LNAI 6797 - Agent-Based Simulation System for ... - Springer Link

Agent-Based Simulation System for Supporting Sustainable Tourism Planning Dingding Chao, Kazuo Furuta, and Taro Kanno Department of Systems Innovation, Graduate School of Engineering, The University of Tokyo 7-3-1 Hongo, Bunkyo-ku Tokyo, 113-8656, Japan {chao,furuta,kanno}@sys.t.u-tokyo.ac.jp Abstract. The expanding tourism market, in particular of East Asia, has drawn great interests and has raised a series of significant issues for researchers and planners in sustainable development. Unsustainable tourism development caused problems such as loss of natural resources, conflicts between tourists and local residents, and so on. This research intends to understand the development process of Recreational Business Districts (RBDs) in tourism areas and to provide a framework for supporting sustainable tourism development by analyzing interactions between tourists and RBD. An AgentBased Simulation (ABS) combined with Geographic Information System (GIS) provides planning supports to tourism bureaus and policy makers to help them assess possible future development plans in tourism under certain scenarios. Keywords: sustainable tourism development, recreational business district; planning support architecture; agent-based simulation; GIS.

1

Introduction

Tourism has gained worldwide significance, becoming one of the largest industries in many countries (Clancy,1999). The rapid increase in the tourism market, in particular of East Asia, has drawn great interests and has raised a series of significant issues for researchers and planners in sustainable development. One of the most significant problems in tourism planning is its complex nature, and it makes the decision-making difficult. Clearly, new directions in tourism researches are required (Carter and et al., 2001) to support the accelerating demands for multi-disciplined studies in this field. Agent-Based Simulation (ABS) is a technique to deal with complex processes among various system components; it is expected to open the door for deeper understanding of tourism and the development of Recreational Business Districts (RBDs) (Malaka and et al, 2000) offering a way of linking multi-disciplinary theories. A planning support architecture based on a combination of ABS and Geographic Information System (GIS) can be used to examine and explore the spatial-temporal data at the macroscopic or individual level, which reflect patterns of changes and effects of tourists’ activities as well as other potential aspects, to study a system of RBD. It also provides new perspectives for decision makers to evaluate different development plans. T. Onoda, D. Bekki, and E. McCready (Eds.): JSAI-isAI 2010, LNAI 6797, pp. 243–252, 2011. © Springer-Verlag Berlin Heidelberg 2011

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A general term “sustainaable tourism” is widely used among discussions on tourrism these years. Accurately deefining this term is difficult, however, due to its muultidimensional nature. One off the most significant factors to affect sustainablilty of R RBD is touirsts loads. In Bulter’s widely accepted destination (RBD) life cycle model, the tourism destination followss a cycle in terms of the number of tourists visiting there. The turning point of the liffe cycle curve comes about, because the number of tourrists exceeds the carrying capaacity of the area and causes decline in environmenntal attractions. Other factors, such as interests conflicts between tourists and reideents, may also impact sustainablle tourism (Carter and et al., 2001). The exsiting geneeral models, however, are not sufficient to explain the complex nature of the tourrism system, especially the inteeractions on the microscopic level were not substanntial enough so that the theoriess may need further exploration before applied to practtical tourism planning processes. This research intends to understand the development process of RBDs in tourrism areas and to provide a fram mework for supporting sustainable tourism developmentt by analyzing interactions betw ween tourists and RBD. Agent-based simulation combined with GIS under the framew work provides planning supports to tourism bureaus and policy makers to help them m assess possible future development plans in tourism unnder certain scenarios. Hakone is chosen as the target study area in this work, as it is onee of the most famous tourism districts d in Japan. The district, however, also faces soome problems in environment preservation, p contracting domestic market, and decreasee of local population.

Fig. 1. Sustaainable Tourism Indicators (After Becker, 1999)

2

Tourism System Framework F

In order to simulate behavio or of a tourism system and to assess development plans for sustainable tourism, undersstanding on the processes of both the macroscopic and the microscopic level is requiired, and appropriate indicators must be chosen for this purpose. The indicators can c generally be divided into four groups: econom mic,

Agent-Based Simulation System for Supporting Sustainable Tourism Planning

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environmental, social, and multi-dimensional indicators. The indictors were reprojected also according to their temporal and spatial scales into the matrix as shown in FIGURE 1 (Becker, 1999). Hakone is a destination with a total area of 92.82km² and several RBDs. Its tourism development planning strategies are taken out on seasonal and yearly basis. According to the application domain of our study, Hakone, falls within the range of “RBDs-Destinations” and “Seasons-Years” so that the time scale and appropriate indicators are chosen from the matrix for the simulation: population of the local residents, number of tourists, land use change data, and the service quality perceived by tourists.

3 3.1

Simulation Model Overview

The simulation model integrates ABS and GIS to explore phenomena of change in land useland use/cover, population of residents, and number of tourists in Hakone. Spatial and statistical data were processed using ArcGIS 9.3 (ESRI, 2008) and distributed into several layers of the model to represent the key features that the agents share and interact with. We obtained the original data of the land use, road network, and public facilities provided by National Land Information Office of Japan and converted them into different sub-layers: Land Type Layer, Transportation Layer, Tourist Spot and Public Facility Layer, and Tourists and Residents Layer. All the layers are re-projected onto the GIS grid plan and programmed in Netlogo 4.1 (Wilensky, U. 1999). Many previous works have been done using agent-based models to study land useland use change (Parker, et al., 2002; Batty, 2005; Li and Liu, 2008), however, compared with the target study areas of the past researches, tourism destinations are usually more developed and have many constraints in future development. We adopted the model of a previous research on rural-urban land useland use change to capture the changes from preserved land to building lots (Li and Liu, 2008; Wu, 2002). Their model used ABM combined with GIS to examine the urban development patterns and predict the future trends. Although some componets of the tourism system are similar with their urban development system such as land use change and residents’ assesment of the utility and movements, it differs from urban development in the following aspects : It is not nessessary that the residents in tourism system have to constantly moving from one place to another in order to find a satisfying one to live. Tourists are introduced into the system who have different behavior and standards for evaluation with the residents.The lands in tourism systems have more constrains to develop as the related laws and regulations in RBDs are stricter than thoses in common urben planning. Modifications of the model were made to reflect the differences between these contexts to better capture the characteristics of tourism system. 3.2

Agents

There are three major types of agents used in the simulation: tourist agents, resident agents and land agents, which are coupled with the corresponding spatial features in

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different layers. In addition, there is a government agent, which is an abstract agent and not coupled with any spatial features. Tourist agents distributed in the target areas evaluate the places of interests and select the most attractive one to visit. Resident agents located in the living districts evaluate their living environment and decide to stay longer or move away. Activities of these two agents will change the development potential of a certain location. The government agent will have much more determinate influence on change in land use, which is, however, subject to behavior of tourists and residents. 3.3

Agent Behavior

Tourist Agent This research adopted the method of previous studies (Li and Liu, 2008; Wu, 2002) for calculating utility, attractiveness and development potential of lands. Their model is not for tourism development, however, but for urban development; some modifications have been added for applying it to the tourism domain. The tourist agent was introoduced into the system to interact with both the environemnt and the residents, and have different parameters to assess the attractiveness, differnt from those for ultilities assessment used by the residents. Surrounding environment ′ : The tourist agent checks the patch of interest with its 8 surrounding neighbors and counts the number of patches ′ that are classified as a lake or a forest ′ Availability of public facilities ′ : The tourist agent checks if there is any “public facility” within a certain distance from is the number of patches with facilities. the location of interest. Parameter ′ : Availability of tourism spots The tourist agent checks if there is any “tourism spot” within a certain distance from is the number of patches with tourism spots. the location of interest. Parameter : Availability of transportation ′ The tourist agent checks if there is any “road” within a certain distance from the is the number of patches with roads. location of interest. Parameter ′ Number of people ′ : The tourist agent counts the total number of tourists and residents of interest and its 8 surrounding neighbors.

′

in the patch

The tourist agent evaluates the attractiveness of RBD by the following formula. For Tourist Agent the attractiveness of Land is: ,

where N is the number of attributes,

′

′

′ is the weight for each attribute and

Agent-Based Simulatio on System for Supporting Sustainable Tourism Planning

′

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1

The tourist agent then co ompares the attractiveness of other patches within a certtain distance with the patch of its i present location and decides to visit another place iff its attractiveness exceeds the present position by 10%. The number of tourists w will therefore decrease greatly in n a patch where the attractiveness is of a local minimum m.

Fig. 2. Agents Behavior and Flow of the Program

Resident Agent Each resident agent evaluattes the utility of land agents within a certain distance frrom the following parameters. nt : Surrounding environmen The resident agent coun nts the number of patches that are classified as a llake or a forest : Availability of public faccilities The resident agent check ks if there is any “public facility” within a certain distaance from the location of intereest. Parameter is the number of patches with pubblic facilities. Availability of transportaation : The resident agent check ks if there is any “road”within a certain distance from the location of interest. Parameeter is the number of patches with roads. Number of people :

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The resident agent counts the total number of tourists and residents patch of interest and its 8 surrounding neighbors.

in the

The resident agent evaluates the utility of residence by the following formula. The utility of Land for Resident Agent is: ,

where M is the number of attributes,

is the weight for each attribute and 1

The resident agent then decides whether to stay at the present residence or to leave there by comparing the present utility , of the residence with that of the past evaluation. , It decides to leave the present patch if the utility drops over 10%. When the resident agent leaves the present location, it leaves there forever. Land Agent The land agent calculates the development potential parameters. Land development constraint :

from the following

The preservation conditions of lands in Hakone are generally categorized into 3 groups: business and living areas, preserved areas, and others not specified. Each has a certain level of development constraint. The conditions of each land agent, which is obtained originally from the land use data in FIG. 4.1, are assigned corresponding values at a 0 to 1 scale. Distance to transportation : The land agent checks the distance to the nearest road, and is a score evaluated from this distance. Distance to public facility : is a score The land agent checks the distance to the nearest public facility, and evaluated from this distance using the same scoring standards as : Percentage of developed lands in the neighborhood B Parameter B is the percentage of developed land agents in the neighborhood of a certain distance. Number of visitors B : Parameter B is the number of tourists who visited the location. The development potential of a land agent is calculated from the following formula. * *B B


249

Government Agent The government agent performs the following two tasks. It calculates the numbers of tourists and residents. The government agent observes the whole process and calculates the income from tourists and residents in each turn and decides whether lands can be developed. The government agent determines the construction rate Rcons of a land depending on the development potential. A higher development potential results in a higher construction rate.

4

Test Simulation and Case Study

The weights for evaluating the attractiveness and utility of lands were adopted first from the previous studies and then modified according to the nature of the target area of this study, Hakone. Some test simulations were then performed to adjust the model parameters so that simulation results of the average number of tourists per day and the average number of residents of year 2000-2009 well match the corresponding historical data.

Fig. 3. Result of Test Simulation

4.1

Simulation Conditions

A case study of test simulation was performed to demonstrate the usefulness of the proposed ABS for sustainable tourism development planning. Two simulation scenarios (Plan-A and Plan-B) were set up to simulate different possible future developments of RBDs in Hakone. It is assumed that the government has different tolerance levels R towards land use change. All the other conditions were similar except the tolerance level for construction rate of the government agent, which affects the extent of development in RBDs allowed by the government. A higher tolerance level results in a higher possibility that the government would allow land agents to develop their lands for tourism business. Plan-A represents the conditions in the real world situation with a relatively low tolerance of 0.16; the government is very conservative in using land resources for new constructions. Plan-B is an imaginary plan that has a relatively high tolerance of 0.80; the government takes a more aggressive position for development of RBDs. The time-span of the both cases is 100 steps, and tourist and resident agents have 30 turns

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of actions in each step. Eacch action represents agent’s behavior in one day of the rreal time; 100 steps correspond to 3,000 days. The tourist agent can choose three differrent places to visit in a day, while w the resident agent can have one chance to deccide whether to stay or leave the t present residence. In 2009, 13,489 residents livedd in Hakone and the total number of tourists was 19,649,000. As most of the tourists w were med that the tourists will not stay longer than 24 hourss. It one-day visitors, it is assum means therefore about 53,8 839 people visited Hakone per day. The number of touurist agents was set 5,384 in thee both cases, which means each tourist agent representss 10 real tourists. The number of o resident agents was set 1,349, which means each touurist agent represents 10 real resiidents, correspondingly. 4.2

Simulation Results

ulated result of land use change after taking Plan A andd B. FIGURE 4 shows the simu As expected, a higher tolerance level resulted in a higher development rate, greaater changes in land use and loss l of natural resources. It was observed also from the simulation that areas with h a higher density of transportation and other tourrism services developed faster th han other areas, which well match the real world situatioon.

Fig. 4. Laand use changes resulted from Plan A and B

Fig. 5. Number N of agents resulted from Plan A and B


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FIGURE 5 shows the simulated results of the number of tourists and residents for Plan A and B. As a higher tolerance level resulted in a higher developing rate, it is expected that Plan B is likely to result in a larger capacity for tourists and to attract more people. However the numbers of tourists dropped after a short time of rising, reflecting that the “deterioration stage” of Butler’s Tourism Life-Cycle is likely to happen for the over-developed tourism business districts. And residents have increased after the number of tourists starting to decrease, reflecting the phenomenon of the conflicts between tourists and residents in Tisdell’s work. 4.3

Discussion

The simulation revealed that a high tolerance level for constructions resulted in intense conflicts between tourists and residents, which will largely influence the sustainability of RBDs. The residents and the tourists share the same environment but each has different expectations and evaluation criteria’s of it. The tourism life-cycle can be reflected by the number of people (in case of the RBDs, including both tourists and residents) and the rising number of residents while the RBDs go to a “deterioration stage” reveals the possibility of a conflict between tourists and residents. From the case study the simulation results accented social aspects of sustainable tourism. Both tourists-residents conflicts and destination life-cycle exist during the development process of the destination, but not necessarily at the same time. The tourists-residents conflicts happen as a result of increasing number of tourists come to the RBDs and the living environment of the residents thus degrade the utilities of the land for the residents. Agent Based Simulation can replicate this interaction between tourists and residents to reflect the destination life-cycle on the macroscopic level.

5

Conclusion

Simulation has been demonstrated to reveal the influence of different development scenarios in the future of RBDs.The simulation reveals that the high tolerance of constructions will result in intense conflicts between tourists and residents, which will largely influence the sustainability of the Tourism Business District. In addition, from the result of the simulation, the social aspect of sustainable tourism is accented. To achieve sustainable developing, the conflict between tourists and residents should be reduced by better balancing the interests from both sides during the policy-making process. The research demonstrated that agent-based simulation approach can be applied to building the Planning Support System for sustainable tourism development. The problem of the vague definition of sustainability is avoided by well established pool of indicators and their corresponding time and spatial scale. The analysis on both macroscopic level and individual level are involved in the simulation to deal with the complexity of the tourism system.

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