae: Zag QOL ESA pee<br>as 7 ok<br>(TER SG abr. saa eiieeeeee<br>ry —ee ceNeeNe <zeee!<br>MA<br>i<br>’<br>cee<br>Rentini. a0 passe f<br><!-- End of picture text -->

<!-- Start of picture text --> wet condition route BattasicsLes etd Kateeee<br>lo ry condit Aeage ae ‘imate s<br>d ition es ble, lea Ee<br>Sa a ieee<br>na ree<br>eee) ea ey<br><!-- End of picture text -->
# **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
<!-- Start of picture text --> A) Input Data : B) Creation of : C) Matching on6 i<br>: support points t : distance f :<br>20 ,)<br> km/h $ ee<br>| WAT<br>24 km/h<br>D) Determining valid E) Counterfactual H<br>matches, i.e.M,; © speed construction @<br>. .<br>AA A Ak<br>Legend CH) ee<br>—. Wetfuecondition segment ezWY» cows ohxmax ae<br>v<br><!-- End of picture text -->

~~—~~ :D ~~<mark>—</mark>~~ (v -@ )
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
<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
<mark>old models with exhaust pipes approximately 20 cm above the road surface. This means that when the flood water accumulates to 20 cm on roads, most vehicles would not be able to properly function. Among the 67,752 road segments in our study area, 802 had flood inundation depth greater than 0.2 cm.</mark> <mark>Next, we evaluated the impact of flooding on transport accessibility in Kinshasa by comparing the difference in travel time for commuters to reach their jobs. Generally speaking, the disturbance mechanism of floods on transportation network includes the following: 1) changes in headways, 2) road closure or rerouting, and 3) reduction in travel speed. These disturbance patterns are considered interdependent i.e. they can affect each other and the compounding effect of a combination of these patterns is greater than the sum. For example, reductions in travel speeds and blockages of streets due to flooding can lead to rerouting. Rerouting might allow higher travel speeds on alternative routes but can also lead to congestion. In turn, the overall impacts of flooding and rain on the public transport network are uncertain.</mark> <mark>We carefully examined the impact of flooding on each disturbance pattern and ‘perturbed’ the original multimodal network to create a network model that is able to capture all the changes in headways, road closure and travel speed reduction under a 10-year flood scenario. As only a fraction of the network under wet conditions was surveyed and we could not distinguish unsurveyed routes from routes non-operational due to flooding, we relied on the dry transport network to obtain a representative wet network. In particular, we obtained the wet network used for our accessibility simulations through the following modifications of the dry network:</mark>

- 1) <mark>We removed all Transco bus connections, as Transco busses were observed to be non-operational during heavy rain and flood.</mark>

- 2) <mark>We adjusted headways for each route conditional on network type as reported in section 4.1.1.</mark>

- 3) <mark>We reduced travel speeds on each route conditional on network type using the mean travel speed effects quantified in section 4.1.3: 4 km/h and 2 km/h speed reduction for Esprit de Mort and Esprit de Vie respectively.</mark>

- 4) <mark>We removed all route segments that are inundated in a 10-year flood</mark>

<mark>Building on the resulting wet network, we then ran travel simulations for a total of 8,866 commuters</mark><sup>13</sup> in <mark>Kinshasa using the origin and destination location information provided in the JICA commuter travel survey under both dry and wet conditions for five flood return periods.</mark> <mark>The left panel of F</mark> igure 10 <mark>shows the results of travel time simulations for 8,866 daily commuter trips under dry conditions. On average, the estimated daily commute time from home to work is 43.25 minutes. The right panel of F</mark> igure 10 <mark>shows the results from hot spot analysis of the travel time estimations with blue points showing clusters of low values and red points showing clusters of high values. We observe clusters of commuters who live in the suburban neighborhoods take longer to travel to work, whereas commuters who live in areas near the center of Kinshasa city take shorter time. F</mark> igure 11 <mark>compares the results from the travel time hot spot analysis with commuter personal income hot spots and highlights areas where these plots show diverging results. The southeastern and northeastern suburban areas show clusters of low-income values yet high travel time while the northern region near the Congo river show opposite patterns. This reveals socio-spatial equity issues, as certain low-income areas are systematically disadvantaged in terms of</mark>

> 13 The 8,866 commuters are a subset of the people who were surveyed who declared their travel origin and destination to work.

15
<!-- Start of picture text --> Travel time (dry condition) fe Travel time hot spot analysis (dry condition) F<br>» <5 min Ves * Hot Spot - 99% Confidence y<br>- 5-10min Ne > Hot Spot - 95% Confidence \<br>10 - 30 min iF Hot Spot - 90% Confidence a<br>30 - 45 min | Not Significant |<br>45 - 60 min ER Cold Spot - 90% Confidence 4<br>-- 60-120>4120 min min 4_ ** ColdCold SpotSpot -- 95%99% ConfidenceConfidence a<br>—— Road network<br>Road network<br>at EES, egRis ito L530; gE 2 ee | $i: i ~, A xo<br>31D Baa (Pea ie BRN eg oe: ers ( °<br>May1s Bare. “el FS Rs Pee f.<br>., Af RS wnt BO Myme SE.« A . wa N . os mee TS A<br>Me oeaey a OSBeed HPAES...os NORTH ° }- =.\ ) out DtsSh",° NORTH<br>‘ 0 25 5 10 KM \ 0 25 5 10 KM<br>swf, ot/<br><!-- End of picture text -->

<!-- Start of picture text --> Income hotspot analysis<br>* Hot Spot - 99% Confidence<br>» Hot Spot - 95% Confidence<br>Hot Spot - 90% Confidence ad<br>Not Significant |<br>Cold Spot - 90% Confidence ‘<br>* Cold Spot - 95% Confidence<br>* Cold Spot - 99% Confidence<br>—— Road network<br>ee . =ad rahe 0 ngPO Bees<br>rd ” j 4 & i, eo" ve.<br>a ee Pe ae<br>Yar Hy BRS NORTH<br>a,b 025 5 10 KM<br><!-- End of picture text -->

<!-- Start of picture text --> Travel time hot spot analysis (dry condition)<br>* Hot Spot - 99% Confidence<br>* Hot Spot - 95% Confidence<br>Hot Spot - 90% Confidence r<br>Not Significant |<br>Cold Spot - 90% Confidence S<br>* Cold Spot - 95% Confidence<br>* Cold Spot - 99% Confidence<br>—— Road network<br>a ae= — cA / oe .<br>, tag \ " 4 fry ye .<br>see oh Som, =A 7<br>me, 4 NORTH<br>ot/\ 025 5 10 KM<br><!-- End of picture text -->
<!-- Start of picture text --> Average delay Maluku Delay hot spot analysis oF<br>Unit: minutes * Cold Spot - 99% Confidence<br>Mi o-s + Cold Spot-95% Confidence<br>MN 5-10 * Cold Spot-90% Confidence<br>(10-30 \ - Not Significant<br>i 30 - 60 * Hot Spot - 90% Confidence og<br>(MM 60-90 _~____*_ Hot Spot - 95% Confidence<br>HN 00 - 120 * Hot Spot - 99% Confidence<br>> 20 }<br>Traffic Analysis Zone |<br>: ~ ee wr Oe at<br>N'sele gle le a iy ~ ¥o<br>Lutendele Vara 4 LE Sc Haeoe<br>I f .<br>Fm ~~<br>Cr \ |<br>Kimvula \—_ jili kilambo \\ NORTH<br>MiLsli == 0a25 5 10 KM<br><!-- End of picture text -->
<!-- Start of picture text --> Walk ; we<br>wn<br>co)<br>Qa<br>fea)<br>°<br>=<br>o l<br>2<br>(mean waiting time)<br>Public Transport) | wary<br>0 20 40 60 80<br>Percent Loss of Job Accessibility<br><!-- End of picture text -->

<!-- Start of picture text --> Percent loss of job access A Percent loss of job access A<br>0.0 - 0.10 ® NORTH 0.947 - 5.00 NORTH<br>0.11 - 1.0 5.01 - 10.0 f<br>Mi 1.1-20 MM 10.1 - 15.0<br>MM 2.1 -3.0 MM 15.1 - 50.0 p<br>HMM 3.1 - 4.0 HB 50.1 - 92.7<br>onof DEN) a Sa, x oem a say<br>d“—=<br>02.55 10KM | 02.55 10KM<br>poe H++4H<br>Percent0.0 loss of jjob access A PopulationP Densityy (2017 (2017) " A<br>WM 11-50- 1.0 “<> NORTH 510.0 -- 250 50 ePa NORTH<br>MM 5.1-7.0 MM 260 - 500<br>Wi 7.1 -10 HE510 - 1000<br>Ml 11-20 HM 1100- 1400 Wee<br>» Re y ’ | AAS > y .<br>> i a2” 7 Pea & ef wn Be<br>02.55 10KM 02.55 10KM<br>K+++H4 -K++++H4<br><!-- End of picture text -->
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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|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.

20
<!-- Start of picture text --> Betweenness<br>Four combined networks<br>@ 88047- 172694<br>@ 58541- 88046<br>© 41543- 58540<br>* 27426- 41542<br>* 14711- 27425<br>- 4704- 14710<br>0 - 4703<br>——— Road network<br>»<br>ZAMS<br>hea<br>IN<br>4,<br>NORTH<br>0 25 5 10 KM<br>+—+—+—_ +<br><!-- End of picture text --> <!-- Start of picture text --> Betweenness based on travel paths Roi<br>@ 251-400<br>© 151-250<br>° 101- 150<br>* 51-100<br>21-50 )<br>6-20 ’<br>1-5<br>—— Road network<br>Mins ak f<br>vee<br>Pal Ves ‘<br>‘ Hie ow<br>Se ATO a ES A \<br>< ard GiA3 | "aNSe NORTH<br>1 0 25 5 10 KM<br>+++<br>a<br><!-- End of picture text -->
<mark>This does not include other considerations such as production losses due to supply chain disruptions, or welfare implications of lost access to critical services and supplies.</mark> To calculate the baseline travel delay cost, we first estimated the travel time for each commuter trip (8,866 total) under five flood scenarios using the walking, the drive and the public transit networks based on travel mode information reported in the JICA survey. Next, we calculated travel delay for each flood scenario by subtracting the travel time under dry condition from the estimations. This result was then combined with the personal monthly income information provided in the JICA survey. For each OD trip, we multiplied the delay in travel time due to flooding by the income per unit of time assuming that each worker works eight hours a day, five days a week, and on average 22 days a month. The corresponding output was then doubled to account for return trips from work. To adjust for the difference between sample size and survey population, the cost was multiplied by respective weighting factors for each trip. This same process was repeated for five flood return periods namely: 10-year, 50-year, 100-year, 500-year and 1000-year and the economic cost for all 8,866 trips were later aggregated to the TAZ level. Table 7 shows the number of commuters’ trips affected by five flood scenarios under different delay thresholds. Figure 17 illustrates maps of the averaged economic cost due to travel delay for each TAZ under all flood scenarios. We observe that areas towards the center of Kinshasa city experience relatively higher cost than peripheral suburbs in the south-east and northeast with the exception of the smaller TAZs situated in the heart of the city. It should be noted that the averaged cost aggregation at the TAZ level is more representative in the central regions of the city. Due to the uneven spatial distribution of sampling locations, the peripheral TAZs only contain a few sampling points. For example, the Kinsielele region towards the south of the city shows up with relatively high travel delay costs under all flood scenarios yet there are only 11 sampling points in this particular TAZ.

Figure 18 shows two sets of grouped boxplots of estimated travel time delay in minutes and corresponding economic loss for different levels of personal income under five flood scenarios. In general, floods with longer return periods (more extreme) on average induce longer travel delays and more economic cost compared with those of shorter return periods. When it comes to the variation or uncertainty of flood impacts, the 100-year and 500-year floods are relatively higher than floods of other return periods. Specifically, the 100-year flood’s impact on commuters’ travel delays has the highest variation/uncertainty and the 500-year flood is the highest in terms of economic cost variation/uncertainty. When we examine these two types of impact: travel delay and economic cost by different income groups, it is clear that commuters with medium income levels (monthly salary from $100 to $1,000) experience relatively higher travel delays than low income (monthly salary below $100) commuters and high income (monthly salary greater than $3,000) commuters. It should be noted that the lower-income groups have more extreme estimates (over one hour) in travel delays which suggest the existence of highly disadvantaged commuter groups who are heavily impacted by flood disruptions on road networks.

Table 7: Number of commuter trips affected (8866 total) by delay thresholds.

|**Flood return period**|**>5 min delay**|**>10 min delay**|**>30 min delay**|**>1 hour delay**|**>2 hour delay**|**Number of**<br>**failed trips**|
|---|---|---|---|---|---|---|
|**10-year flood**|7520|6491|4261|2418|342|679|
|**50-year flood**|7007|6047|4085|2810|1049|1217|
|**100-year flood**|6779|5842|4032|3027|1821|1453|
|**500-year flood**|5940|5047|3408|2590|1822|2329|
|**1000-year flood**|5344|4475|2878|2108|1477|2924|

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<!-- Start of picture text --> 10-year flood<br>0255 10 KM<br>><br><!-- End of picture text --> <!-- Start of picture text --> 100-year flood<br>0255 10 KM<br>of4 25:ese Van oi<br>a ee ><br>A B Cc<br>I) o- $500 I) $3,500 - $5,000<br>$500 - $1,000 $5,000 - $7,500<br>| $1,000 - $1,500 [J $7,500 - $10,000<br>"$1,50- $2, 0 00 ff > $10,000<br>"$2,000- $3,500 [_] TAZs<br><!-- End of picture text -->

<!-- Start of picture text --> 50-year flood<br>0255 10 KM<br>ef 28!& 8 y<br>“Yd v ig ,<br><!-- End of picture text --> <!-- Start of picture text --> 500-year flood<br>0255 10 KM<br>ee é<br>BRCpay<br>6 m ik? q<br>(* aoe Wr<br>a !<br><!-- End of picture text --> <!-- Start of picture text --> 1000-year flood<br>0255 10 KM<br>‘i<br>aes A<br>SOYvere<br>“ i<br>mY op We |<br>.<br><!-- End of picture text -->

<!-- Start of picture text --> = 800<br>rs '<br>E600 -<br>3 : | ‘ i, : | H i ' H ’ i<br>2 400 i]; an 4 i ;<br>5 | | 7 ‘ : ‘ ‘ mo<br>g 200 i ' : . hag dh<br>o i! :<br>fo}<br>=: 0 ‘mm<br>Less than $100 $100-$199 $200-$299 $300-$399 $400-$499 $500-$999 $1000-$1499 $1500-$1999 $2000-52999 $3000 and above<br>Sa eT ae<br>2D i ead t H<br>= 20,000 a :<br>£ 40,000 . _<br>g () 10-year flood oo<br>2 60,000 (Ns 50-year flood .<br>§ (Ql 100-year flood : ‘<br>80,000 (Ql 500-year flood<br>Ml 1000-year flood .<br><!-- End of picture text -->
In sum, the total weighted daily travel delay costs<sup>18</sup> due to 10-year, 50-year, 100-year, 500-year and 1000year floods are $5,367,000, $8,642,000, $11,628,000, $13,304,000 and $14,374,000 respectively. <mark>When taking flood occurrence probability (i.e. 1in 10, 50, 100, 500 and 1000 years) into consideration, the average daily transport disruption cost weighted by occurrence probability is $1,166,000.</mark> If we assume that on average there are 28 days of flood disruptions on the Kinshasa transportation network then the annualized amount lost to floods is <mark>$32,648,000. It should be underlined that these amounts, because they only consider time lost for commuting purposes and not direct damages or other indirect costs such as disrupted supply chains, constitute a conservative evaluation of the costs of floods in Kinshasa.</mark>

# **5 Conclusion and Discussion**

In Kinshasa, DRC, the combination of pluvial and fluvial floods of multiple return periods impacts all transportation modes and causes a reduction in vehicle speeds, road closures, lengthened daily commute patterns, reduced job accessibility and economic costs due to travel delay. Under wet conditions, the headways for transit modes such as Esprit de Mort increase by 42% from 31 to 44 minutes on average. Flood water accumulation on road surfaces that exceeds 0.2 meters in depth will cause road closures and blockages. Consequently, commuters’ daily trips to and from work need to be rerouted or canceled resulting in pronounced delays in travel time and associated economic costs. Based on our estimation, the total economic cost with respect to daily commuters’ travel delay is $5,367,000 under the 10-year flood scenario. Combined with simulated outputs from multiple flood return periods, the daily weighted average of travel delay cost caused by flood occurrence probability is $1,166,000. It should be noted that this estimation provides a lower bound to the total economic loss due to such type of flooding because it does not account for direct damages to transport infrastructure and for other indirect damages such as <mark>production losses due to supply chain disruptions, or welfare implications of lost accessibility to critical services and supplies.</mark> Several other findings and conclusions emerge from our analysis. First, accessibility to jobs decreases under wet road conditions. On average, the share of jobs that can be reached within one hour will decrease by 0.1% for the walking network, 10.9% for the driving network, and 5.0% for the public transit network. Combined with visualizations of the spatial distribution of loss of job accessibility at TAZ level for three networks (Figure 14), the government as well as planning and design agencies can target specific areas and regions for road fortification and better integration of multiple transport modes to minimize the loss. Second, we observe heterogeneity in travel time impact pattern from floods from both spatial and socio-economical perspectives. In some areas within the Kinshasa city (e.g. Kimbanseke), the income level of its residents is relatively low whereas the travel delay they experience during a 10-year flood event (based on our simulation) is relatively high. The exact opposite pattern can be observed in areas such as Centre Ville, Lingwala, Barumbu, etc. This pattern raises socio-spatial equity issues which are important for public policy considerations. Similar disparities can also be observed for other flood return periods with more extreme values due to travel delays for lower-income groups. These results provide valuable guidelines for planning efforts to target disadvantaged groups and communities for physical and financial aid support. Third, our modified road segment criticality analysis sheds light on the prioritization of road interventions to increase transportation resilience to floods. City officials and transportation planners in Kinshasa aiming to climateproof the city’s transport network face difficult choices in where to start. These results provide some

> 18 The total cost is an estimation of the economic cost to commuters, associated with one day of flooded road conditions in the 10-year flood scenario.

24
guidance as to which road segments to invest first based on their importance in maintaining decent travel times for many commuters under wet conditions.

Finally, we hope to point out that given climatic uncertainties in the future, the frequency and magnitude of floods in Kinshasa and other regions around the world will likely increase. The 10-year flood scenario has the lowest exposure level among all five flood return periods that we examined in this study. As climate change impacts unfold over time, the potential impact of floods on transportation is likely to increase dramatically. It is possible to imagine that the economic cost we report for the current 50-year flood event, for example, will occur more frequently in the future, thereby increasing the returns from resilient transport infrastructure planning and design in Kinshasa.

A limited number of uncertainty areas deserve some discussion. First, because of the difficulty of surveying the entire public transport network during the narrow window when floods are still affecting travel conditions, only a share (approximately 25%) of routes were mapped under wet conditions. While we implemented strategies to overcome this data incompleteness and were able to draw valuable and reasonable insights from the sampled data, future studies could provide a more complete picture of public transport during heavy rain episodes in developing country cities and could help establish causal chains between floods, vehicle frequencies, rerouting and decreased travel speeds more clearly. Ongoing work on the city of Kigali, Rwanda, which has a smaller population, urban footprint and public transport network, will include a survey of the entire transit system in both dry and wet conditions, and will provide a more complete and precise picture of public transport disruptions from floods. Second, in our study, we used mosaics of the maximum value of each raster pixel from fluvial and pluvial flood map products to approximate flood depth and extent induced by the combination of both types of floods. Although this provides a useful estimate that captures the extremes of both types of floods, such operation lacks considerations of the hydrodynamics of flood processes and may introduce error and uncertainties in flood depth and extent estimations.

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