transport network vulnerability assessment methodology, based on the cost-distance method and gis...

8/12/2019 Transport Network Vulnerability Assessment Methodology, Based on the Cost-distance Method and GIS Integration

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Transport Network Vulnerability

Assessment Methodology, Based

on the Cost-Distance Methodand GIS Integration

Dragos Toma-Danila

Abstract Considering the various effects of natural disasters, and the need for a

fast intervention and recovery time, before facing the associated problems it is

needed to mitigate the risks. A basic and initial step is to assess the vulnerability in

high risk areas. The importance of a transport network is major, whether it is a

road, railway (for access) or pipe (for resources) network. Various methods were

described for analyzing their behavior to disastrous events (like earthquakes,

landslides, flooding). The methodology proposed in this study integrates all related

input data within a GIS software, adding by so the spatial dimension, and adapt the

cost-distance method to obtain fictive costs that translate into vulnerability statesfor each point of a network. Also, the hot-points that can determine detour costs

are taken into consideration, by means of random What if? scenarios that are

generated by an automation model. The fact that the cost-distance method requires

origins to which the costs will refer it is important, because the vulnerability values

will also be related to how hard it is for an emergency intervention team to reach a

certain segment of the network. Because of the various degrees of freedom in the

methodology, different methods can be also added to the actual core, in order to

serve the purpose, whether it is emergency route analysis, road planning or loss

estimation assessment. In order to test and exemplify the methodology and theresults, a road network seismic vulnerability assessment example is presented, for

a Romanian County right on top of the Vrancea Seismic Area. Specific details are

given about the possibilities to implement the methodology.

Keywords Network analysisCost-distance methodVulnerability assessmentGIS

D. Toma-Danila (&)

National Institute for Earth Physics, Magurele, Ilfov, Romania

e-mail: [email protected]

S. Zlatanova et al. (eds.), Intelligent Systems for Crisis Management, Lecture Notes

in Geoinformation and Cartography, DOI: 10.1007/978-3-642-33218-0_15,

Springer-Verlag Berlin Heidelberg 2013

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1 Introduction

Each country in the world is subject to different natural disasters, which pose

various risks to the population and economy. The extent of the damage (both interms of space and effects) determines the scale of the recovery actions to follow.

But one of the most basic initial steps is to have access in the affected area. Upon

this depend the quick real assessment of the situation, the salvation of people

requiring medical care or trapped under debris, the delivery of provisions and other

important actions. The road and railway networks need to be operative as quickly

as it can, making also proper links with the airports and harbors. Following a big

natural disaster, the traffic flow to the affected region also greatly increases, so the

support to sustain it must be assured.

As it can be seen, no matter of the disaster, the transportation network is the

fundamental base for interventions. By assessing its vulnerabilities, early planning

can be made in order to eliminate the threat of disrupting the important links

within the network. The planning can consider numerous aspects, like the creation

of alternative routes, strengthening the vital corridors, bridges or tunnels safety,

reconsidering the efficiency of the emergency intervention centers and medical

resources in the territory etc. The methodology proposed in this study will refer to

the vulnerability assessment, with an example for the seismic risk.

In order to evaluate the vulnerability of a transport network, various methods

have been previously developed [6]. According to Pinto et al. [7], three types of

studies that asses the seismic performance of transportation networks can bedepicted, based on the level they are referring to:

Level I studies: the attention is focused on the functioning of the network in

terms of pure connectivityuseful for rescue function right after the earthquake

Level II studies: add the consideration of the network capacity to accommodate

traffic flows

Level III studies, that try to give a more realistic general picture, by combining

direct physical damage estimates with various economic models.

The purpose of the methodology presented here might be considered as a level Istudy, but nevertheless the possibility of adding knowledge based on more com-

plex procedures and on an economical approach can turn it into a more advanced

study. Practically, it can adapt to the requirements of the user, keeping just the

cost-distance method as a core. The cost-distance method is specific for evaluating

the economic travel costs in a territory, but this cost can be considered fictive,

showing how hard it is to reach a point from an origin, by accumulating vulner-

ability values along the way. The idea of using the Cost-Distance Method in

various fields of study like ecology [2] or road planning [1] was already consid-

ered, providing proper results.With the proposed methodology, performed with relevant and well understood

input data, answers to several important questions can be obtained:

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How efficient is the transport network distributed, as support for prompt inter-

ventions in risk areas?

What are the vulnerable areas (of the network) and what impact can have the

isolation of them?

How well are the emergency intervention centers distributed along the network?How long can it take to intervene, giving the vulnerabilities of the road?

What are the safest and reliable routes?

What segments are vital for the access in some isolated areas?

How many people can be affected by the failure to provide quick reaction

measures?

2 About the Methodology

The fundamental idea behind the proposed methodology was to create a

completely GIS integrated complex tool, allowing different but not fixed inte-

gration of own data and procedures from different fields, in order to assess the

vulnerability of a network. This network might be of roads, railways, pipes or any

other network with a spatial extent. What weve tried was to involve more of the

geographic component of the elements (link them in a singular system) and focus

on the relations within the whole network. Studying the capabilities of the Spatial

Analyst Toolbox in the ESRI ArcInfo software, especially the Cost-Distance

Method, we observed that by setting the right input (create a cost vulnerability

raster just for the network) you can obtain a map showing how potentially difficult

is to get from an origin to any connected place, therefore if a crossed cell is

vulnerable, the next cell will also be vulnerable, if there is no safer way to get to it.

As the ArcMap help on cost functions explains, the cost values assigned to

each cell are per-unit distance measures for the cell. If the cell size is expressed in

meters, the cost assigned to the cell is the cost necessary to travel 1 m within the

cell. If the resolution is 50 m, the total cost to travel either horizontally or verti-cally through the cell would be the cost assigned to the cell times the resolution

(total cost = cost * 50).

In selecting the GIS software suited for the implementation of the methodology

that uses not only the cost-distance analysis but also spatial editing, raster analysis

or automation of processes (as seen in Fig. 1), ESRI ArcGIS software, as a leading

and worldwide used solution, with easy to use but complex features, was the

proper solution and environment.

As it can be observed in Fig. 1, the first mandatory element for the assessment

of the network vulnerability is the network definition. This can be done in a vectoror raster format directly. Important is that after the conversion into raster, con-

nected segments are linked, the cell size is small enough and the blank spaces near

the network get the No Data raster value. In order to fix problems that occur

Transport Network Vulnerability Assessment Methodology 201


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when the polyline is converted into raster, a good practice is to generate buffers

around them, initially.

Another mandatory input data is the definition of Emergency Intervention

Origins. In order to compute the vulnerability with the Cost-Distance Method, it is

needed to know points to refer to. If a city has an Emergency Intervention Center

for natural disasters, or a hospital for example, then the vulnerability of the net-

work in this city is the smallest. There is no limit in defining these origins, but theyhave to be placed on top of the network cells with data. The provided data doesnt

necessarily have to be realfor planning purposes, proposed points can be created

and the vulnerability differences can be noticed.

One of the important steps that involve the operators understanding is the

definition of elements that can influence the network vulnerability. Each one has to

decide what data to use, what the implications to the network are or what is more

hazardous. In the end, the methodology requires just a cost raster, with the similar

extent as the network raster. But getting to it, several steps need to be carried out.

Initially, each set of data must be converted into raster. Then, depending on theimportance of the numeric intervals or text description, new risk values are

assignedin general, based on a similar scale. This similar scale will reflect when

all the impact rasters are compiled together, and a certain weight factor cumulating

Fig. 1 The general plan of the proposed methodology

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100 % is attributed. For the case study bellow, a model of how the reclassification

values are assigned is given and also proposed. In general, natural disasters

influence the landscape, so data sets like the slope or the land use, which can be

obtained for free from SRTM or CORINE data sets, can be added to the weighted

overlay compilation.After obtaining the cost raster, the first Cost-Distance Analysis can be

performedfor a perfectly linked network situation. During a natural disaster,

several points are more vulnerable than others. If these points will block the

network, how will the joined segments be affected? To answer this question, the

next step of the methodology involves a What if? approach, that tries to assess

the vulnerability further, by simulating a large and random number of situations.

The automated analysis created (with the aid of ArcGIS Model Builder) requires

the definition of the hot spots, which can be made based upon the cost raster

created earlier, or on specific knowledge. This time, data has to be in a vectorformatpolygons that cut the segments perpendicular. Each simulation deter-

mines what points are considered to block the network, then changes values from

the initial cost raster into no data, and then applies the cost-distance method.

After many simulations, from worst case scenario to few blockages scenario, all

distance rasters can be merged as mean, into a final output of the network vul-

nerability, called also distance raster.

The output, together with additional data, will hopefully be able to answer

realistically to the questions enumerated in the introduction. By adding a popu-

lation layer and reclassifying the distance raster values, estimations about thepossible number of isolated people after a natural disaster can be made. Also, the

ArcGIS Spatial Analyst Toolbox offers the possibility to perform a Cost Path

Method that will return the best (safest) path between any point in the network and

the costly nearest origin. There are many possibilities, for different purposes. In the

case study we tried to provide a good example of the methodologys capabilities.

3 Case Study: Vrancea County Road Network Seismic

Vulnerability Assessment

3.1 Main Characteristics of the Analyzed Area

In order to exemplify and test the proposed methodology, a representative study

area was selectedthe Vrancea County. Located in Romania, this county is under

a constant seismic risk generated by the Vrancea active seismic area. This area is

located in the curvature of the Carpathian Mountains, at the contact between the

East European plate and the Intra-Alpine and Moesic subplates, and it has been inthe Twentieth century the cause of 32 intermediate depth earthquakes with

Mw C 6, including devastating events like the one 10 Nov 1940, with Mw 7.7 and

h = 150 km, and the one on 3 Mar 1977, with Mw 7.4 and h = 94 km (Fig.2).



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These particular events highlighted major problems near but also very far from

the epicenters (up to 300 km). In the Vrancea County, more than 90 % of Panciu

city was destroyed during the 1940 event, a lot of buildings in Focsani City and

different villages collapsed, due to the poor quality of constructions but also to

landslides, liquefaction and post-earthquake phenomenon (Figs.3 and 4). The

1940 and 1977 events also severely affected Bucharest capital city, which is

&150 km away from the epicentral area; over 33 buildings and flats collapsed in

Fig. 2 The localization of the Vrancea County and the earthquake epicenters in the Romplus

Catalogue [5]

Fig. 3 Slumping and lateral

spreading of the road, in

Balintesti, near the Vrancea

County, after the 1940

earthquake [4]

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3.2 Performing the Cost-Distance Spatial Analysis

As the first part of the article highlighted, a few mandatory input data files are

required, in order to perform a basic Cost-Distance Analysis. The selection below

is meant to show how free data converted into GIS can be used, and how important

is the association of proper weight factors. The implementation must be considered

Fig. 5 Map showing

the population distribution

and the emergency

intervention centers in case

of a natural disaster

Fig. 6 General map of the

county, showing the relief,

rivers and road network

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mainly as an exercise, because each region and set of data provides different

challenges in assigning risk priorities.For the definition of the road network, a vectorial GIS road database was

created from a vectorial database digitized from declassified military maps from

1996, actualized in Google Earth with the aid of Google/Yahoo/Bing Maps

overlays (thanks to the http://www.mgmaps.com service). Together with the

geometry, the classification of the road was keeps as an attribute, being later

considered in the description of the road vulnerability.

The locations of the emergency intervention centers were obtained from the

official site of the General Inspectorate for Emergency Situations (www.igsu.ro).

The county headquarter is in Focsani; other locations are in Adjud, Panciu and

Vidra. For the basic purpose of this study, all of these centers were considered

equal, as intervention potential efficiency.

For the description of road start-up vulnerability to earthquakes, several data,

converted into a raster format, was used. As said before, the type of road was

considered. Four classes were identified, and assigned numbers from 1 to 4 (1 for

the European Roads, 2 for National Roads, 3 for County Roads and 4 for Local

Roads).

The impact of slope on the road must always be considered, not just because of

the actual road angle, but also because of the potential hazardous versants and

roughness of the terrain. For computing the slope angle, a Digital Elevation Model(DEM) can be used. Based on the SRTM 3 Arc-Seconds free GIS data (http://

srtm.csi.cgiar.org/), slope angle values were computed for the Vrancea County,

varying from 00 to 31.50 (Fig. 8). The reclassification was based on equal intervals,

as it can be seen in Fig. 9. The highest the angle, the more vulnerable is considered

the road cell.

In case of an earthquake, the intensity defines how the earthquake was felt at the

surface. Each intensity scale links observed effects to a value, most common

between 1 and 12 (Modified Mercalli, MedvededSponheuerKarnik sca-

le = MSK and EMS-98). In the case of the road network, high intensities candetermine the disrupting of road segments, by cracking, structure failures (bridges,

tunnels etc.), soil liquefaction, landslides etc. Giving that Vrancea County is so

close to the hypocenter, expected high intensities are not to be neglected. That is

Fig. 7 Pie chart showing the

length of the road network in

Vrancea County

http://www.mgmaps.com/http://www.igsu.ro/http://srtm.csi.cgiar.org/http://srtm.csi.cgiar.org/http://srtm.csi.cgiar.org/http://srtm.csi.cgiar.org/http://www.igsu.ro/http://www.mgmaps.com/


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why, for the analysis, we used the isoseismal map, providing MSK intensities

between IX 1/2 and VII (Fig.10). Considering the effects associated with these

high intensities, multiplication values were defined (92 for I = VII, 93 for

I = VIII or VIII and 94 for I = IX and more) and later added in multiplying

risk elements that might be triggered by the earthquake, such as slope angle

(causing landslides or soil cracks), bridges or industrial sites failure.

Because road networks also depend on the adjacent land cover they pass

through, GIS data from the CORINE 2006 mission (http://www.eea.europa.eu/

data-and-maps/data/corine-land-cover-2006-raster) was also used. In this data,

major river bodies are defined, so bridge locations can be identified; knowing the

characteristics of the area, these crossings can receive a certain vulnerability value,

multiplied with the value of intensity. As a further work in the field, bridges can be

Fig. 8 Map of the slope

angle values

Fig. 9 Reclassification

values for the slope angle

raster

208 D. Toma-Danila
http://www.eea.europa.eu/data-and-maps/data/corine-land-cover-2006-rasterhttp://www.eea.europa.eu/data-and-maps/data/corine-land-cover-2006-rasterhttp://www.eea.europa.eu/data-and-maps/data/corine-land-cover-2006-rasterhttp://www.eea.europa.eu/data-and-maps/data/corine-land-cover-2006-raster


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added to the analysis as structures with specific damage functions, possible to be

also analyzed in real time by sensors. Beside bridges, CORINE data also includespossible hazardous areas, like industrial facilities, swamps, rock formations or

types of forest (Fig. 11). Table1shows how the classification was carried out.

Fig. 10 Isoseismal map of

the maximum credible

Vrancea earthquake

(Mw = 7.7) [3]

Fig. 11 CORINE 2006 data

used for the Vrancea County



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After all necessary data is converted into raster and reclassified, an important

step is to properly assign weight values for each risk factor. By using the Weighted

Overlay Tool, each of the final 3 impact rasters receive importance values and

weight (Table2). In this example, the slope was considered more important(50 %), the type of the road was considered to influence the vulnerability 20 %,

and land cover data 30 %, the last two having been also multiplied according to the

MSK maximum intensity factor. As a consequence, a preliminary vulnerability

map was obtained, with 0 as the less vulnerable road cell and 10 the possible most

affected. Most values are between 1 and 5, but there are also some extreme values.

The areas with these extreme values are later considered as very possible

generators of road blocks. Together with more personal knowledge about areas

that cause roadblocks in case of an earthquake, these most vulnerable points are

added to a shapefile polygon. The idea is that, by assigning random values

numerous times (using Field Calculator with arc rand expression), all various

possible scenarios will reflect the different dependencies in the road network; if a

whole area depends on a single vulnerable link with the origin points or the detour

route too long, this will reflect in a higher cost-distance.

In this case study, a specific model was created in ArcGIS Model Builder

(Fig.12), allowing the automation of the process. 100 different scenarios were

randomly computed, each one showing a particular situation that might occur, with

more or less hot points determining road blocks. All the cost-distance values in

each cell were then mediated, and a final vulnerability map was obtained (Fig.13).

Comparing it with the initial vulnerability map, an increase in the vulnerability of

some more isolated areas is revealed.

Table 1 Reclassification values for the CORINE 2006 data

CORINE 2006 land cover classification Reclassification

value

Continuous urban fabric 6

Discontinuous urban fabric, industrial or commercial units,construction sites, water bodies

4

Rocks, mineral extraction sites 3

Dump sites, beaches, dunes, sand, different types of forest 2

Table 2 Weight overlay percentages and values used for generating the basic cost raster

Raster type Weight overlay (%) Reclassification (after multiplication with MSK Intensity)

Road weight 20 1 = 1; 2 = 2; 3 = 4; 4 = 6

Land cover 30 0 = 0; 4 = 2; 6 = 5; 8 = 7; 9 = 8;12 = 10; 16 = 10

Slope 50 0 = 0; 2 = 1; 3 = 1; 4 = 2; 6 = 4; 8 = 6; 12 = 6;

16 = 8; 18 = 8; 24 = 10; 30 = 10; 32 = 10

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3.3 Result Analysis

The final results are more relevant showed on maps, which is in fact the purpose of

using a GIS. The distance raster can be symbolized in order to show different

vulnerability degrees, and various overlays can be made, in order to better

understand the implications and the solutions to the questions in the introduction.

In Fig.13 is shown the road network vulnerability, together with building

damage estimates obtained from a simulation for the 1977 earthquake, with SE-

LENA Software [8]. It can be noticed that although the most vulnerable roads are

in the western part of the county, there are not a lot of settlements there, and also

Fig. 12 Scheme of the model built in order to create random simulations, for the cost-distance

method

Fig. 13 The road network

vulnerability and the building

loss estimates during a 1977

similar earthquake



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network monitoring to planning purposes, the methodology can assess the

behavior of the network and its vulnerability.

The dependency for a start-up complex GIS database is reduced.

Maps can be created very easily, giving that all the output is in GIS format.

The Cost-Path Method can be applied in order to find the best routes.

As this paper marks the first steps in using the mentioned approach, if

scientifically approved, further studies will take place, in order to find better ways

of reclassifying the risk factors rasters, creating a complete module for ArcGIS or

experimenting with more complex data and network models.

References

1. D.M. Atkinson, P. Deadman, D. Dudycha, S. Traynor, Multi-criteria evaluation and least cost

path analysis for an arctic all-weather road. Appl. Geogr. 25(4), 287307 (2005)

2. M.A. LaRue, C.K. Nielsen, Modelling potential dispersal corridors for cougars in midwestern

North America using least-cost path methods. Ecol. Model. 212, 372381 (2008)

3. G. Marmureanu, C.O. Cioflan, A. Marmureanu, Intensity seismic hazard map of Romania by

probabilistic and (neo)deterministic approaches, linear and nonlinear analyses. Rom. Rep.

Phys. 63(1), 226239 (2011)

4. N. Mandrescu (ed.), The Large Vrancea Intermediate Depth Earthquakes Occurred in the

XXth Century and Their Effects on the Romanian Territory; Photographic Testimonies

(Academiei Romane, Bucharest, 2008)

5. National Institute for Earth Physics (NIEP), Romplus earthquake catalogue 2012, Romania

6. Pho Thanh Tung, Road vulnerability assessment for earthquakes. Ph.D. Dissertation,

International Institute for Geo-information Science and Earth Observation, Enschede, 2004

7. P.E. Pinto, F. Cavalieri, P. Franchin, A. Lupoi, Definition of system components and the

formulation of system functions to evaluate the performance of transportation infrastructures.

SYNER-G Project No. 244061, Deliverable no. D2.6, June 2011

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