metadata: authors: 0: author: Marcelo Gordillo,Darwin
1: author: House,Ruth Schuyler
2: author: Raina,Aditi
count: Japan
docna: 0: docna: Incorporating Resilience in Infrastructure
            Prioritization : Application to the Road Transport Sector
docty: Policy Research Working Paper
subtopic: Natural Disasters,Transport Services,Roads and Highways Performance
teratopic: Environment,Transport
abstracts: cdata!: Disruption of infrastructure services
            can cause significant social and economic losses,
            particularly in the event of a natural disaster. The World
            Bank Group and the Government of Japan established the
            Quality Infrastructure Investment Partnership to focus
            attention on the quality dimensions of infrastructure in
            developing countries, with a focus on promoting disaster
            resilience. Moreover, to support infrastructure investment
            decision making for sustainable and resilient development,
            the World Bank and Kyoto University have operationalized key
            resilience concepts at the project level and developed
            quantitative indicators capturing key aspects of
            infrastructure resilience related to the road transport
            sector. These indicators estimate resilience, expressed as
            functionality loss and recovery time across four dimensions:
            travel time, economic benefit, provision of life-saving
            services, and provision of relief goods. The paper applies
            indicator calculations to three case studies of proposed
            bypass roads in Japan and provides an example comparison of
            calculated indicators across the three projects for each
            resilience dimension. Further piloting of the approach will
            help refine the indicators, test their relative utility in
            decision making, and offer a better understanding of the
            data and analytical demands.
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repnme: repnme: Incorporating Resilience in Infrastructure
            Prioritization : Application to the Road Transport Sector
display_title: Incorporating Resilience in
            Infrastructure Prioritization : Application to the Road
            Transport Sector
listing_relative_url: /research/2018/09/30419039/incorporating-resilience-infrastructure-prioritization-application-road-transport-sector
topicv3: Transport m1326661 135,Earthquake m1503308 3154,Flood m1503315 3216,Tsunami m1503625 3160,Traffic Volume m1505460 4213
publishtoextweb_dt: 2018-09-13T00:00:00Z
historic_topic: Environment,Transport
no_of_pages: 39
author_keys: 0: author_key: 937656
1: author_key: 1365991
2: author_key: 1555573
model_extractions: WPS8584 Policy Research Working Paper 8584 Incorporating Resilience in Infrastructure Prioritization Application to the Road Transport Sector _Darwin Marcelo Schuyler House Aditi Raina_ Infrastructure, PPPs & Guarantees Global Practice September 2018
Policy Research Working Paper 8584

# **Abstract**

Disruption of infrastructure services can cause significant social and economic losses, particularly in the event of a natural disaster. The World Bank Group and the Government of Japan established the Quality Infrastructure Investment Partnership to focus attention on the quality dimensions of infrastructure in developing countries, with a focus on promoting disaster resilience. Moreover, to support infrastructure investment decision making for sustainable and resilient development, the World Bank and Kyoto University have operationalized key resilience concepts at the project level and developed quantitative indicators capturing key aspects of infrastructure resilience related to the road transport sector. These indicators estimate resilience, expressed as functionality loss and recovery time across four dimensions: travel time, economic benefit, provision of life-saving services, and provision of relief goods. The paper applies indicator calculations to three case studies of proposed bypass roads in Japan and provides an example comparison of calculated indicators across the three projects for each resilience dimension. Further piloting of the approach will help refine the indicators, test their relative utility in decision making, and offer a better understanding of the data and analytical demands.

This paper is a product of the Infrastructure, PPPs & Guarantees Global Practice. 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/research. The authors may be contacted at dmarcelo@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
# Incorporating Resilience in Infrastructure Prioritization: Application to the Road Transport Sector

Darwin Marcelo, Schuyler House and Aditi Raina Keywords : Infrastructure resilience, infrastructure prioritization, climate resilience, transport, resilient roads, quality infrastructure JEL Classification Codes : R42, H54, O18, O21

## Acknowledgments

This paper is the result of joint work by Kyoto University and the World Bank. The Kyoto University team includes Associate Professor Masamitsu Onishi and Professor Hirokazu Tatano from the Disaster Prevention Research Institute, and the World Bank Team includes Darwin Marcelo, Schuyler House and Aditi Raina. Special gratitude to Naho Shibuya, James Newman, and Satoshi Imura from the World Bank Disaster Risk Management group for their support and to the Ministry of Land, Infrastructure, Transport, and Tourism (MLIT) that kindly shared data and inputs for the case studies. The paper benefited from World Bank peer reviewers, including Pierre Graftieaux, Julie Rozenberg, Jun Erik Rentschler, and Sanae Sasamori. The team is grateful for support from Cledan Mandri-Perrot, Jordan Schwartz, Luis Tineo, Haruko Nakamatsu, and Jolanta Kryspin-Watson. The World Bank’s Infrastructure, Public-Private Partnerships, and Guarantees Group (IPG) led this work with support from the Japan-World Bank Program for Mainstreaming Disaster Risk Management in Developing Countries through the Tokyo DRM Hub.
# Introduction

Modern society depends extensively on physical infrastructure systems, which serve as the foundation for a wide range of human activities. Given the nature of infrastructure as a critical input to trade and industry, economic productivity, public health, human safety, and quality of life, the disruption of infrastructure services can cause catastrophic economic losses and devastating impacts on health and the operability of the regions they serve. Therefore, the World Bank Group and the Government on Japan established the Quality Infrastructure Investment (QII) Partnership with the objective of raising awareness and promoting enhanced attention to the quality dimensions of infrastructure investment projects in developing countries. They identify five aspects that must form the key elements in project design, namely: economic efficiency, safety, environmental and social sustainability, economic and social contribution and resilience against natural disasters.

Due to widespread gaps in infrastructure funding and increasing demands for improved infrastructure services, most countries worldwide face the challenge of having to select from among proposed infrastructure projects to develop and fund those most apt to attain developmental goals. To support project prioritization in infrastructure sectors such as transport, water, and energy, the World Bank Infrastructure, PPPs, and Guarantees Group (IPG) developed the Infrastructure Prioritization Framework (IPF) (Marcelo, et al, 2015). The IPF aggregates underlying project-level indicators into two composite indices, the socioenvironmental index (SEI) and the financial-economic index (FEI), which characterize the expected relative outcomes of proposed projects within a sector.

Although QII principles recognize resilience as a key aspect of infrastructure project design and planning, the composite indicators considered by the IPF have, until now, only assumed an 'ordinary state' of non-disruption, meaning that they do not consider how infrastructure assets maintain operability and/or recover in the face of disaster events. Moreover, the IPF analyses have not yet considered the potential benefits of infrastructure projects regarding their abilities to contribute to overall resilience at the local, regional, or national levels. The aftermath of the 2011 Great East Japan Earthquake that unleashed a tsunami and left some 2
20,000-people dead or missing<sup>1</sup> in Japan, underscores the importance of considering resilience to disaster in infrastructure investment decisions. The disaster heavily affected Sendai, the capital city of Miyagi Prefecture and a regional economic hub. The tsunami completely submerged the city's primary wastewater treatment, while some 500,000 residents lost access to water. Also, the tsunami damaged 325 kilometers of coastal railway assets and flooded about 100 kilometers of the national highway in the Tohoku region, leaving devastated towns in need of assistance without inland transport access.

This paper presents a practical approach to bring the concept of infrastructure resilience to the project level, with direct application to transport infrastructure. The proposed approach complements the IPF and offers additional indicators as potential inputs to the framework that can help decision makers discern between projects from a resilience perspective. The paper begins with a discussion of the concept of infrastructure resilience and follows with proposed indicators to measure aspects of resilience in transport at the project level.

# Applying the Concept of Resilience to Infrastructure Projects

The concept of _resilience_ has been discussed in various academic fields and conceptualized in numerous ways (Cutter _et al._ , 2010). For social sciences, the main interest is the resilience of communities, whereas for engineering fields, the focus is on the resilience and robustness of structures. Organizational resilience, conversely, focuses on the ability of an organization to absorb shocks and to adapt in a changing environment (ISO 22316). In most fields, the concept is recognized to be multifaceted. Bruneau (2003) conceptualizes resilience as the ability of a system to reduce the chances of a shock, to absorb a shock capable of causing abrupt reduction of performance, and to recover quickly after the occurrence of a shock.

In line with these definitions, we present an application of the resilience concept that describes (a) an asset's ability to withstand shocks in such a way that minimizes functionality losses (i.e., robustness or resistance), and (b) the asset's capacity to recover functionality across multiple dimensions following a disaster event (i.e., recoverability). Dealing with the expected

> 1 World Bank. 2014. Learning from Megadisasters: Lessons from the Great East Japan Earthquake. Washington, DC: World Bank. https://openknowledge.worldbank.org/handle/10986/18864

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functionality of an asset within a transportation system, both pre- and post-shock, inherently considers the asset's connection to a broader network. A network approach to transportation is crucial because transport functionality depends not only on the characteristics of individual assets but also on their contributions to the system's overall functionality and performance. That said, while an asset's functionality naturally relates to its locus within a system, the focus here is to encompass (a) the ability to minimize loss of asset functionality (within the system) and (b) the ability to recover asset functionality after a disruption.

Figure 1 illustrates the "recovering of functionality" process of an asset after a disruption (i.e. natural disaster). In the event of a disaster, an infrastructure asset (e.g., a road or bridge) is susceptible to losing some or all of its functionality. We define the decrease in functionality as _loss of functionality (_ LoF _)_ . After the disruption, functionality will typically recover, reaching a near-total or total pre-disaster level of performance over time. We term this lapse _time for recovery (_ TfR _)._ The recovery time depends on the systems and processes in place, as well as physical attributes of the asset itself. Therefore, the _accumulated loss of functionality_ ( ALF ) will account for the aggregated functionality loss over the recovery time. Mathematically, ALF is equal to the integral of the LoF function over the interval between the disruption and the end of the TfR period _._ This integral is the area indicated by the shaded area in Figure 1 .<sup>2</sup> 2 For the sake of simplicity, this illustration assumes that the functionality is recovered in a linear fashion. However, this may not always be the case. In some instances, perhaps the road only opens when full functionality is restored, and, in others, one lane of a many-lane highway may open first, followed gradually by others. Therefore, the ALF’s upper bound could take a variety of functional shapes depending on the path of recovery (how much functionality is restored at various stages of repair), such as a straight line (yielding a rectangular ALF area or even a staggered path). The ALF should then be calculated according to the functional shape that emerges from the specific scenario.

4
<!-- Start of picture text --> Zz<br>a<br>i=<br>°<br>cw]<br>c<br>Wwa<br>Functionality level<br>before disruption e<br>a Accumulated Loss<br>=5oO of(ALF)Functionality<br>S -<br>c<br>Z<br>6<br>a<br>an S<br>Functionality level<br>after disruption<br>F Time for Recovery’ (TrR) Time<br>Disruption<br><!-- End of picture text -->
resist even major earthquakes with the highest seismic intensities.<sup>3</sup> Consequently, an accurate assessment of the LoF requires information on (a) the kinds of disruptions that infrastructure projects are likely to face and (b) the expected functionality levels resulting from these disruptions at various levels of intensity.

# 1.2. Recoverability

_Recoverability_ refers to the ability of an asset to recover its functionality quickly. Recoverability is associated not only with physical factors, but also with social, organizational, resourcerelated, and managerial factors. Recoverability is dependent on the readiness of resources and preparedness of organizations to respond to disaster events. Recovery of functionality requires the availability of human and capital resources (e.g., machinery) as well as organizational plans and processes required to take necessary actions rapidly and effectively.

For example, the recovery of destroyed road facilities requires construction manpower and equipment in addition to established processes and institutional measures, such as agreements between the public and the private sectors to collaborate in an emergency or the establishment of a clear incident management system. A proxy measurement of recoverability is the duration required to reach a target recovery level.

# 1.3. Project-Level versus System-Level Resilience

When evaluating and measuring resilience, it becomes important to clarify the level of analysis. In other words, the 'resilience of what' matters. To apply the notion of resilience in infrastructure prioritization, it is helpful to distinguish between project-level and system-level resilience. Assessment of project-level resilience considers the functionality loss in the event of a shock and the ability of an asset to quickly recover from functionality loss. Asset functionality loss is a function of both the structural robustness of the facility and the level of exposure to a disruptive event. Recoverability depends on the availability of resources for reconstruction and the governance of emergency response management.

System-level resilience, on the other hand, refers to the region's or infrastructure network's capacity to absorb and recover from disasters. Infrastructure projects are generally elements

> 3 The Japan Meteorological Agency uses a seismic scale of zero to seven to describe the degree of shaking at any given time during an earthquake.

6
of a functional system. This is particularly true for transportation networks. A government, for example, may consider impleme <mark>nting a new road section designated as an emergency transport route instead of a similar project that serves no emergency relief function. Given the nature of road infrastructure assets as network components, the construction of a new road may also enhance resilience by increasing redundancies in the network that allow access to alternative routes and absorb increased traffic in the case of emergency.</mark> The methodology proposed in this paper is intended to estimate project-level resilience and not system-level resilience, though project functionality is inherently dependent on and related to the asset's relationship to other parts of a system. Further, applied to the transport sector, the goal is to use information that is likely to be available in project feasibility studies for proposed road infrastructure or public data sets to estimate the effect of a disruption on various aspects of functionality. Keeping in line with the nature of the Infrastructure Prioritization Framework (IPF), the analysis at this level is also designed to draw on less complex and less analytically-demanding indicators to make the approach more feasible to apply in information- and capacity-constrained environments.

# 1.4. Dimensions of Functionality

Infrastructure services attend to a wide array of interconnected purposes: they facilitate trade, allow mobility of people and goods, provide access to services, and support a host of other human activities. The relationships between assets in different infrastructure sectors are complex, and due to sectoral variations with respect to physical structures, locations, and degrees of interconnectivity, natural disasters affect infrastructure assets in different ways. The functionality (and potential loss of functionality) of an infrastructure system is difficult to measure for all sectors within a given region. As such, it is presently more meaningful to make judgments of relative resilience within a sector and for similar types of projects. This paper, therefore, focuses specifically on applying concepts of resilience to road infrastructure projects. While measurable aspects of functionality may be directly observable in a non-disrupted (normal) state, the LoF magnitude can only be estimated prior to a disruption. Moreover, the LoF magnitude after a disruption is only partially measurable by examining selected aspects of functionality. For road infrastructure, some of the most important elements of functionality include travel time, road utilization, provision of emergency services, and provision of relief 7
goods. The first two interrelated dimensions – travel time and utilization – are aspects related to economic impact, whereas the latter two dimensions – provision of emergency services and relief goods – are life-saving aspects.

When a road network faces a disruption, the loss of functionality manifests itself in many forms of economic loss. Damage may be reflected in increased transaction costs, loss of productivity, destruction of resources and wastage, and other negative economic impacts. Most apparent, however, the loss of functionality relates to increased travel times due to inoperability of affected roads. While reduced connectivity (interconnectedness of various areas) has impacts beyond its relationship with travel time, analyses largely center on the increased travel times between areas affected by a disaster event. Therefore, we focus the consideration of _economic_ impact on the key feature of increased travel time. Moreover, travel time may be used directly as a measure of functionality, or as an input to a traffic demand function.

<mark>When a natural disaster strikes, road infrastructure serves another crucial function, namely to facilitate the preservation of human life. In the aftermath of a disaster, road access is prerequisite to effective search and rescue, conveyance of injured and vulnerable citizens to emergency facilities and shelters, and the supply of relief goods, medical services, and other forms of aid. Also, connecti</mark> vity to resource depots, emergency shelters, and medical facilities is vital <mark>to support lifesaving functionality. W</mark> hile the notion of connectivity relates to travel time and other economic impacts, with respect to the preservation of human lives, the question associated with connectivity is whether affected areas are accessible within a window of time required to provide life-saving resources or evacuate the critically ill or injured population.

In addition, road infrastructure must also be structurally robust to prevent road users from harm or death associated with the collapse of road structures. While it is recognized that road assets must be made 'safe to fail' (Ahern, 2011), because the intention of this study is to provide an initial basic methodology to calculate a limited – yet essential – set of resilience indicators, other aspects of resilience such as this will remain areas for future study.

To summarize, then, this approach to analyzing infrastructure resilience focuses on four dimensions k of road functionality: travel time t , road utilization u , access for lifesaving services l , and provision of relief p . These functions serve as the basis for the set of calculated resilience indicators in road transport described in the following section.

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# Infrastructure Resilience Indicators

This section presents basic infrastructure resilience indicators to support infrastructure investment decision-making. One major concern regarding infrastructure projects is the resilience of constructed facilities, i.e., project-level resistance and robustness that help minimize loss of functionality. When applied to transport, the proposed indicators directly consider resilience at the project level through the concept of _accumulated loss of functionality_ ( ALF ). As shown in the next section, ALF conceptually relates inversely to resilience. In other words, the lower the ALF , the more 'resilient' the project is.

## 1.5. Accumulated Loss of Functionality

Consider a proposal for a road project i . Let ALF�� denote a variable that represents the _accumulated loss of functionality_ for the project associated with each of the four dimensions k mentioned above ( t =travel time, u =road utilization, s =life-saving and r =relief provision), given the occurrence of natural disaster. In this context, the accumulated loss of functionality ALF�� for project i corresponds to the dotted area in Figure 1. This calculation of the ALF follows the formula below: where LoF�� measures the loss of functionality for project i in dimension k , and TfR�� the time for recovery (time to regain pre-disruption level of functionality). Alternatively, the formula below offers a measurement of project-level resilience Res�� conceptualized as the inverse of accumulated loss of functionality:

## 1.6. Variation in Accumulated Loss of Functionality

ALF considers functionality changes before and after a disruption, for example, a land slide or a flood in the case of road transport projects. Policy makers may use this information when comparing infrastructure proposals, giving more priority to projects with lower ALF s. Policy makers may also consider the variation in ALF with and without the proposed infrastructure project. This variation in ALF would represent the project's contribution to the road 9
connection's level of resilience – a metric that can also be useful to inform selection from among a set of proposed projects.

� In this form of analysis, let ALF�� denote the _accumulated loss of functionality_ as in equation 1, but now under two possible states: with project ( j�w ) and without project ( j�wo ). From a resilience point of view, the accumulated loss of functionality _with_ project ( ALF��� ), is expected to be lower than _without_ project ( ALF���� ), especially if the project specifically aims to improve resilience. The resulting ALF��� may, however, be equal or even higher than the withoutproject ALF���� . The difference between the two results ( _with_ and _without_ project) provides a measurement of the net contribution of any given project i to the reduction on accumulated loss of functionality.

From a resilience point of view, ∆ALF�� is expected to be negative, reflecting a reduction in accumulated loss. Depending on the features of the infrastructure project, however, it may be positive or equal to zero. In the best-case scenario, a proposed project i generates zero Accumulated Loss of Functionality ( ALF��� = 0). In this scenario, there would be a 100% reduction in the existing Accumulated Loss of Functionality level. This may be the case, for example, of a road project proposal that, if implemented, guarantees to maintain its level of functionality (e.g. travel time) even after a major disruption (e.g. a magnitude 7 earthquake). In general, ∆ALF�� will be less than zero whenever ALF��� �ALF���� , since there would be an expected reduction of the existing Accumulated Loss of Functionality level caused by the proposed project. The expected reduction will result from decreases in either LoF�� or TfR�� or both. Figure 2 represents a case where, with the project, both LoF�� and TfR�� simultaneously decrease. The difference between the areas of the two shaded triangles captures ∆ALF�� .

Figure 2. Accumulated Loss of Functionality with and without project 10
<!-- Start of picture text --> =<br>“a<br>c—<br>23<br>=<br>uwa<br>Functionality before | eee eennneee nee<br>CR) cece rere reg<br>Functionality before |2 Pe { oe<br>disruption without project i<br>Functionality after | i {<br>disruption with project H i<br>Functionality after 3} H i<br>disruption without project ' H i<br>' { Recovery Time }<br>iLTiR withAproject jaReduction j<br>i Time<br>Disruption TAR without project<br><!-- End of picture text -->

w ik w ik Ww ik
<!-- Start of picture text --> a) AALFix = 0 b) AALF;, = 0 c) AALFix > 0 d) AALF;;, > 0<br>F wl; vol F wl; won F wl; woe F wl; woe<br>aaaLa<br>1. ALFZ = ALFW¥° 1. ALF}Z = ALFW¥° 1. ALF, > ALFYY° 1. ALF” > ALFI°<br>2. Higher functionality with 2. Higher functionality with 2. Higher functionality with 2. Higher functionality with<br>project before and after project before disruption, project before disruption, project before and after<br>disruption, 3. Lower TfR with project, 3. But same TfR with and disruption,<br>3.But same LoF and TfR with | 4.But same functionality with) — without project 3. Lower TfR with project<br>and without project and without project after | 4.But same functionality with, 4.But higher LoF with project<br>disruption and without project after<br>5. But higher LoF with project — disruption<br>5. But higher Lof with project<br><!-- End of picture text -->

s=0 s=1 s=0 s=l
project under hypothesized natural disaster scenarios, LoF calculations also provide the inputs to calculate _variations in_ ALF resulting from the implementation of a project. The last section before the conclusion describes this with examples.

The following sections describe measures to estimate LoF in the economic dimension, more specifically, travel time and utilization, and in the life-saving dimension, including the provision of emergency services and provision of relief goods.

# LoF Indicators for Economic Dimension

# _LoF Indicator for Travel Time_

In terms of functionality loss for road infrastructure, the simplest way to assess the economic impact resulting from a disaster event is by measuring its effect on travel time between a representative origin-destination pair. Ideally, all roads connected to the proposed link should be evaluated using a network model approach. However, given the data intensity and technical capacity limitations in developing countries, for this analysis, the origin and destination are identified as the primary towns that the link is intended to serve.

���� Let t� denote the travel time between an origin-destination (O-D) pair under a non���� disrupted state s�0 , and t� the travel time under a disrupted state s�1 . j captures two possible project implementation statuses: with project ( j�1 ) and without project ( j�0 ). ���� Transport simulation models can offer estimations for t� in each case.

For any disaster event d , the expected road network LoF resulting from a proposed road project i for the travel time dimension ( k�t ) would correspond to

# _LoF Indicator for Economic Benefit_

A more direct measure of economic impact associated with increased travel time is the effect on road utilization (road demand). According to standard transport economics theory, the consumer surplus is a key consideration when assessing the economic benefit of a road network (de Palma, Andre, et al, 2011, World Bank, 2005). Calculation of consumer surplus requires specifying a traffic demand function, i.e., the relationship between travel volumes and generalized costs, including time value of users, travel time, and tariff charges.

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x= q(t) q(t) q(t)=—-mxttc (xt —x°) (tt t°) <!-- Start of picture text --> x<br>/ x= q(t)<br>/ Disrupted state<br>-<br>t® t<br><!-- End of picture text --> ijs) <u>HO</u> ~~+ FM) @JO- IM)~~ j(1) U
���� road under a disrupted state ( s�1 ) with project status j , and t� denotes the travel time under a non-disrupted state ( s�0 ). A simpler specification of equation 6 is as follows: This approach may be appropriate if data on utilization are available or possible to estimate, as it gives a better indication of the economic losses associated with decreased road use.

# _Conceptualization of Economic Dimension LoF_

Figure 5 illustrates the concepts of LoF for travel time <mark>and utilization. Suppose there are two existing road links, Route α and Route β , between cities A and B. The construction of a new road "Route γ " is under investigation. Route α does not pass through areas prone to earthquakes and connects the two cities in a travel time of 200 minutes. By design, Route β allows to maintain a functional service level, with a reduced travel time of 100 minutes, even after an earthquake with a level-5 seismic intensity (SI), but not beyond that level. The proposed Route γ could maintain operability after a level-6 earthquake, but not a level 7.</mark> Figure 5 illustrates potential disruption scenarios, both with and without the proposed project Route γ . These include the non-disrupted state and three possible disruption scenarios: a level-5 SI earthquake, a level-6 SI earthquake, and a level-7 SI earthquake.

Each potential disaster event will have an impact on the travel times between the A-B origindestination pair. This is due to the designs and locations of the proposed Route γ and the existing routes α and β . Without Route γ , if Route β is disrupted, Route α would be the only available way to connect cities A and B. After a level-6 or level-7 earthquake, the travel time would increase to 200 minutes.

The proposed Route γ is more resistant than Route β and could operate after a level-6 earthquake, but not after a level 7 (Route β would collapse under both scenarios). If implemented, Route γ would keep the connection between the cities functional even after a level-6 earthquake.

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<!-- Start of picture text --> Scenario<br>No disruption Disruption: SI<5 (d = 1) Disruption: SI=6 (d = 2) Disruption: SI=7 (d = 3)<br>(S = 0) (S = 0) (S = 0,1) (S = 1)<br>Vj = 0) ae Au : a<br>Project | |<br>With Route # Route @ Route p Route p<br><!-- End of picture text -->

j=l d = 1|2|3)

° = 100, } =50 d=1 2=100, }=50 d=2 %%=200, }=50 d=3 %=200, }TM =200 d=2
For these reasons, we propose a deterministic approach that does not directly incorporate probabilities of <mark>disaster scenarios</mark> in the calculations of potential losses and rather assumes disaster scenarios as if they effectively occurred. If known, however, probabilities of such disaster scenarios may guide the assumptions under this approach. Moreover, if available, these probabilities may provide inputs to calculate an indexed ALF via a weighted average.

The framework can also be applied to the upgrading or improvement of an existing road section. The example in Figure 6 presents the case of a structural robustness upgrade for an existing road section. The logic is basically the same. Now suppose there are two links between cities A and B called Route α and Route β . Route α passes through an area free of earthquake risks, whereas Route β does not. <mark>A project i to s</mark> tructurally reinforce <mark>Route β</mark> is <mark>under investigation.</mark> Assume three disaster scenarios: (1) the occurrence of a level-5 earthquake ( d�1 ), (2) the occurrence of a level-6 earthquake ( d�2 ) and (3) the occurrence of a level-7 earthquake ( d�3 ). Route β can withstand an earthquake of SI less than 6, but not one of SI 6 or 7. If Route β is upgraded with project i , it could withstand an earthquake of SI less than 7 but not one of SI 7.

Figure 6 summarizes the various configurations resulting from the combination between project status and disaster scenarios . The associated travel times of the representative O-D pair for each state of network are: The travel times of the with-case and without-case are different under scenario 2 ( d�2� . The travel time between the O-D pair doubles if scenario 2 occurs in the without-project case, whereas no change in travel time would occur in the with-case since the proposed project would withstand a level-6 earthquake. Given scenario 2, then, LoF (the economic loss due to the increase of travel time) arises in the without-project case but does not in the with-project case. This LoF savings over the period of recovery would account for the project's _contribution to resilience_ .

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<!-- Start of picture text --> Scenario<br>No disruption Disruption: Sis5 (d = 1) Disruption: SI=6 (d = 2) Disruption: Sl=7 (d = 3)<br>(S = 0) (S = 0) (S = 0,1) (S= 1)<br>| | | |<br>Route a Route a Route a Route a<br>Without ed on,<br>y =0) 1,0" a 200 me 1° = 20<br>x : ae a - : mi . Route P_ — > 4 Route - , Route p 7<br>Project j T | {<br>Status Route a Route « Route a Route a<br>J =1) 7 Route ., x Re ute » “4 Route ff » : Route #<br><!-- End of picture text -->

<!-- Start of picture text -->
1.7.2.<br><!-- End of picture text -->

i(s) _ j(0) j(4) an = Pop, Popi j(0) il j(A) il i(s) il
## _LoF Indicator for the Dimension of Relief Goods_

The functionality of a road network in terms of human safety and lifesaving also includes the delivery of relief goods. With regards to the delivery of relief goods, accessibility is the key issue of functionality, as relief goods will not reach villages and municipalities isolated by the impacts of natural disasters. While LoF is a difference calculation for other aspects of functionality (i.e., the difference between the ordinary and disrupted states), LoF for the functionality of delivery of relief goods is simply defined as the isolated population under the disrupted state, since there is no (zero) isolation under the ordinary state. This calculation, therefore, is as follows: ���� where Pop�� is the population living in towns and villages that will become isolated under a specified hazard scenario s with the project status j for each project i . Again, the contribution to resilience of a project, with respect to access for provision of relief goods, can ���� be captured by utilizing the difference in LoF�� for the with- and without-project cases and the time for recovery.

# 1.8. Indicator of TfR

Time for Recovery ( TfR ) depends on the degree of damage as well as the level of emergency preparedness, including the existence of an emergency management plan and the preemptive establishment of cooperative agreements for road clearance works and ���� reconstruction between road administrators and contractors. Let TfR� denote the estimated time for recovery for a disrupted road network ( s�1 ) after a natural disaster d for each proposed project i with implementation status j .

To determine TfR , it also becomes necessary to establish the point in time at which recovery can be considered 'complete'. Reconstruction of roads to fully restore pre-event service levels may require extensive time and resources. Therefore, in the emergency period, road administrators often make disrupted roads available by provisional methods of construction that restore service, but not to full pre-event standards. This state of 'temporary recovery' is the state to which TfR applies.

19
The recovery time estimation requires assessing the state of existing assets, evaluating possible damages, and estimating the construction works capacity and availability of financial and human resources. In addition, damage assessments and other estimations can be used to calculate the workforce needed in the event of a disaster. Furthermore, the gap between the workforce needed to recover functionality and actual available personnel provides essential inputs to estimate the time for recovery. If damage assessments are not available or not possible to estimate, proxies using historical data may provide an estimate of the likely resource demands and time required for recovery. Any of these cases will require empirical data obtained through expert interviews and by examining the input requirements for recovery in similar historical cases.

The time for recovery ( TfR ) associated with the various dimensions of functionality (i.e., lifesaving, relief, and economic) may be different. For lifesaving, functionality is reestablished with a minimal level of restored access (e.g. if one lane of a highway clears for passage of emergency vehicles). However, pre-event traffic demand cannot be restored to a functional level until the disrupted road is open to the public again. The time for recovery in terms of the economic dimension of functionality is often longer than that of lifesaving and relief.

In Japan, for example, a post-tsunami road clearance plan is currently under development that specifies required recovery times. The plan focuses on highways threatened by a potential large-scale tsunami resulting from an expected Nankai Trough Earthquake with 9.0 SI. At Wakayama Prefecture, the plan requires the clearance of at least one lane of the connecting highways leading to the coastal areas from inland highways within 24 hours after a tsunami. Within 48 hours, important places (e.g., city halls) in coastal areas are to be connected with inland highways by clearing at least one lane of coastal highway. All coastal highways required to provide life-saving services, conduct search and rescue activities, and deliver emergency relief must be cleared within 72 hours. To implement this plan, having in place an inland highway and connecting highways to coastal areas is critical. Also, ensuring the provision of key power and economic resources is essential. Pre-event treaties between government and key stakeholders, especially with local contractors, are crucial to guarantee the availability of resources for recovery.

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# Japan Case Studies

## 1.9. Case 1: Construction of Bypass Road in a Flood-Prone Area

The first case project for testing the calculation of resilience indicators is a proposed construction of a new bypass road intended to save travel time and to develop an alternative route for the existing route, which is located in a flood-prone area (see Figure 7 ).

The existing route of National Highway No. 312 experienced a major flood of the Maruyama River when Typhoon No. 18 hit in October 2004. This road section is a major highway connecting two local cities in the northern Hyogo prefecture, Toyooka City and Asago City. Toyooka City is a regional base in the North Tajima region wherein a first-aid station hospital, Toyooka Hospital, is located. Some residents in the flooded area are isolated and lose access to the first-aid station hospital. Moreover, the increase in travel time between the two cities due to necessary detouring is not negligible. The proposed bypass route passes through a mountainous area with some tunnels and, hence, is not exposed to flood risk.

### Indicator Data

Table 1 shows a list of necessary data for the calculation of resilience indicators for each dimension. The historical record of flooding in 2004 is set as an expected scenario for the disrupted state of the existing route.

21
|Table 1. C<br>LF|ase 1 Resilience Indicator Data|||
|---|---|---|---|
|o<br>Indicators|Relevant Data|Data Source|Content|
||Traffic volume per min under theordinarystate (x�)|Design traffic<br>volume|13.68 pcu/min<br>(19,700 pcu/day)|
||Traffic volume per minwithoutthe project under thedisrupted<br>state (x�)|Simulation|6.84 pcu/min|
|Economic|Travel time between representative OD (Asago – Toyooka)without<br>the project under theordinarystate (t<sup>�����</sup>)|Route search<br>system|50 mins|
|Loss|Travel time between representative OD (Asago– Toyooka)withthe<br>project under theordinarystate (t<sup>����</sup>)|Route search<br>system|42 mins|
||Travel time between representative OD (Asago – Toyooka)without<br>the project under thedisruptedstate (t<sup>�����</sup>)|Route search<br>system|79 mins|
||Travel time between representative OD (Asago – Toyooka)withthe<br>project under thedisruptedstate (t<sup>����</sup>)|Route search<br>system|42 mins|
||Population of coverage area (Tajima Area) (Pop��<br>����)|Population<br>statistics|180,607|
||The percentage ratio of population who can access major hospitals<br>in 30 minuteswithoutthe project under theordinarystate|Route search<br>system|45.1%|
|Saving<br>Lives|The percentage of population who can access major hospitals in<br>30 minuteswiththe project under theordinarystate|Route search<br>system|52.2%|
||The percentage of population who can access major hospitals in<br>30 minuteswithoutthe project under thedisruptedstate|Route search<br>system|35.7%|
||The percentage of population who can access major hospitals in<br>30 minuteswiththe project under thedisruptedstate|Route search<br>system|47.3%|
|Relief|The number of isolated populationwithoutthe project under the<br>disruptedstatePop�<br>�����|0|8162|
|Goods|The number of isolated populationwiththe project under the<br>disruptedstatePop�<br>����|0|8162|

22
<!-- Start of picture text --> FIGURE 8.<br>ROAD CLEARANCE WORK PROCEDURE<br>AND BYPASS PROJECT<br>without the bypass project<br>existing road in inundated area<br>eo -—@<br>with the bypass project<br>o—-. -—®<br>Note Arrows show direction of road clearance work.<br><!-- End of picture text -->
<!-- Start of picture text -->
1.9.2.<br><!-- End of picture text -->

1.9.2.

iu ~~OT~~ wo, <u>00 +79) X (13.68 — 6.84)</u> wo =<sup>~~WEFT)~~</sup> ~~* Cobos 08)~~ = 441.18 <!-- Start of picture text --> Wo = — x LoRW? x TER?<br>= 5 x 441.18 x 1.25 = 275.74<br><!-- End of picture text --> = ALEY =0 4 = ALE? — ALF’ = 275.74 — 0 = 275.74 ”° = [180,607 x (0.451 — 0.357)] = 16,977.06 wo = ~~—~~ x LoFW? x TERY?
= ~~—~~ X 16,977.06 X 1.25 = 10,610.66 1” = 180,607 x (0.522 — 0.473) = 8,849.74

|’ = ~~-~~ x LoF” x TFR”

= ~~—~~ X 8,849.74 x 0.625 = 2,765.54

| = ALF/”° — ALF” = 10,610.66 — 2,765.54 = 7,845.12

” = LoFW? = 8,162 Wo = ~~—~~~ x Lok? x 1.25 = 5,101.25 i” = ~~—~~~ x LoRW x 0.625 = 2,550.625

> = ALF? — ALEY = 2,550.63
# 1.10. Case 2: Construction of Bypass Road in a Tsunami-Prone Area

The second case project is another bypass road intended to reduce travel time and provide an alternative route to avoid a tsunami-prone area (see Figure 9 ). The existing National Highway No. 42 runs through the nearby coastal area along the Pacific Ocean, which is characterized by steep slopes and serves as the only primary route available to connect cities along the coastal area. If the existing highway is disrupted, there is no alternative route available for the coastal cities it serves. This road section is exposed to the high possibility of a tsunami caused by the Nankai Trough Earthquake which is expected to occur in the next few decades. The proposed bypass runs outside the tsunami-prone area, and, therefore, works as the alternative route during disruption of the existing national highway. Most residents live along the existing national highway and would lose access to the major hospital if the road is obstructed. Therefore, even with the bypass, the population suffering due to the tsunami would remain the same. However, the critical impact of the bypass would be on the recovery time since it allows for additional points to start road clearance work.

## Indicator Data

Table 2 lists the data in Case 2. The economic loss calculations required knowing the traffic volume of the ordinary and disrupted states between the representative O-D pair (KamitondaKushimoto). The existing national highway is the only primary route connecting towns in the coastal area. If the existing highway is disrupted, the only available detour is a secondary route including narrow roads in the mountainous area. While it usually takes 60 minutes between Kamitonda and Kushimoto under the ordinary state, the disruption of the highway would result in a 119-minute travel time. The bypass consists mostly of tunnels and runs through areas away from the coast and hence, is free from the risk of a tsunami. The travel time between 26
Kamitonda and Kushimoto using the bypass is 49 minutes. The traffic volume between Kamitonda and Kushimoto is assumed to be the design traffic volume in the ordinary state, whereas the volume in the disrupted state is assumed to be zero, based on expert opinion. The expected time for recovery in the case of a disruption of the existing route is 255 hours (10.625 days), based on the road clearance plan for a tsunami disaster prepared by MLIT.

|Table 2. C<br>Indicators|ase 2 Resilience Indicator Data|||
|---|---|---|---|
|of LoF|Relevant Data|Data source|Content|
||Traffic volume per min under theordinarystate (x�)|Design traffic<br>Volume|5.14 pcu/min<br>(7,400 pcu/day)|
||Traffic volume per minwithoutthe project under thedisruptedstate (x�)||0 pcu/min|
|Ei|Travel time between representative OD (Kamitonda – Kushimoto)without<br>the project under theordinarystate (t<sup>�����</sup>)|Route search<br>system|60 mins|
|conomc<br>Loss|Travel time between representative OD (Kamitonda – Kushimoto)withthe<br>project under theordinarystate (t<sup>����</sup>)|Route search<br>system|49 mins|
||Travel time between representative OD (Kamitonda – Kushimoto)without<br>the project under thedisruptedstate (t<sup>�����</sup>)|Route search<br>system|119 mins|
||Travel time between representative OD (Kamitonda – Kushimoto)withthe<br>project under thedisruptedstate (t<sup>����</sup>)|Route search<br>system|49 mins|
||Population of coverage area (Kinan Area) (Pop<sup>����</sup>)|Population<br>statistics|243,025|
||The percentage of population who can access major hospitals in 30<br>minuteswithoutthe project under theordinarystate|Route search<br>system|78.1%|
|Saving<br>Lives|The percentage of population who can access major hospitals in 30<br>minuteswiththe project under theordinarystate|Route search<br>system|78.5%|
||The percentage of population who can access major hospitals in 30<br>minuteswithoutthe project under thedisruptedstate|Route search<br>system|52.8%|
||The percentage of population who can access major hospitals in 30<br>minuteswiththe project under thedisruptedstate|Route search<br>system|52.8%|
|Relief|The number of isolated populationwithoutthe project (Pop�<br>�����)|Population<br>of|61,571|

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<!-- Start of picture text -->
1.10.2.<br><!-- End of picture text -->

wo ~~(60 +199) x (5.14 -0)~~ = 4,60.03 wo = ~~—~~ x LoRW® x TERY?

= ~~—~~ x 460.03 x 10.625 = 2,443.91 = ALE =0 ,=ALF —ALF = 2,443.91 ”° = [243205 x (0.781 — 0.528)] = 61,530.87 ? = ~~—~~ x LoFW® x TERY? = ~~—~~ x 61,530.87 x 10.625 = 326,882.72
” = [24,3205 x (0.785 — 0.528)] = 62,503.69 w” = ~~-x~~ LoF” x TfRY = ~~—~~ X 62,503.69 X 5.25 = 164,072.17 1 = ALF)” — ALF!” = 162,810.55 7° = LoRY = 61571 = ~~—~~ xX 61571 X 10.625 = 327095.94 Ww = ~~-~~ x LoBY x TfRY = ~~—~~ X 61571 X 5.25 = 161631.75 r= ALEW? — ALE” = 165464.19
expected to be heavier as compared to a snowstorm in terms of recovery time required. The national highway is the major corridor in this area, though a detour route is still available. The proposed bypass tunnel would run through the landslide-prone area and allow for this road section to remain free from the risk of landslides.

# Indicator Data

Table 3 shows the necessary data for the calculation of resilience indicators for each dimension in Case 3. As with the above cases, the traffic volume in the ordinary state is taken to be the design traffic volume. The travel time is calculated by a route search system like Google Maps. Since alternate routes to National Highway No. 9 exist, a disruption to the road section causes only a 4-minute increase in travel time between Toyooka and Tottori. Using a route search system, areas accessible to the major hospital can be determined and, hence, the population with access can be calculated by drawing on demographic statistics. Because the disrupted section is just a single point, none of the population is isolated, even in the disrupted state.

The expected time for recovery depends on the type of hazard event. A landslide event requires 2 days for recovery, on average.

Table 3. Case 3 Resilience Indicator Data

|Indicators of<br>LoF|Relevant Data|Data source|Content|
|---|---|---|---|
||Traffic volume per min under theordinarystate (x�)|Design traffic<br>volume|6.11 pcu/min<br>(8800 pcu/day)|
|Ei|Traffic volume per min under thedisruptedstate (x�)|Simulation|3.06 pcu/min|
|conomc<br>Loss|Travel time between representative OD (Asago – Tottori)<br>withoutthe project under theordinarystate (t<sup>�����</sup>)|Route search<br>system|109 mins|
||Travel time between representative OD (Asago – Tottori)with<br>the project under theordinarystate (t<sup>����</sup>)|Route search<br>system|109 mins|

30
# ~~Se~~ ~~<u>| Pf ==</u>~~

<!-- Start of picture text -->
1.11.1,<br><!-- End of picture text -->

1.11.1,

= 338.55 wv? ~~=—~~ x (LoRW? x TERY) = ~~—~~ X 338.55 x 2 = 338.55 y = ALF’ =0
∆ALF� �ALFwou �ALFwu � 338.55 Likewise, LoF for the dimension of saving lives and for the dimension of providing relief goods can be calculated as follows: because the ratio of population who can access the major hospital is same in the ordinary and disrupted state, ∆ALF� �0. And finally, because the flood scenario does not cause any isolation of villages, ∆ALF� �0 .

# 1.12. Comparing Project Resilience

The calculations in the above cases can be used to compare the projects' variations in accumulated loss of functionality ( Table 4 ) by analyzing the projects' contribution to both, the percentage of functionality losses reduced in the three dimensions, and the absolute amount of ALF reduced. Because the above three cases are specifically intended to mitigate exposure to particular hazards, they are designed to eliminate functionality losses, thus making their respective LoF<sup>�</sup> and ALF<sup>�</sup> calculations equal to zero.

Table 4. Comparing loss reductions due to bypass projects

|Case Project / Dimension|Case 1|Case 2|Case 3|
|---|---|---|---|
|∆ALF�<br>�|100% (275.7)|100% (2,443.91)|100% (338.55)|
|∆ALF�<br>�|73.94% (7,845.12)|49.81% (16,2810.5)|-|
|∆ALF�<br>�|50% (2,550.625)|50.59% (165,464.2)|-|

_Note: Percentage reduction is presented with the_ ∆ALF _in parenthesis_ Table 4 suggests that the second project will make the greatest contributions to reducing loss in the event of an extreme event. This is because, first, even though all three projects reduce functionality losses in terms of utilization by 100%, the bypass in Case 2 would impact a greater amount of traffic volume. Second, while the functionality loss for saving lives is reduced only by 50% in Case 2 as compared to 74% in Case 1, again, the absolute number of people whose access to lifesaving services is preserved is greater. Similarly, for the last dimension, even though the percentage loss is the same between the first two cases, the absolute number of people saved from potential isolation is greater in the second. In other words, the Case 2 project makes the greatest contribution to resilience among the three proposed bypass projects in absolute terms.

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# 1.13. Sensitivity Analysis

In this section, we test the results of the analysis by varying some of the underlying assumptions to ensure that results are not highly sensitive to minor variations. To do so, the saving lives indicators were re-estimated. The assumption underpinning the calculations changed, from the percentage of the population that can access a major hospital within 30 minutes (as in the original calculation), to the percentage that can access emergency care within 60 minutes.

Table 5: Assessing ∆ALFwl changing assumption of access to lifesaving services within 60 minutes

|Dimension - Saving Lives (60 mins)|Case 1|Case 2|Case 3|
|---|---|---|---|
|The percentage of population who can access major hospitals<br>in 60 minuteswithoutthe project under theordinarystate|90.20%|99.50%|90.20%|
|The percentage of population who can access major hospitals<br>in 60 minuteswiththe project under theordinarystate|93.60%|99.50%|90.20%|
|The percentage of population who can access major hospitals<br>in 60 minuteswithoutthe project under thedisruptedstate|79.80%|74.10%|90.20%|
|The percentage of population who can access major hospitals<br>in 60 minuteswiththe project under thedisruptedstate|89.00%|74.10%|90.20%|
|∆ALFl<br>w|77.9%<br>(9,143.23)|50%<br>(33,2035.62)|-|

Comparing the results in Tables 4 and 5, we find that there is a negligible variation in the results even when the time assumption is doubled. This suggests that the results of the analysis are sufficiently robust for project comparison.

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# Discussion and Conclusion

The proposed indicators offer a starting point for calculating and applying resilience indicators to support project selection. Moreover, the World Bank's Infrastructure Prioritization Framework (IPF) can use the resilience measurements presented in this paper in two ways. First, if the data to calculate the proposed resilience estimates are available or acquirable at the project level, they can be directly inputted into the IPF as additional criteria in the socialenvironmental index (SEI). In most countries, however, the required data are likely to be sparse. In that case, the aim should be to measure infrastructure resilience for only those projects that emerge as "high priority" from the prioritization analysis and use resilience indicators as complementary information to support final project selection, but not as a part of the SEI.

While this approach is not intended to 'solve' problems of resilience or determine sector-wide or regional strategies for mitigating disaster, the proposed indicators introduce considerations of resilience that can guide infrastructure investment decisions. Moreover, the indicators offer two potential approaches to consider resilience with respect to infrastructure investments. On the one hand, the proposed transport asset's expected Accumulated Loss of Functionality ( ALF ) may be considered in isolation to better understand the 'resilience' of road projects. In this case, the question at hand is how much functionality various proposed projects will lose in the event of a disaster. On the other hand, if projects are pursued specifically to improve a region's overall resilience, a more important metric would be an ALF variation ( ∆ALF ) that measures a project’s impact on the overall road system.

The proposed indicators also address four interrelated dimensions of resilience: travel time, utilization, provision of emergency services, and provision of relief goods and services. What measures are included in resilience calculations and what approach to measurement is taken ( ALF or ∆ALF ) will depend on the policy goals associated with the proposed projects, as well as the information available to inform considerations of functionality and resilience.

Piloting application of these approaches in real-life contexts will help further develop the indicators themselves and improve their application to infrastructure decision-making. Also, piloting these indicators will lead to a better understanding of the different approaches' and dimensions' relative usefulness for decision-making. Also, the results should be subject to 34
expert review and compared to alternative (possibly qualitative) approaches to assessing the relative 'resilience' of proposed projects. In addition, since there are likely to be uncertainties related to data used for estimating functionality as well as the assumptions employed, it is essential to test the results with a range of specifications during the piloting of this work. This would not only test the sensitivity of the results but also their robustness.

Piloting should also aim to develop guidance for applying the ∆ALF indicator where investments follow the specific policy goal of improving system or regional resilience. Further guidance should be developed to understand the prevalence of and develop approaches to deal with the special cases described in section 1.6, wherein projects may improve functionality in the ordinary state but make relatively lower or no improvements to post-hazard functionality. In these situations, there may be calculated increases to ALF due to the relatively higher losses of functionality due to overall gains in functionality. A potential option to deal with this, which should be tested in piloting, is to compare estimated post-hazard functionality _with_ a proposed project to the estimated post-hazard functionality _without_ the project.

Further, piloting will also lead to a better understanding of the informational and analytical demands associated with estimating loss of functionality and time for recovery. Since these resilience indicators are intended to support infrastructure decision-making under various informational conditions, it is important that the input data required to calculate resilience indicators be reasonably accessible. Piloting should focus on improving approaches to estimating time for recovery.

35