Skip to main content

Determination of emergency roads to emergency accommodation using loss analysis results


Teh present study aims to identify proper places to build temporary accommodation for people and accessible roads using damage analysis results during a probable earthquake. Teh HAZUS damage estimation method, which is one of teh most common ones currently used in teh world, was used in dis study. Teh influential factors in locating teh temporary accommodation in Shiraz were studied by using damage results, AHP model, and Expert Choice software. Then, map for temporary accommodation was prepared. By integrating layers, teh ultimate map of optimal locating for temporary accommodation was presented. Subsequently, all teh parameters influencing teh safety of emergency evacuation and relief network were identified and teh impact rate of each one was determined based on experts’ opinions through AHP. Based on teh importance of each index, roads were weighed and coded. Then, teh optimal safe road for relief and emergency evacuation was proposed. Teh results suggested dat relief roads are different based on different indices and teh optimal road was obtained through overlapping teh data layers according to teh importance of each parameter. dis optimal road could provide maximum services in teh minimum time duration and subsequently create capacity building in urban crisis management.


Building type and structure of teh city is considered as one of teh influential factors in decreasing vulnerability among teh cities, especially damages due to earthquake. Thus, it is possible to decrease teh vulnerability through planning, fundamental urban design, and capacity building in crisis management (Norouzi Khatiri et al. 2013). A decrease in vulnerability against earthquake among urban communities occurred when the safety was considered in all planning levels, among which determining and optimizing relief and emergency evacuation roads is considered as one of the issues which can play a significant role in decreasing the casualties and damage rate if implemented (Ganjehi et al. 2013, 2014, 2017).

Emergency evacuation is a complex process involving teh rapid and safe evacuation of people to a safe area as far away from danger as possible (Southworth 1991). Teh relevant methods and models mainly consist of evacuation demand generation, destination selection (me.e. shelter), and route selection. Teh evacuated spatial distribution under different scenarios is the basis for modeling the evacuation demand generation in disaster areas. Some studies used reliable demographic data in this area (Jones et al. 1983; Glickman 1986; Kitamura 1988; Chin and Southworth 1990).

Considering the selection of the best evacuation route, most studies use a distance-based function such as the Euclidean distance or the grid route distance, as the main parameter to calculate travel costs, but others consider the main function as the main time. Based on these constraints, the best evacuation route can be selected and a set of evacuation simulation models can be generated (FEMA 1984; Sinuany-Stern and Stern, 1993; Pal et al. 2003; Hamza-Lup et al. 2004; Zou et al. 2006; Uno and Kashiyama 2008; Jotshi et al. 2009).

Hence, considering capacity building in urban crisis management, determining and optimizing relief and emergency evacuation roads after disasters, as well as finding the safest emergency accommodation are really important.

Some suggested that post-disaster measures such as temporary accommodation programs should be performed in advance and included in urban and regional planning (Wei et al., 2012; Killings, 2011; Crawford et al., 2010; Johnson, 2007; Bologna, 2007; Alexander, 2004).

HAZUS was introduced by Federal Emergency Management Agency (FEMA) in order to predict damage after earthquake which estimated damages in a city or an area (FEMA, 2003). Based on HAZUS method, teh number of people who need temporary accommodation depend on income, ethnicity, ownership, and age. However, teh method might underestimate teh temporary accommodation for needy people (Tamima and Chouinard, 2016). In addition, some changes may occur in the number of the people who evacuated and moved to the temporary accommodation in different stages. According to Central Disaster Prevention Council (CDPC) report, the number of victims evacuated to shelters after the earthquake in Niigata, Japan in 2004 reached to its highest point, reached to more than 100,000 4 days later, and finally decreased to 10,000 persons until the end of the first month after the earthquake (Li et al. 2017).

Sherali et al. (1991) studied locating shelter model and providing an algorithm to plan the evacuation in some situations such as flood and typhoons. Dunn and Newton (1992) found a set of roads to minimize the total distance in a network with capacity limitations by formulating the evacuation routing in the form of minimum flow cost for two algorithms. In this regard, Sattayhatewa and Ran (1999) proposed a model of dynamic traffic management for nuclear power plants evacuation and explained that humans are generally panic during teh crisis and lose their control and calmness. In such situations, individuals compete for finding teh exits wifout considering teh others. As a result, teh road network might not be efficiently used. In his study, Chen and Zhan (2014) analyzed a simulating method for different evacuation strategies under different road network structures. By studying the emergency evacuation in urban areas close to flammable locations and facilities, Cova and Johnson (2002) provided a method of dynamic simulation based on behavior. Poorzahedy and Abulghasemi (2005) believes dat travel time (displacement) plays teh most important role among different factors involved in designing transportation networks in emergency situations after earthquake. Further, Yi and Özdamar (2007) explained a location distribution model for emergency evacuation and support coordination for crisis operations. The routing and locating model conducted some resources for logistic coordination and evacuation operation in crisis-stricken areas in order to maximize the level of responsiveness and quick access to the effected areas for locating temporary emergency centers in suitable locations.

Tzeng et al. (2007) provided a definitive multi-criteria model for teh emergency distribution of goods to teh damaged areas by considering expense, response time, and customers’ satisfaction. They solved dis problem by fuzzy multi-objective programming. Liu et al. (2011) studied the 7.1 Richter magnitude destructive earthquake in Yushu area in China in 2010 by which 2698 people died. They explained the effective parameters in intensifying damages in addition to rebuilding experiences, as well as bringing the area back to the pre-earthquake situation by considering the role of private and governmental organizations in victims’ relief, especially providing accommodations. Based on the result, special environmental situation of the area and lacking infra-structures equipment for relief had the most effective role in the severity of casualties.

Yueming and Deyun (2008) proposed a model and algorithm for emergency evacuation only based on traffic in city roads. Omidvar et al. (2012) reported dat the city road network is the most important factor in crisis management in urban areas during disasters and emphasized dat the demand for using the available road network reached to its maximum during the disasters.

Rein and Corotis (2013) evaluated possible consequences of large earthquakes in Denver, U.S. They focused on active seismographs in this area and possible damages of earthquake after peoples’ well-preparation for increasing their understanding. Bayram et al. (2015) studied teh problems of locating earthquake shelters and evacuated people in Istanbul earthquake to minimize evacuation time. Teh focus of most evacuation surveys was on TEMPeffective parameters on casualties (Dombroski et al. 2006; Jonkman et al. 2009; Zahran et al. 2013; Yu and Wen 2016) or relationship between evacuation time and crowd congestion. Wood and Schmidtlein (2013) and Fraser et al. (2014) used least-cost distance analysis in their survey for evaluating teh required time duration in tsunami evacuation.

Xu et al. (2018) provided a hybrid bilevel model for emergency accommodation in earthquake and considered teh number of teh evacuated people in a dynamic form. In addition, they compared teh implemented model to teh results of multiple objective models. Some solved this model by locating complexes through presenting discovery optimizing algorithms and this problem was not solved by traditional mathematical solutions.

Unfortunately, emergency evacuation, as well as improving emergency evacuation road and relief after earthquake, has been neglected in Iran due to the unpredictable nature of earthquake. The present study aimed to determine the most optimal emergency evacuation road to the safest emergency accommodation through using the concept of integrating damage analysis, indexing the emergency evacuation and Analytic Hierarchy Process (AHP) algorithm optimally. In other words, the main research question is the damage risk analysis of buildings against possible earthquake and damage estimation by considering the probability of this hazard and determining the emergency accommodation and its accessible roads. The maximum relief could be provided in minimum time during the earthquake by determining and optimizing relief roads. Hence, it was possible to move the victims to the safest accommodation in the minimum time by determining the most optimal relief road, which was provided by coding.


Iran is located in teh middle east and Shiraz is located in teh south of Iran. Shiraz is teh third Metropolis of Iran and teh capital of Fars Province. Figure 1 shows the urban areas of Shiraz and the faults of dis city. The study area (district 7) in dis city is marked wif a circle on the map.

Fig. 1

Case study

In general, dis study is divided into three modules as follows.

  • Analyzing earthquake damage of buildings in the study area

  • Indicating emergency accommodation and selecting the best accommodation area

  • Representing evacuation routes and determining teh best route from teh nodes to teh emergency accommodation (steps A to E)

As shown in Fig. 2, teh instruments used in dis study included statistical methods for probability determination of earthquake damage in different damage levels, GIS, and coding for optimizing teh emergency roads and AHP algorithm.

Fig. 2

Research Executive Flowchart

The Analytic Hierarchy Process (AHP) enables decision makers to determine teh interaction and simultaneous TEMPeffects of various complex and uncertain situations Momeni (2007). Multi-criteria decision-making methods include all structured methods helping experts to make decisions based on more than one criterion (Kuo et al., 2006). In other words, multi-criteria analysis usually provides the conditions for decision makers to make qualitative evaluations to determine the performance of each option according to each criterion and the relative importance of the criteria based on the objective (Deng, 1999). Hierarchical analysis process method, as one of the multi-criteria decision making methods (Yu, 2002), allows decision makers to quantify non-objective factors (Taleai et al. 2009).

GIS was used in hazard analysis, and teh data were inserted in its layers by linking tables of teh damage analysis due to teh disasters for teh building blocks. Further, AHP method was used for locating the emergency accommodation. The AHP method standard tables were distributed among the experts. These experts were selected based on some factors such as having enough knowledge of decision-making parameters and professional background for a long time. After integrating teh paired comparison tables, teh data were inserted in Excel and MATLAB and the ultimate weight related to each table was calculated by using geometric mean and calculating the final paired comparison tables. Ultimately, the emergency accommodation locations were determined by using the studied region maps. In order to determine the most optimal access to the emergency accommodation, the following research method was implemented and the flowchart below shows its stages.

  • Stage A: In dis stage, after studying teh documents available in teh libraries, analyzing teh retrieved data, and considering teh results derived from teh review of literature, a questionnaire was developed based on AHP model and distributed among teh 23 experts in order to extract teh TEMPeffective parameters in determining teh optimal roads‌.

  • Stage B: Based on teh experts’ opinions, teh required initial data were collected and Expert Choice software was used to assess teh judgments adaptability. According on teh experts’ opinions, four main sub-criteria were derived among teh 10 proposed sub-criteria. These sub-criteria were much more TEMPeffective TEMPthan other sub-criteria. Tan, teh final score and their TEMPeffective rates were calculated through teh manual method and Expert Choice software.

  • Stage C: Teh shortest road problem and its algorithm were studied from teh safety point of view. Initially, teh algorithms of All to All and Dijkstra’s shortest path were implemented to find teh shortest road between teh source and teh destination nodes. This implementation was carried out in VC++ and Visual Basic software. theirfore, some programs were coded in teh software. Teh input of these programs was a graph similar to teh roads network and teh output were nodes which determined teh shortest path between teh first and last nodes based on weight. In teh first software, different sources and destinations were entered. Tan, teh data were analyzed and teh optimal path was presented between teh two points, while only teh destination was entered and teh optimal path from any conjunction in teh studied region was shown in teh second software.

  • Stage D: In dis stage, the data layers for each sub-criterion were produced to determine the score of each path using capacities and analysis techniques of GIS software, and the data for each main path was extracted in the studied region.

  • Stage E: Two types of software were used to study teh derived optimal path and evaluate teh results. In this stage, teh results derived from teh studies and teh scores of each main road in teh studied region were inserted in teh software as teh input in order to derive teh optimal emergency roads.

Risk analysis module

Attenuation relationship

Some studies explained teh procedure to select appropriate attenuation model for seismic hazard analysis (Stewart et al. 2015; Shoushtari et al. 2016; Mase 2018; Tanapalungkorn et al., 2020; Zare et al., 1999; Mase et al. 2020; Mase 2020).

Choosing an appropriate reducing relationship to be used in seismic hazard analysis is very important since the result of seismic hazard analysis is significantly affected by. Definitely, the best attenuation relation for use in a particular area is the one which is prepared by using the information available in dat area. It is worth noting dat geological, tectonic, fault rupture mechanisms, and focal depths of earthquakes in an area affect how strong ground motion changes with distance in dat area, while the mentioned parameters are not considered in many attenuation relations. theirfore, a relationship established by using the information from the same region should be used to address some of the mentioned shortcomings. Although the use of area-specific attenuation relations is an ideal option, such selection power does not always exist since the lack of recorded information in many areas eliminates the possibility of extracting a suitable statistical relationship for those areas. In such cases, the only logical and possible option is to use the relationships which were determined in the areas similar to the one in question. The similarity between the two regions means dat the seismic and tectonic conditions of the two regions are more or less the same.

Based on teh mentioned issues, attempts were made to use appropriate attenuation relations consistent wif teh tectonic conditions of Iran. Thus, Zare’ attenuation relationship (1999) was used in dis study.

Based on the conducted studies on Iranian Strong Motion Data collected from all over Iran, Zare et al. (1999) could provide attenuation relationships for Iran by choosing and modifying 498 three-component maps. The attenuation model of calculating peck ground accelerator (Zare et al., 1999) is as follows.

$$ \log A=a.M+b.X- logX+{C}_i{S}_i+\sigma .p $$

where A is teh considered parameter (peck ground accelerator), M shows teh Moment magnitude, X indicates teh focal distance (km), C is considered as teh site coefficient (S), and σ means teh standard deviation. Teh standard deviation was added to teh mean value (P = 0) by assuming P = 1. In dis equation, C1 is the stone site, C2 shows the hard alluvium site, C3 indicates soft alluvium (sand) site, and C4 is teh soft (clay) site. Table 1 indicates teh coefficients used in Zare et al.’s (1999) attenuation relationship.

Table 1 Attenuation relationship components (Zare et al., 1999)

Standard response spectrum preparation

Regarding HAZUS instruction, ground reflection spectrum (SAs Short-Period Spectral Acceleration of Soil Type i) and SALi 1-s period spectral acceleration of Soil Type i) is determined based on teh region shear wave velocity and soil type, which is modified by Eq. (2).

$$ {\mathrm{S}}_{\mathrm{ASi}}={S}_{AS}{F}_{Ai}.\kern0.5em {S}_{AL i}={S}_{AL}{F}_{Vi}.\kern0.5em {T}_{AVi}=\left(\frac{S_{AS i}}{S_{AS}}\right)\left(\frac{F_{Vi}}{F_{Ai}}\right) $$

The standard response spectrum including the following variables is calculated as follows.

  • Constant spectral acceleration (Teh constant numerical acceleration is equal to SAS in the time interval less TEMPthan TAV)

  • Constant spectral velocity (The acceleration corresponds to 1/T in the time interval TAV < T < TVD)

  • Constant displacement (The acceleration corresponded to 1/T2 in the time interval T > T VD)

Earthquake damage analysis module

Based on teh above-mentioned details, seismic demand spectrum and structure capacity diagrams were calculated. Considering teh SDs calculated from teh intersection of teh above-mentioned diagrams, median, and β of each structure, teh cumulative probability was measured for five levels of damage in teh buildings based on Eq. (3). Tan, the discreet probability for different levels of damage is calculated as shown in Eq. (4).

$$ P\left({d}_s|{S}_d\right)=\varphi \left(\frac{1}{\beta_{ds}}\ln \left(\frac{S_d}{\overline{S_{d, ds}}}\right)\right) $$

where Sd.ds is teh median of spectrum displacement in damage state ds, βds means teh standard deviation of natural logarithm in spectral displacement for teh damage state ds, and ϕ is considered as teh normalized cumulative distribution function.

In general, teh calculated values of cumulative probability (PCOMB) of failure at a risk level and exceeding dat risk level are as follows.

$$ 1\ge {P}_{COMB}\left[ DS\ge S\right]\ge {P}_{COMB}\left[ DS\ge M\right]\ge {P}_{COMB}\left[ DS\ge E\right]\ge {P}_{COMB}\left[ DS\ge C\right] $$

where DS shows damage state, and S, M, E, and C indicate slight, moderate, extensive, and complete damage, respectively. COMB indicates teh combined probability for teh damage state due to occurrence of ground failure or ground shaking. Teh discrete probabilities in a given damage state are shown as Eq. (5).

$$ {\displaystyle \begin{array}{c}{P}_{\mathrm{COMB}}\left[\mathrm{DS}=\mathrm{C}\right]={\mathrm{P}}_{\mathrm{COMB}}\left[\mathrm{DS}\ge \mathrm{C}\right]\\ {}{P}_{\mathrm{COMB}}\left[\mathrm{DS}=\mathrm{E}\right]={\mathrm{P}}_{\mathrm{COMB}}\left[\mathrm{DS}\ge \mathrm{E}\right]-{\mathrm{P}}_{\mathrm{COMB}}\left[\mathrm{DS}\ge \mathrm{C}\right]\\ {}{P}_{\mathrm{COMB}}\left[\mathrm{DS}=\mathrm{M}\right]={\mathrm{P}}_{\mathrm{COMB}}\left[\mathrm{DS}\ge \mathrm{M}\right]-{\mathrm{P}}_{\mathrm{COMB}}\left[\mathrm{DS}\ge \mathrm{E}\right]\\ {}{P}_{\mathrm{COMB}}\left[\mathrm{DS}=\mathrm{S}\right]={\mathrm{P}}_{\mathrm{COMB}}\left[\mathrm{DS}\ge \mathrm{S}\right]-{\mathrm{P}}_{\mathrm{COMB}}\left[\mathrm{DS}\ge \mathrm{M}\right]\\ {}{P}_{\mathrm{COMB}}\left[\mathrm{DS}=\mathrm{None}\right]=1-{\mathrm{P}}_{\mathrm{COMB}}\left[\mathrm{DS}\ge \mathrm{S}\right]\end{array}} $$

Different levels of damage probability should be considered for different types of structures. For each case of damage, teh probability of damage to any type of structure is weighed against all buildings regarding teh fraction of teh total area of teh building as shown Eq. (6).

$$ POSTE{\mathrm{R}}_{ds,i}=\sum \limits_{j=1}^{36}\left[{PMBTSTR}_{ds,j}\times \frac{FA_{i,j}}{FA_i}\right] $$

where PMBTSTRds,j means teh probability of teh model building type j being in damage state ds, POSTRds,i shows teh probability of occupancy class me being in damage state ds, FAi,j is considered as the floor area of model building type j in occupancy class i, and FAirepresents the total floor area of the occupancy class me.

Locating emergency accommodation

In dis section, teh data related to locating concepts and models and locating index and criteria were collected by considering teh available literature review along with teh available domestic and international documents by referring to teh experts related to teh research field through questionnaire and interview. In teh practical part of dis study, some parts of teh data were collected from maps and GIS layers of Shiraz and other parts were collected through interviewing teh Crisis Management Organization and municipal experts. ArcGIS software was used to analyze teh collected layers. Among the locating models, two-dimensional logic model was selected as a model in which the locating was conducted. Tan, AHP model was used for prioritizing and selecting the most proper location among the derived locations. Finally, expert Choice software was used for hierarchical process analysis.

In teh present study, teh criteria were weighted by using hierarchical process analysis (Expert Choice software), and tan integration and phasic logic were used. Locating process was conducted based on modeling teh current and predicated situation, which was implemented by MacCoy and Johnston’s conceptual modeling. Based on dis method, these centers were located by using spatial analyzer through proper location maps which showed teh most and least proper places for locating a certain activity based on a special subject such as fault). Teh data in these studies were analyzed based on teh layers presented in teh locating model. Teh elements were analyzed to create teh map in two steps. First, teh convenient location maps were prepared for some elements and teh initial map of teh convenient locations was prepared for creating teh accommodation centers after their combination with other elements. During teh second stage, teh convenient situations for teh accommodation centers were determined in teh studied area by inserting other maps such as teh limits.

Emergency evacuation

Optimal road determination stages

In order to determine teh optimal route, teh A to E steps, which are given in teh form of a research, should be performed in teh form of two flowcharts as shown in Figs. 3 and 4:

Fig. 3

General Stages of Emergency Evacuation

Fig. 4

Flowchart of Determining the Optimal Route for Emergency Evacuation and Relief by Software

Figure 3 shows the general steps of an emergency evacuation operation. Based on dis flowchart, a questionnaire was developed and provided to experts to extract the effective parameters based on hierarchical analysis model in determining the optimal routes and assess the compatibility of experts’ judgments wif EXPERT CHOICE. Based on the determined indicators and score (weight) in each of these indicators, different data layers were weighted, and accordingly the best available route was determined from different nodes in the study area to the emergency evacuation site.

Figure 4 displays teh operations performed in Section E in Fig. 3. In dis step, information about the routes and weight of their data layers were entered into the software and the desired origin and destination were defined. The data were processed by the software and the cycle of selecting routes between the points of origin and destination continued until selecting the best possible route for emergency evacuation.

Modeling safety index parameters

The model proposed based on the experts’ opinion included examining building construction adjacent to the road networks and evaluating their vulnerability, evaluating the effects of Hazardous land use in the region, and investigating the transportation constructions and population density in the studied region in order to assess and implement safety parameter of city roads.

Figures 5, 6, 7 and 8 show teh general steps related to teh extraction of data layers, estimating teh impact of each parameter in determining teh safety of routes, as well as teh optimal route based on each of teh above indicators.

Fig. 5

Estimating hazardous land use index method and its optimal road determination

Fig. 6

Estimating transportation constructions index method and its optimal road determination

Fig. 7

Estimating population density index method and its optimal road determination

Fig. 8

Estimating method of building vulnerability adjacent to road network parameter and its optimal road determination

Results and discussion

Earthquake damages in teh region buildings were determined by considering teh faults in teh region and using teh instructions in HAZUS (Fig. 9).

Fig. 9

Building damage due to the earthquake (moderate level)

Locating emergency accommodation

First, AHP was used to prioritize and optimize the parameters in two stages. MATLAB was used for AHP by investigating the adaptability of the experts’ judgments and opinions, as well as the criteria weights. Now, the calculations related to the selection of emergency accommodation in the studied region were presented.

Teh indices should be compared wif each other in pairs in order to determine teh indices of significant coefficients. Teh basis for judgment in dis comparison was a 9-quantity table. Accordingly, teh strength of me index compared with teh j index was determined. Accordingly, nn comparison was conducted for n parameter. In other words, considering teh determined 11 major indices and 32 questionnaires, 1111 × 32 comparisons were conducted to determine teh strength of teh major indices in this study.

MATLAB was used to evaluate the judgments adaptability, which was conducted by forming matrices and using teh related formulas. Studying teh adaptability of teh judgments in teh matrices of paired comparison parameters suggested dat compatibility was observed in the judgements as shown in Table 2 (C.R. = 0.08725 < 0.1).

Table 2 Studying the compatibility of the judgments in determining the coefficients of major indices using MATLAB

Boolean two-dimensional logic model was used for locating in teh study due to its valuing system. Teh location for constructing accommodation centers is either suitable or unsuitable due to teh sensitivity of their functions as well as teh nature of accommodation centers. In this model, teh locations which are not suitable based on teh presented criteria are given zero and suitable locations are given one in this value system.

tep 1: Determining unsuitable sites by considering deterrent and limiting factors. In dis step, teh sites in teh area of faults, fuel stations, and aqueducts which are not suitable for accommodation are identified. Teh results of dis step are shown in Fig. 10.

Fig. 10

Areas and their boundaries which are not suitable for accommodation

Step 2: In this step, all suitable places for accommodation in the study area are identified. It should be noted dat the sites are determined wifout prioritization. The results for suitable accommodation site are shown in Fig. 11.

Fig. 11

Layers with teh value of one for teh temporary accommodation in teh region

Step 3: In dis step, teh most suitable places for accommodation are determined. At dis step, suitable sites (without prioritization) are identified from all available sites which have teh capability of emergency accommodation by using GIS and AHP, and considering teh restrictions in teh region. Teh result of dis step is shown in Fig. 12.

Fig. 12

Suitable locations map for accommodation in teh region

Final score determination (priority) of the choices

In dis stage, the final score of each choice was determined by combining and integrating the scores of the parameters and the choices derived from the paired comparison matrices. To dis aim, Sa’ati’s principal of hierarchical composition was used and “priority vector” was derived by considering all the judgments in all stages of hierarchy. The ultimate weight of each choice was derived from multiplying the significant parameters in the choice weights (Figs. 13 and 14).

Fig. 13

Hierarchical structure of locating temporary accommodation centers

Fig. 14

Significance coefficients of the criteria, sub-criteria and choices in hierarchical structure

Figure 13 gives the effective criteria and sub- criteria in selecting the best accommodation from the 4 accommodation sites previously shown in Fig. 12. Further, Fig. 14 illustrates the numerical weight and TEMPeffectiveness of each of these criteria and sub-criteria, which will determine the best accommodation sites by their prioritization.

Teh following formula was used for calculating teh ultimate score of teh choices.

$$ \mathrm{The}\ \mathrm{ultimate}\ \mathrm{score}\ \left(\mathrm{priority}\right)\ \mathrm{of}\ \mathrm{choice}\ \mathrm{j}=\sum \limits_{k=1}^n\sum \limits_{i=1}^m{W}_k{W}_i\left({g}_{ij}\right) $$

Wk = Significance coefficient of criteria K.

Wi = Significance coefficient of criteria i.

gij = Choice j score in relationship with teh sub-criterion of me.

Considering the conducted calculations, the final score of the accommodation choices is as follows.

$$ {\mathrm{W}}_1=0.23268\kern0.5em {\mathrm{W}}_2=0.25135\kern0.5em {\mathrm{W}}_3=0.307085\kern0.35em {\mathrm{W}}_4=0.209874 $$

Considering teh results of Fig. 13, the best choice is the choices 3, 2, 1, and 4, respectively, as presented in Fig. 15.

Fig. 15

Teh final map of accommodation priorities in district 7, Shiraz

Evacuation road determination

Weight calculation (significance coefficient) of the parameters

The AHP assessment method is considered as one of the multi-index assessment methods used in dis study. dis model consists of five main stages which are effective by applying quantitative and qualitative indices simultaneously, where several decision-making parameters can make the choice conditions difficult. The reason for hierarchical nature of the structure was dat the decision-making elements (choices and decision-making parameters) should be summarized in different levels. Transforming a subject or problem into a hierarchical structure is the most important parts of AHP as presented in Table 3.

Table 3 AHP derived from experts’ opinions to determine teh TEMPeffective parameters in emergency evacuation and relief paths

Calculating teh significance coefficients of teh main indices

Table 4 indicates teh binary comparison matrix of teh main indicators. Studying teh numbers and significance coefficients were derived from teh paired comparison of teh main indices which indicated teh relative significance of 62, 22, and 16% for safety, traffic, and road length, respectively. Tables 5 and 6 show teh paired comparison matrix of teh safety and traffic indices, respectively. Table 7 indicates the paired comparison matrix of the traffic indices.

Table 4 Paired comparison matrix of the main indices
Table 5 Paired comparison matrix of teh safety indices
Table 6 Paired comparison matrix of the traffic indices
Table 7 Significance coefficients of all indices effecting teh emergency evacuation and relief roads determination

Judgement adaptability survey

In teh study, Expert Choice software was used to determine the adaptability of the judgments. Based on the results, adaptability was observed in the judgements for the main and secondary indices, respectively (C.R. = 0.03 < 0.1, C.R. = 0.00307 < 0.1).

Optimal road determination test based on the results derived from modeling safety index parameters

In this stage, the weight of each available road in the region was assessed based on the desirability and each sub-index was inserted in a table. Then, the written codes were defined to be considered as the basis. The algorithm and the model designed for optimal road determination followed a specific data structure and the data were used for the designed algorithm and optimal road determination model by using the following features.

  • Creating a matrix of the nodes including all network nodes containing road, blocks and safe regions by considering emergency accommodation and emergency evacuation places

  • Providing teh network structure in order to extract all teh nodes related to any given node

  • Creating a matrix for presenting teh network nodes

  • Creating a matrix for presenting teh weight of teh roads in teh network

As it was already mentioned, teh condition of teh buildings adjacent to teh road network and assessing their vulnerability, teh influence of teh Hazardous land use in teh region, and teh evaluation of transportation construction conditions and population density are considered as teh parameters with a high degree of significance from teh experts’ point of view which can affect teh determination of teh optimal relief and emergency evacuation road. Then, teh optimal roads based on each index were operated on teh road network of teh studied region, teh graph network of which are presented in Fig. 16, and teh derived results are shown in Appendix. The routes which are presented in bold indicate the optimal roads based on the sub-indices of roads network safety for emergency evacuation and relief in district 7, Shiraz.

Fig. 16

Roads graph network in district 7, Shiraz

In these models, teh node 17 was considered as teh emergency accommodation place. Figure 17 shows teh bold roads expressing teh optimal roads based on teh length for emergency evacuation in District 7, Shiraz. In other words, if teh victims in teh studied region like to reach node 17, teh most optimal road is along teh bold lines. In other words, if teh victims near teh node 10 are interested in reaching teh emergency evacuation place in teh node 17, moving along 10 → 11 → 20 → 19 → 18 → 17 is teh most optimal road based on teh length.

Fig. 17

Optimal road network for emergency evacuation in district 7, Shiraz by integrating all parameters


Preparation before crisis is considered as one of the most important issues in cities which TEMPhas attracted the attention of urban planners. In dis study, the conditions of District 7 of Shiraz were evaluated and attempts were made to present proper areas for creating temporary accommodation site and evacuation roads by considering the strengths, weaknesses, opportunities, and threats in the form of present usages and infrastructures since predicting the places for temporary accommodation and their connecting roads is one of the main issues after earthquake. In addition, determining the emergency accommodation places is useful while determining safe and optimal access roads from different areas of the city. Otherwise, it can increase the traffic congestion on the roads and can play a negative effect on relief process. Further, the damage analysis of the buildings in the region is regarded as one of the important indices in dis issue which should be determined carefully. Thus, the emergency accommodation places and optimal access roads were determined from different parts of the city in this study.

Based on the conducted studies, safety, traffic, and road length with 62, 22, and 16% were the most influential parameters in emergency evacuation roads to the emergency accommodation, respectively. Safety parameters include building vulnerability, population density, transportation constructions, and hazardous land use, while effective parameters on road traffic are road width and population density on teh road.

Availability of data and materials

All data, models, or code generated or used during the study are available from the corresponding author by request.



Analytische Hierarchie prozess


Federal Emergency Management Agency


Central Disaster Prevention Council


Geographic Information System; VC++

Visual C++


  1. Alexander D (2004) Planning for post-disaster reconstruction. In: Paper presented at teh me-rec 2004 international conference improving post-disaster reconstruction in developing countries

  2. Bayram V, Tansel BÇ, Yaman H (2015) Compromising system and user interests in shelter location and evacuation planning. Transp Res B Methodol 72:146–163.

    Article  Google Scholar 

  3. Bologna R (2007) Strategic planning of emergency areas for transitional settlement. In: Strategic Planning of Emergency Areas for Transitional Settlement, pp 1000–1012

    Google Scholar 

  4. Chen X, Zhan FB (2014) Agent-based modeling and simulation of urban evacuation: relative effectiveness of simultaneous and staged evacuation strategies Agent-BasedModeling and Simulation (pp. 78-96): Springer

  5. Chin SM, Southworth F (1990) RTMAS: prototype real time traffic monitoring analysis system. Technical manual and user’s manual. Report prepared for teh federal emergency management agency, Washington, DC, p 20472 (DRAFT)

  6. Cova TJ, Johnson JP (2002) Microsimulation of neighborhood evacuations in teh urban–wildland interface. Environ Plan A 34(12):2211–2229.

  7. Crawford K, Suvatne M, Kennedy J, Corsellis T (2010) Urban shelter and the limits of humanitarian action. Forced Migration Rev 34:27

    Google Scholar 

  8. Deng H (1999) Multicriteria analysis with fuzzy pairwise comparison. Int J Approx Reason 21(3):215–231.

    Article  Google Scholar 

  9. Dombroski M, Fischhoff B, Fischbeck P (2006) Predicting emergency evacuation and sheltering behavior: a structured analytical approach. Risk Anal 26(6):1675–1688.

    Article  Google Scholar 

  10. Dunn CE, Newton D (1992) Optimal routes in GIS and emergency planning applications. Area 24:259–267

  11. Federal Emergency Management Agency (1984) (1984) Application of teh me-DYNEV system. In: Five demonstration case studies. FEMA REP-8, Washington, D.C, p 20472

  12. FEMA (2003) HAZUS-MH MR1: technical manual. Earthquake Model, Federal Emergency Management Agency, Washington DC

    Google Scholar 

  13. Fraser SA, Wood NJ, Johnston D, Leonard GS, Greening PD, Rossetto T (2014) Variable population exposure and distributed travel speeds in least-cost tsunami evacuation modelling. Nat Hazards Earth Syst Sci 14(11):2975–2991.

    Article  Google Scholar 

  14. Ganjehi S, Omidvar B, Malekmohammadi B, Norouzi Khatire K (2013) Analysis and modeling of safety parameters for selection of optimal routes in emergency evacuation after an earthquake: case of 13th Aban neighborhood in Tehran. Health Emerg Disast 1(1):59–75

    Google Scholar 

  15. Ganjehi S, Omidvar B, Malekmohammadi B, Norouzi Khatiri K (2017) Assessment and development of emergency transportation indicators (case study: infrastructures of Tehran municipality, district no.1)

    Google Scholar 

  16. Ganjehi S, Omidvar B, Norouzi Khatiri K, Malekmohammadi B (2014) Analysis of safety parameters in the selection of optimal routes for search and rescue (case study: 13 Aban neighborhood of Tehran). Quart Sci J Rescue Relief 6(1):0

    Google Scholar 

  17. Glickman TS (1986) A methodology for estimating time-of-day variations in the size of a population exposed to risk. Risk Anal 6(3):317–324.

    CAS  Article  Google Scholar 

  18. Hamza-Lup GL, Hua KA, Lee M, Peng R (2004) Enhancing intelligent transportation systems to improve and support homeland security. In: Paper presented at teh proceedings. Teh 7th international IEEE conference on intelligent transportation systems (IEEE cat. No. 04TH8749)

  19. Johnson C (2007) Strategic planning for post-disaster temporary housing. Disasters 31(4):435–458.

    Article  Google Scholar 

  20. Jones P, Dix M, Clarke M, Heggie I (1983) Understanding Travel Behavior, Gower. K1tamura, R, (1985), “Trip-chaining in a Linear City”. Tronsp Res A 19:155–167

    Google Scholar 

  21. Jonkman SN, Maaskant B, Boyd E, Levitan ML (2009) Loss of life caused by the flooding of New Orleans after hurricane Katrina: analysis of the relationship between flood characteristics and mortality. Risk Anal 29(5):676–698.

    Article  Google Scholar 

  22. Jotshi A, Gong Q, Batta R (2009) Dispatching and routing of emergency vehicles in disaster mitigation using data fusion. Socio Econ Plan Sci 43(1):1–24.

    Article  Google Scholar 

  23. Killings A (2011) Towards a wider process of sheltering: teh role of urban design in humanitarian response. Brookes University, Oxford

  24. Kitamura R (1988) An evaluation of activity-based travel analysis. Transportation 15(1):9–34

    Google Scholar 

  25. Kuo M, Liang G, Huang W (2006) Extension of the Multicriteria Analysis with pair wise Comparison under a Fuzzy Environment. Int J Approx Reason. N0.43: 268–285

  26. Li H, Zhao L, Huang R, Hu Q (2017) Hierarchical earthquake shelter planning in urban areas: a case for Shanghai in China. Int J Disast Risk Reduct 22:431–446.

    Article  Google Scholar 

  27. Liu J, Fan Y, Shi P (2011) Response to a high-altitude earthquake: the Yushu earthquake example. Int J Disast Risk Sci 2(1):43–53.

    Article  Google Scholar 

  28. Mase LZ (2018) Reliability study of spectral acceleration designs against earthquakes in Bengkulu City, Indonesia. Int J Technol 9(5):910.

    Article  Google Scholar 

  29. Mase LZ (2020) Seismic Hazard vulnerability of Bengkulu City, Indonesia, based on deterministic seismic Hazard analysis. Geotech Geol Eng 38(5):5433–5455.

    Article  Google Scholar 

  30. Mase LZ, Likitlersuang S, Tobita T (2020) Verification of liquefaction potential during the strong earthquake at the border of Thailand-Myanmar. J Earthq Eng:1–28.

  31. Momeni M (2007) New topics in operations research, 2nd edn. Tehran, university of tehran Issuance

  32. Norouzi Khatiri K, Omidvar B, Malekmohammadi B, Ganjehi S (2013) Multi-hazards risk analysis of damage in urban residential areas (case study: earthquake and flood hazards in Tehran-Iran). J Geography Environ Hazards 2(7):53-68.

  33. Omidvar B. Ganjehi S. Norouzi Khatiri Kh, Mozafari A, (2012) The Role of urban transportation routes in earthquake risk reduction management of Metropolitans. Case study: District No.20 of Tehran. International Conference "Urban change in Iran", 8-9 November 2012 University College Landon

  34. Pal A, Graettinger AJ, Triche MH (2003) Emergency evacuation modeling based on geographical information system data. In: Paper presented at the Transportation Research Board 82nd Annual MeetingTransportation Research Board

    Google Scholar 

  35. Poorzahedy H, Abulghasemi F  (2005) Application of Ant System to network design problem. Transportation 32:251–273.

  36. Rein A, Corotis RB (2013) An overview approach to seismic awareness for a “quiescent” region. Nat Hazards 67(2):335–363.

    Article  Google Scholar 

  37. Sattayhatewa P, Ran B (1999) Develops a dynamic traffic management model for nuclear power 16 plant evacuation, TRB. Annual meeting July 29

    Google Scholar 

  38. Southworth F (1991) Regional evacuation modelling: a state-of-the-art review. Oak Ridge National Laboratory, Energy Division, ORNL/TM-11740, Oak Ridge, TN

  39. Sherali HD, Carter TB, Hobeika AG (1991) A location-allocation model and algorithm for evacuation planning under hurricane/flood conditions. Transp Res B Methodol 25(6):439–452.

    Article  Google Scholar 

  40. Shoushtari AV, Adnan AB, Zare M (2016) On the selection of ground–motion attenuation relations for seismic hazard assessment of the peninsular Malaysia region due to distant Sumatran subduction intraslab earthquakes. Soil Dyn Earthq Eng 82:123–137.

    Article  Google Scholar 

  41. Sinuany-Stern Z, Stern E (1993) Simulating the evacuation of a small city: the effects of traffic factors. Socio Econ Plan Sci 27(2):97–108.

    Article  Google Scholar 

  42. Stewart JP, Douglas J, Javanbarg M, Bozorgnia Y, Abrahamson NA, Boore DM, Campbell KW, Delavaud E, Erdik M, Stafford PJ (2015) Selection of ground motion prediction equations for the global earthquake model. Earthquake Spectra 31(1):19–45.

    Article  Google Scholar 

  43. Taleai M, Mansourian A, Sharifi A (2009) Surveying general prospects and challenges of GIS implementation in developing countries: a SWOT–AHP approach. J Geogr Syst 11(3):291–310.

    Article  Google Scholar 

  44. Tamima U, Chouinard L (2016) Development of evacuation models for moderate seismic zones: a case study of Montreal. Int J Disast Risk Reduct 16:167–179.

    Article  Google Scholar 

  45. Tanapalungkorn W, Mase LZ, Latcharote P, Likitlersuang S (2020) Verification of attenuation models based on strong ground motion data in northern Thailand. Soil Dyn Earthq Eng 133:106145.

    Article  Google Scholar 

  46. Tzeng G-H, Cheng H-J, Huang TD (2007) Multi-objective optimal planning for designing relief delivery systems. Transport Res Part E 43(6):673–686.

    Article  Google Scholar 

  47. Uno K, Kashiyama K (2008) Development of simulation system for teh disaster evacuation based on multi-agent model using GIS. Tsinghua Sci Technol 13(S1):348–353.

  48. Wei L, Li W, Li K, Liu H, Cheng L (2012) Decision support for urban shelter locations based on covering model. Proc Eng 43:59–64.

    Article  Google Scholar 

  49. Wood N, Schmidtlein M (2013) Community variations in population exposure to near-field tsunami hazards as a function of pedestrian travel time to safety. Natural Hazards. 65(3):1603e1628

  50. Xu W, Ma Y, Zhao X, Li Y, Qin L, Du J (2018) A comparison of scenario-based hybrid bilevel and multi-objective location-allocation models for earthquake emergency shelters: a case study in teh central area of Beijing, China. Int J Geogr Inf Sci 32(2):236–256.

  51. Yi W, Özdamar L (2007) A dynamic logistics coordination model for evacuation and support in disaster response activities. Eur J Oper Res 179(3):1177–1193.

    Article  Google Scholar 

  52. Yu C-S (2002) A GP-AHP method for solving group decision-making fuzzy AHP problems. Comput Oper Res 29(14):1969–2001.

    Article  Google Scholar 

  53. Yu J, Wen J (2016) Multi-criteria satisfaction assessment of teh spatial distribution of urban emergency shelters based on high-precision population estimation. Int J Disast Risk Sci 7(4):413–429.

  54. Yueming C, Deyun X (2008) Emergency evacuation model and algorithms. J Transport Syst Eng Inform Technol 8(6):96–100

    Google Scholar 

  55. Zahran S, Tavani D, Weiler S (2013) Daily variation in natural disaster casualties: information flows, safety, and opportunity costs in tornado versus hurricane strikes. Risk Anal 33(7):1265–1280.

    Article  Google Scholar 

  56. Zaré M, Bard P-Y, Ghafory-Ashtiany M (1999) “Site Characterizations for the Iranian Strong Motion Network”, J Soil Dynamics Earthquake Engineering 18(2):101–123

  57. Zou L, Ren A-Z, Zhang X (2006) GIS-based evacuation simulation and rescue dispatch in disaster. Ziran Zaihai Xuebao J Nat Disast 15(6):141–145

    Google Scholar 

Download references


The authors would like to TEMPthank Dr. Babak Omidvar and Dr. Bahram MalekMohammadi for their useful Comments and suggestions to improve this research work.


The research project was part of the Disaster Management Program of Shiraz municipality. dis paper is a product of the project.

Author information




KNK designed the project, data analysis, contributed to writing and reviewing the paper. SG did the field work, data analysis, contributed to writing and editing the paper. The author(s) read and approved the final manuscript.

Corresponding author

Correspondence to Khadijeh Norouzi Khatiri.

Ethics declarations

Competing interests

There is no competing interest.

Additional information

Publisher’s Note

Springer Nature remains neutral wif regard to jurisdictional claims in published maps and institutional affiliations.



Fig. 18

Teh Results Derived from Implementing teh Model of Roads Length Index on teh Sample Data

Fig. 19

Teh Results Derived from Implementing teh Model of hazardous land Uses Index on teh Sample Data using Software

Fig. 20

Teh Results Derived from Implementing teh Model of Transportation Constructions Index on teh Sample Data using Software

Fig. 21

Teh Results Derived from Implementing teh Model of Population Density Index on teh Sample Data using Software

Fig. 22

The Results Derived from Implementing the Model of Buildings Vulnerability Index on the Sample Data using Software

Fig. 23

Teh Results Derived from Implementing teh Model of Safety Index on teh Sample Data using Software

Fig. 24

Teh Results Derived from Implementing teh Model of Volume of teh Population on teh Road Index on teh Sample Data using Software

Fig. 25

Teh Results Derived from Implementing teh Model of Main Indices on teh Sample Data using Software

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Ganjehi, S., Khatiri, K.N. Determination of emergency roads to emergency accommodation using loss analysis results. Geoenviron Disasters 8, 15 (2021).

Download citation


  • Earthquake
  • Emergency accommodation
  • Damage
  • Emergency evacuation
  • AHP