Journal of Natural Disaster Science, Volume 28, Number 2, 2008, pp91-98

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Dynamic Route Decision Model-based Multi-agent Evacuation Simulation- Case Study of Nagata Ward, Kobe
Yuling LIU＊, Norio OKADA＊ and Yukiko TAKEUCHI＊＊
Disaster Prevention Research Institute, Kyoto University, Kyoto, Japan＊＊
Graduate School of Global Environmental Studies, Kyoto University, Kyoto, Japan＊(Received for 11 Apr., 2008 and in revised from 23 May, 2008)

ABSTRACTIn this research, involving local people’s concerns in the decision on the evacuation route, a dynamic route decision model is proposed by explicitly considering group evacuation, landmarks & evacuation signs, and familiarity
with the local environment. A spatial relationship-dependent approach is used to measure the difference in residents regarding local knowledge. To understand the people’s decision-making process for evacuation routes, asurvey was conducted with members of the Zonta Club in Japan. Based on the survey, a prototype version of amulti-agent simulation (MAS) system is developed for the case area, Nagata Ward, Kobe.

1. BACKGROUND
Regarding evacuation routes, the shortest-route method is
widely used for evacuation simulation (Chen and Zhan, 2006;
Takahashi et al., 1989; Stern et al., 1996; Katada et al., 2004). The
GIS network module is also a popular way to analyze the evacuation route (Tachi et al., 2001). Moreover, some researches developed specific route choice models considering specific factors. For
example, leadership was taken into account in the research of
Ashibe (2006); Arai et al. (2003) then detailed the group-following
behavior under a total chaos situation to evacuate from underground shopping centers. The difference in walking speed among
age levels was taken into account and in this research, the following behaviors were separated into the autonomous type and the follow type. Ohata et al. (2007) used the minimum angle as the moving direction. In their research, agents were classified into two
types. One type is supposed to know where the shelter is, and theother is not. Communication between the two types is set as towhether the second type knows someone who belongs to the first
type. Through the simulation, they evaluate the configuration of
the shelters against tsunami disaster in Kushiro City. Besides
these, many research works has been conducted considering traffic
capacity during evacuation (Lu et al., 2003; Thomas and Justin,2003).
Unfortunately, few researchers consider the difference in environmental cognition of residents' evacuation behaviors in their simulations. As we know, an individual evacuation plan varies in thedecision on evacuation route, starting time of evacuation, intended
shelter, etc. In order to understand people’s concerns regarding
evacuation shelters and routes, a survey was distributed to the
Nagata Elementary School community in Nagata Ward, Kobe,
where residents suffered the Great Hanshin-Awaji Earthquake in1995 (Takeuchi et al., 2006). The result shows that almost all people know where the shelter is and more than 84 percent of people

deem the distance to a shelter as a very important factor among 45
valid answers. From the survey result, we also found that even residents who live in the neighborhood may take a different route to ashelter. This proves that individuals hold their own local knowledge that is formed by their daily life. It is necessary to involve
local people’s concerns in the decision on evacuation route in thesimulation.
In this research, a dynamic route decision model is proposed
to highlight the decision process with multi-factors including
familiarity, group evacuation, and landmark & evacuation signs. A
spatial relationship-dependent evaluation method is used to quantify residents’ familiarity with the local road network. For accompanying behavior, evacuees adopt both the autonomous type and thefollow type. Based on this model, a multi-agent simulation (MAS)system is developed using KKMAS [KKMAS homepage], a MAS
platform for Nagata Ward, Kobe.
2. DYNAMIC ROUTE CHOICE MODEL
In this research, the hazard is a flood inside a levee due to aflash storm. Barriers caused by inundation, flooded roads, and collapse of buildings may block off an evacuation route. We assume
that a barrier may happen anywhere among the road network andthat the blocked status may remain during the simulation. If a person is surrounded by barriers, it will be viewed as a failure of evacuation. So, the problem becomes how people make their own decisions in route searching.
The road network consists of blocks (cho) that can be subdivided into sub-blocks (chome) as shown in the black circle in Fig.
1. According to population data in each sub-block and the evacuation ratio, evacuees are evenly distributed within the nodes in each
sub-block (chome). For common nodes between each sub-block,the number of people gathering at two nearby sub-blocks will beequal. People are assumed to evacuate from each node (blue) that

KEY WORDS: dynamic route decision model, multi-agent, evacuation simulation, survey

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Fig. 1 Block and sub-block

Fig. 2 Junction type

represents a crossing, starting point, or terminal of a road in theroad network. People will move on the links connecting two
nodes.
Basically, there are four junction types as shown in Fig. 2.
For I, there is only one choice for people who stay at Node A. But
for II, III, and IV, possible choices increase from two to four. In
the current research, we treat the topographic condition as homogeneous.
Dynamic can be understood in two ways. On the one hand,a decision is made step by step when people move from one node
to the next reachable and appropriate node; on the other hand,interactions among evacuees are dynamic processes. The model
consists of the following sequential piecemeal decision-making
problems for each node j where an agent is assumed to make achoice of links to the next node.

{nmax U l j = S WiVil jl j ŒB ji =1}Here, U l j is the degree of efficiency for road choice making; iis each factor that affects people’s decision on the evacuationroute; lj is links connected with node j that is barrier free; n is thenumber of total factors considered (in this research, n is equal tothree); Wi is the weight of factor i; Vil j is the evaluation values ofreachable link lj regarding factor i; Bj is the set of links blocked by
the hazard; and B j is a complement of set Bj, so B j is a set composed of all reachable links connected with node j.
In the case study, three factors including familiarity, groupevacuation, and landmarks & evacuation signs are considered.
Landmarks & evacuation signs are usually marked in the form ofan arrow that suggests the direction toward a formal shelter. The
characteristics of group evacuation are described. Familiarity thendescribes the preference of people for different routes.
2.1 Priority among the factors
Robert and John (2003) proposed a conceptual framework formodeling variability in human behavior. Based on this framework,in the case study, evacuees are classified into two groups: one isresidents and the other is non-residents such as travelers, commuters, or foreigners who are not familiar with the local environment. Different from residents, these non-residents may even not
know where a shelter is and how to get to a shelter when a disaster
happens. The level of local knowledge leads to different priority
order in evaluating routes. Regarding the priority order of factors,
a survey was conducted with 20 members of the Zonta Club in
Kyoto. The following lists information on the survey.
・Date: Oct. 11, 2006
・Object: Members of the Zonta Club, a total of 20 persons
All members are females of 30 to 70 years old living in Kyoto.
Among them, 35% suffered slightly due to the Hanshin- Awaji
Earthquake in 1995 and 5% suffered flood disaster in 2004 by
Typhoon No. 23.
・Survey content: Previous disaster experience, evacuation activity, and shelter planning-related considerations
・Survey format: First giving a presentation to understand the context and then conducting the survey
The factors are listed as follows: familiar road as usual, thedirection neighbors are heading in, landmark to a shelter, roadwidth, moving distance, inundation situation, hazards along theroad such as landslide and others. In order to identify the two
types of agents’ concerns on the decision on an evacuation route,the following two questions are used in the questionnaire. One is,If a flood disaster happens, you decide to evacuate. Please selectfactors that you think are important for you in choosing the evacuation route and write the priority order, and the other question is,
When a flood disaster happens, how do you choose an evacuationroute if you are in a place that you are not familiar with? Please
select your choice and write the priority order.
For significantly high-weight factors see (a) Residents, and (b)Non-Residents in Fig. 3. This shows that for residents, significant
factors ranked in order of priority are familiar landmark & evacua-

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Fig. 3 Result of survey regarding the ranking of factors

Table 1 Weight of factors

tion signs, high risk around the road, and accompanying persons.
On the other hand, for non-residents, the ranking becomes landmark & evacuation signs, accompanying persons, high risk aroundthe road, and road width, in order of priority.
Based on the results shown in Fig. 3, the following equation isused to calculate the relative weight of factors.
Wi = r1i + 0.5r2i + 0.25r3i + 0.125r4 iHere, Wi represents the integrated weight value, andrji, j = 1, 2, 3, 4 represents the percentage of each rank regarding factor i respectively. Thus, we obtain the weights of each factor for different agents as shown in Table 1.
2.2 Evaluation of each factor
Each factor was evaluated as follows;・Landmark & evacuation signs
V0 l j = 1 if landmark & evacuation signs exist at link lj; otherwise,
V0 l j = 0
・Group evacuation
V1l j = 1 if more than three agents gather at link lj; otherwise V1l j = Group Member Num/3.0. Different from the research
by Arai K. et al. (2003), we set a threshold for people to decideto follow others.
・Familiarity
In this research, people are assumed to follow the directionthat conforms to the spatial relationship between the current position and the intended shelter. Fig. 4 shows the process of calculation of factor-familiarity with the local environment.
Taking a GIS space as an example, an evacuee now stays at
Node No. 260 and he intends to go to a shelter called Shinyo
Elementary School denoted by Node No. 237.
There are four choices: No. 32, No. 93, No. 119, and No. 118

as shown in Fig. 5. In terms of spatial relationship, outsidemeans that neither the X nor Y coordinate is inside the scope of therectangle that is surrounded by the current node and destinationnode, so the evaluation value is set to -1 so that No. 32. insidemeans that both the X and Y coordinates are within the scope ofthe rectangle and the evaluation value is set as 1. Partial meansthat either the X or Y coordinate is within the scope of the rectangle. In this case, the evaluation values depend on the angle among
the judged node to the current node to the destination node asshown in the above sketch map. V2 l j , the evaluation value of familiarity, is defined as the cosine of the angle.
The smaller the angle is the closer to a straight line between
the current node and the destination node the moving direction is.
Correspondingly, the evaluation value becomes bigger. The
method effectively keeps the distance to a destination within a controllable scope of changes. When more than one link has the maximum evaluation value, the probability function will be used tochoose one among all backups. From a statistical point of view,
the selection process can be viewed as a random decision making
process for an agent.
2.3 Definition of agentsIn the case study, from a behavioral point of view, agents areclassified into residents and non-residents. This study area is readcircle area in Fig. 6. This area is sevens local community(Chome). Table 2 shows each Chome’s household and population.
Every Chome’s formal shelter is Shinyo Elementary School . As
described above, residents are supposed to be familiar with thelocal environment and non-residents are those who are new to theenvironment. So, the difference in local knowledge leads to different evacuation behaviors between them. From an evacuation
capacity point of view, agents are classified into old, young,and child types as shown in Table 3. As a parameter, the ratio of
residents to non-residents is used to calculate the number of non-94

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Fig. 4 Caluculation flow chart of dynamic route decision modelingFig. 5 Sketch map of calculation

residents and the detailed type of non-residents is randomly set in
the simulation.
A household is viewed as the simulation unit and the simulation step is defined as one second. In the simulation, we supposethat all agents will evacuate to a shelter on foot, and walking speed
depends on the type of household. For the old type, the walking
speed is set as 0.8 m/s, for the young type, it is set as 1.4 m/s,
and for the child type, it is set as 1.0 m/s. When the evacuation
ratio changes, the total number of evacuees for each household
type will automatically be calculated and put in the two-dimensional simulation space.

Fig. 6 Map of the case area in Nagata Ward, Kobe

2.4 Simulation model
In this case area, there is only one formal shelter called Shinyo
Elementary School. Fig. 7 shows active rule of residents (a) andDYNAMIC ROUTE DECISION MODEL-BASED MULTI-AGENT EVACUATION SIMULATION - CASE STUDY OF NAGATA WARD, KOBE

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Table 2 Spatial region scope of the case area

Table 3 Agent type regarding population structur

non-residents (b). When agents arrive at the node that represents
the position of Shinyo Elementary School, the evacuation isviewed as complete. Interactive communication between residents
and non-residents is considered in this research. If a non-resident
decides to follow a resident, then a group is created as a moving
unit. In this case, the two models are combined and the group will
follow the decision of the resident, the leader in this group. Whenthe group arrives at the target node, every member in this group isdeemed to have finished evacuation.
2.5 imulation interface
In this research, based on KKMAS, a simulation system isdeveloped. Fig. 8 shows the main simulation interface. In thissimulation, the variables include the block ratio, evacuation ratio,
and ratio of residents to non-residents. In this case area, we
assume the hazard to be a flood inside a levee by a flash storm andthat the hazard remains at the same level with time by setting the
spatial distribution of the flooded area before the simulation. The
block ratio represents integrated environmental change by flood
water, landslide hazard along the evacuation route, collapsed buildings, etc. Evacuees cannot pass through blocked roads and have toselect another reachable road as the forward direction.
We assume that people will evacuate at the same time after

receiving the first evacuation instructions. According to statistical
population data, evacuees are evenly distributed in each Chome.
Correspondingly, the number of each type of household is calculated. Fig. 9 shows how the evacuation completion percentagechanges with time.
2.6 Simulation results
As said above, there are three parameters in the current simulation system including evacuation ratio, block ratio, and ratio of
residents to non-residents. In Case 1, the evacuation ratio is set at
30% and the ratio of residents to non-residents is set at 50:1. We
compare the evacuation situation when the road is in a different
block ratio respectively of 0%, 10%, and 50% (Fig. 10(a)). The
simulation is run 10 times and the average results of evacuation
completion are shown in the following charts.
The results show that the time consumed for evacuation completion increases with the block ratio changing from 0% to 10% to
50% (Fig. 10(b)). And they show a small change from the beginning but a rapid increase when the block ratio changes from 10% to50%. And statistical successful evacuation also changes in a similar way. From 0% to 10%, there is no obvious change, but when
the road network becomes worse, successful evacuation decreases
rapidly.

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Fig. 7 Residents and Non-residents’ activity rules

Fig. 8 Simulation interface

In Case 2, the evacuation ratio is respectively set at 10%, 20%,
30%, 40%, and 50% with the block ratio set at 10% and the ratio of
residents to non-residents set at 50:1. Fig. 11 shows the simulation
results. When the evacuation ratio changes from 10% to 30%, theconsumed time shows a slow increase but it becomes almost thesame from 30% to 50% (Fig. 11(a)). Successful evacuations show
a direct proportional increase with the evacuation ratio. From theabove two cases, we also found that among all successful non-residents, 100% of them followed a group (Fig. 11(b)). It means that
communication with residents plays a very important role in guid-ing non-residents to a successful evacuation.
The MAS system has been used as a medium in communication activities with people from the Zonta Club in Kyoto regarding
evacuation issues. Most of the members showed great interest in
the evacuation simulation, and the simulation process stimulated
them to think about evacuation problems that they might face.
3. DISCUSSION AND CONCLUSION
In this research, involving local people’s concerns in the deci-

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Fig. 9 Chart of the simulation process

Fig. 10 Simulation results in Case 1

Fig. 11 Simulation results in Case 2

sion on the evacuation route, a dynamic route choice model is proposed considering group evacuation, landmarks & evacuation
signs, and familiarity with the local environment. A spatial relationship-dependent approach is used to measure the difference inresidents regarding local knowledge. Compared to some popularroute-searching models such as the shortest-route method, the great
advantage of the dynamic route choice model is that it explicitly

involves people’s ideas in the model and highlights individual differences in evacuation behaviors. A survey focusing on the routedecision process was conducted to identify how people evaluate theimportance of each factor, which provides us with a better understanding of people’s evacuation behaviors. Based on the model, a
prototype version of the multi-agent simulation system is developed with Nagata Ward, with Kobe as a case area.

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Y. LIU, N. OKADA AND Y. TAKEUCHIIn the current simulation, in place of analysis of the flooding
process, the block ratio, a static parameter, is used to measure damage by flood disaster and all hazards are viewed as uniform andlevel. We intend to simulate people’s evacuation behavior under agiven hazard situation. But the lessons from pre-flood disasters
showed us that many people died of sudden flooding water and soil
collapse around the evacuation route during their evacuation.
Additionally, one of the main reasons for non-evacuation is thatpeople are greatly concerned about hazards along the evacuationroute. So, as the next step, we will make efforts to integrate a floodmodel into the MAS simulation and detail the types and levels of
existing hazards along each evacuation route.
Mutual help between residents and non-residents is consideredin this research. Self-help and mutual help among residents, especially those vulnerable people for whom it is difficult to evacuateby themselves, however, are severe difficulties facing Nagata
Ward. So, in order to empower the community, collaborative
activities such as gaming, questionnaires, and workshops havebeen conducted with local residents. Based on feedback from
respondents, simulation rules and models will be further improvedand extended to incorporate more specifically residents’ evacuation
behaviors and concerns.
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