Journal of China Tourism Research
ISSN: 1938-8160 (Print) 1938-8179 (Online) Journal homepage: http://www.tandfonline.com/loi/wctr20
Agent-based Modeling of the Spatial Diffusion of Tourist Flow—A Case Study of Sichuan, China Rongxu Qiu, Wei Xu & Shan Li To cite this article: Rongxu Qiu, Wei Xu & Shan Li (2016): Agent-based Modeling of the Spatial Diffusion of Tourist Flow—A Case Study of Sichuan, China, Journal of China Tourism Research, DOI: 10.1080/19388160.2016.1160847 To link to this article: http://dx.doi.org/10.1080/19388160.2016.1160847
Published online: 29 Mar 2016.
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JOURNAL OF CHINA TOURISM RESEARCH, 2016 http://dx.doi.org/10.1080/19388160.2016.1160847
Agent-based Modeling of the Spatial Diffusion of Tourist Flow—A Case Study of Sichuan, China Rongxu Qiua, Wei Xua and Shan Lib
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a Department of Geography, University of Lethbridge, AB, Canada; bDepartment of Geography, East China Normal University, Shanghai, China
ABSTRACT
ARTICLE HISTORY
This study presents an agent-based model that simulates the tourist flow diffusion process of a tourism destination system. The model begins by building simple tourist agents, whose main activities are selecting tourist spots and organizing tourism schedules given their unique financial and time constraints. Next, the model utilizes tourist spot manager agents, whose responsibilities are alerting tourists about crowdedness and/or posting advertisements to attract tourists. A regional tourist flow model was developed to simulate the decisions and behaviors of these agents and predict tourist flow direction and distribution. The model was applied to the Sichuan province in China, which has 41 Class 3A and above tourism spots. Under different decision-making scenarios, the results of the model simulations showed that northwest, northeast, southeast, and southwest are the four main tourist flow directions and that crowd-alert would assist in effectively regulating tourist flow and tourism distribution patterns except when long-distance tourists dominate.
Received 27 November 2014 Accepted 18 November 2015 KEYWORDS
Agent-based modeling; artificial intelligence; tourist flow; spatial diffusion; tourism model 关键词
基于代理模型; 人工智能; 旅游流; 扩散; 旅游模型
旅游流扩散的基于代理模型—四川省的案例研究 摘;要
本研究提出了一个模拟旅游地系统的旅游流扩散基于代理模型 (agent-based model)。此模型先建立旅游代理，其主要活动是 根据预算和时间限制来选择旅游景点和组织行程。此外，模型 利用旅游景区代理，提醒旅客有关景区的拥挤情况及宣传。此 区域性旅游流模型模拟决定和行为， 并预测旅游流向和分布。本 文套用此模型于拥有 41 个 3A 级或以上的旅游景区的四川省。 在不同假设情况下，模拟结果表明，四个主要的旅游流向分别 为西北、东北、东南及西南。当远程旅客主导，人群预警亦有 助调整旅游客流和旅游流向。
Introduction The continuous growth of the global economy has resulted in tourism becoming the primary mode of recreation since it provides people with the opportunities to enjoy leisure, derive cultural experiences and enrichment, and develop businesses. According CONTACT Rongxu Qiu
[email protected]
© 2016 Informa UK Limited, trading as Taylor & Francis Group
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to the World Tourism Organization (2000), the 2020 estimate of global international travelers is two billion. However, the volume of available and developed tourism resources is limited. Excessive tourist volume places tremendous stress on tourist spots and results in landscape damage, traffic congestion, environmental pollution, low tourist satisfaction, and resource waste (Mathieson & Wall, 1982; Pignatti, 1993; Ries, 1996). Moreover, the imbalance in regional tourism resources and development also results in uneven tourist flows and thus adds much more pressure to sustainable scenic spot management. Research into the dynamic process of tourist flow is vital to the understanding of the spatial interactions between tourists and tourist spots. Information generated from this study can assist in pinpointing hot spots and bottlenecks along tourism routes (Zhong, Zhang, & Li, 2011). Such information is fundamental to tourism planning, economic development, and environmental protection programs that aim to promote sustainable regional tourism systems (Huang & Wu, 2012; Pearce, 1995). Until recently, researchers have developed various types of models to investigate the patterns of tourist flow and their interactions with the tourist environment (Higham, Holt, & Kearsley, 1996). For example, Mariot developed the three travel routes model—travel in, return, and leisure—to delineate the link between a tourist’s home and tourist spots (Pearce, 1995). Campbell (1967) further advanced the travel routes model by including recreational and vocational forms of travel. Mings and McHugh (1992) interviewed 600 respondents and developed a typology model of tourist flow to identify the spatial configuration of travel to the Yellowstone National Park. These modeling exercises assisted in delineating the overall tourist flow configuration by investigating the role of the socio-psychological behavior of travelers, economic conditions, and environmental surroundings in shaping tourist-related geography. These early models were largely based on macro-level tourist flow theories and mostly rely on an analysis of static data and did not explain the dynamic tourist flow processes that interact with large-scale destination areas. Therefore, the first-generation tourist flow models are difficult to apply in practical tourism planning and management that must address dynamic and evolving tourism-related activities and processes. Early attempts were made to develop dynamic tourist flow models. For example, Miossec developed a dynamic model to investigate the structural evolution of a regional tourism system through time and space (Pearce, 1981). However, the model primarily focuses on the statistical characterization of tourist flow in a destination area and generalizes the macro-level tourist flow through summary statistics of the overall characterization of tourists. The model also does not describe the interactions between individual tourists and a tourist spot environment in a complex tourism system. Given the development of computer science and artificial intelligence technology in recent decades, researchers have started to integrate computer simulation technologies into tourist flow research. Gimblett, Richards, and Itami (2001), for example, developed an agent-based modeling (ABM) method to simulate the movement of tourists at a tourist spot. O’Connor, Zerger, and Itami (2005) used artificial intelligence coupled with a geo-temporal method to track and analyze tourists’ behavior. Both of these studies tested different tourist spot management policies and found that computer simulation is a useful tool for tourism planning and tourist spot management. However, most computer simulations on tourist flow to date focused on a single tourist spot (Gimblett et al., 2002; Itami, MacLaren, Hirst, Raulings, & Gimblett, 2000; Lawson,
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Itami, Gimblett, & Manning, 2006; Manning, Valliere, Wang, Lawson, & Newman, 2002). Simulating the tourist flow of a regional tourism system that addresses multiple tourist spots is rare. Johnson and Sieber (2011) developed an agent-based model to simulate multi-spots tourism in Nova Scotia, Canada. However, their research primarily focused on external influences, particularly the American economic crisis and its effect on local tourism development and provided limited utility to the management of local tourist activity. This study investigates the dynamic tourism process by integrating micro-level tourist behaviors with a macro-level tourist flow structure in a regional tourist spot system. By investigating simultaneously the tourist spot-selection and route-organization behavior of tourist agents and the tourist spot-management behavior of tourist spot manager agents, this study develops an agent-based simulation model to simulate the diffusion process of tourist flow in a defined spatial and temporal space. The model is then applied to 41 individual tourist spots in the province of Sichuan in China. This study is organized into six sections. Following the introduction, the second section reviews the literature on complex tourism systems and agent-based modeling. The third section describes in detail the tourist flow diffusion simulation model including the main components and key technologies used in its development. The fourth section describes the study area and the data used in this study. The results of three scenario simulations that rely on the model and research inputs are discussed in the fifth section. The final section provides a discussion and the conclusion.
Complex Tourism Systems and ABM: A Review From the systems point of view, two types of systems exist in nature: simple systems and complex systems (Weaver, 1948). Simple systems are defined using a limited number of elements, and their states and interactions are frequently modeled using deterministic mathematical methods. In contrast, complex systems are driven by a large number of variables and cannot be analyzed using traditional statistics that are constrained by their limited computation abilities (Wilson, 2000). One important feature of a complex system is that it frequently exhibits orderliness as a result of the internal interactions of a large number of heterogeneous objects or elements (Durlauf, 1998). Typically, such orderliness is associated with the surprising, unanticipated, or emergent behavior produced by the system’s elements through micro-level operations (Batty, 2009). To date, most studies on tourist flow have focused on the macroscopic features of a destination area and have built mathematical models to analyze the volume and structure of the flow of tourists. However, the results of these studies are too abstract to be applied to the actual management of tourism destinations and scenic spots. Similar to most complex social processes, tourism destination regions are considered dynamic and evolving complex systems (Butler, 1980; Mill & Morrison, 2002). According to Mill and Morrison (2002), a complex tourism system essentially consists of four parts: the tourist spot, marketing of the tourist spot, demand for the tourist spot, and travel to the tourist spot. Leiper (1990) argued that a tourism system is composed of the tourism-generating region, industry, destination region, transit route, and tourist. From a computer simulation modeling point of view, arguably a tourism system
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primarily constitutes several entities, including individuals such as tourists, tourist spot managers, and tourist spot stakeholders; and organizations such as travel agencies and local or central governments. These various entities interactively engage in numerous activities or services such as touring, travel coordination, accommodation, transportation, tourism marketing, and promotion. The environmental elements, which include tourism attractions, road networks, and landscapes, provide contextual foundations for both actors and related activities. On the basis of this view, tourism systems operate in a dynamic environment in which various entities or agents and environmental conditions interact with one another through touring and management activities (Leiper, 1990; Mill & Morrison, 2002). The dynamic and unpredictable interactions of these system elements at the micro level may lead to the emergence of self-organized new phenomenon such as varying tourist flows, given the nature of a complex system (McDonald, 2006). Simultaneously, entities or agents adapt to and match the changing environmental conditions that emerge and are driven by the dynamic processes and changes within the system (Plog, Ritchie, & Goeldner, 1987). In a complex tourism system, the activities of the individual tourists may reveal the behavioral characteristics of the dynamic complexity of tourism systems. These individual tourists decide on the tourist spot and duration of the visit on the basis of their available time, financial budget, and personal preferences. Individual tourism decisions at a micro level can lead to a complex tourist flow direction and scale at a macro level, making it difficult to understand tourism systems using traditional static mathematical and statistical methods (Batty, 2009). In contrast, dynamic modeling techniques such as ABM are designed to delineate simple individual behavior at a micro level and then dynamically aggregate the individual behavior from the bottom up to complex large crowds at a macro level to derive a spatial configuration of tourist flows (Axelrod & Tesfatsion, 2006). The ABM was developed in the 1980s when access to computing power started to increase. The method is used to represent elemental or individual-level objects and populations and their changes through space and time. One of the conspicuous characters of an agent-based model is its ability to generate emergent spatial and temporal patterns at the macro level from the micro level (Batty, 2009). Agents are autonomous entities or objects that exist in a defined environment, sense the environment, and then act on it (Franklin & Graesser, 1997). A typical ABM comprises three components: agents, environment, and rules. In ABM, homogeneous and heterogeneous agents interact within the environment through various rules and receive feedback from the environment; agents also communicate, collaborate with, or compete against each other. ABM has proven its capabilities to simulate dynamic processes in a wide range of research fields and has been successfully applied in the areas of economic, social, and environmental sciences (Baggio, 2008). Bonabeau (2002) used ABM to simulate the optimal evacuation process from stampedes in an emergency situation; Kim and Batty (2011) developed an agent-based model to simulate the spatial and temporal process of urban growth; Deadman and Gimblett (1994) modeled the people-environment interactions involved in forest recreation activities. All of these applications demonstrated the superb ability of ABM in understanding dynamic processes and solving complex problems.
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Arguably, ABM is particularly appropriate to represent individual tourists’ decisionmaking processes and how these processes lead to the complexity of the entire tourism system. By modeling tourism processes, planners and researchers can experiment with the dynamic processes of a tourism system, attempt different management strategies, and identify the best intervention options (Johnson & Sieber, 2011). Moreover, through integration in a geographic information framework, tourism ABM can also model the complex spatial and temporal process of tourism flows (Benenson & Torrens, 2004). For example, one or more attractions may frequently exist along a specific tourism route populated with crowed tourists, whereas other routes only attract a few visitors. Alternatively, on other occasions, one tourism attraction may have a large number of tourists at one time but very few at other times. Aggregated statistical tourism data may not assist in revealing such complexities embedded in tourist flow processes; however, dynamical simulation using ABM assists in identifying such spatial and temporal uneven distribution of tourists. This study provides an example of the use of ABM based on complex system theory to simulate the dynamic tourist flow processes among tourist spots in the Sichuan province in China.
Tourist Flow Diffusion Simulating Model Agents of Complex Tourism System Tourists are the fundamental building blocks of complex tourism systems; hence, tourist agents dominate tourism ABM. Mobility is a key characteristic of an individual tourist. Starting from making tourism plans to joining different types of touring activities, tourist agents perceive and react very proactively to the tourism environment and adapt their behavior voluntarily or involuntarily to the environment (see Figure 1). The entire travel process is highly goal-oriented, particularly for long-distance travelers.
• • •
Orign Income Time
• • •
Location Grade Target
Environment
Figure 1. Context dependence and interaction among agents and environment.
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Long-distance travelers usually arrange their traveling schedule and financial resources well in advance of starting a tour, given the unfamiliarity of their destination areas. For example, they book hotels and purchase transportation tickets several days or months ahead of their actual travel. Therefore, to long-distance tourists, once a goal is set, it must be achieved because the cost of canceling or altering a trip is very high. The tourist spot’s manager is the next important agent in tourism ABM. During the tourism process, the interactions among tourist agents, between tourist agents and manager agents, and among tourist agents, manager agents, and the environment are easily observed (see Figure 1). Tourists communicate their experiences with the tourist spots to which they have traveled when they meet fellow travelers. Tourist spot managers can also obtain feedback from tourists and improve their services. The agents of a complex tourism system are autonomous and heterogeneous. Individual agents can have different perceptions of the same landscape. Many aspects of human cognition are known to be heavily context-sensitive, including decision making, reasoning, and perception (Kokinov, 1994). This feature enables individuals to develop habits, norms, and expectations that pertain to particular types of situations (Chen, Mak, & McKercher, 2011). In complex tourism systems, each tourist agent brings his/her own habits, norms, and expectations that are shaped by individuals’ demographic and behavioral characteristics, including their origin, income, time, and financial budget. Each manager agent develops his/her own habits, norms, and perceptions based on the tourist spot’s location, grade, and target (see Figure 1). All these agents proactively, actively, and adaptively interact with tourist spots over time. Normally, a more recognizable situation enables more particular types of behavior to be developed; a higher number of behavior types that are special to a particular situation enable the situation to be more distinguishable. Tourist Spot Selection and Organization Choosing the main destination area, selecting the visit sites of a destination area, and making time and budget plans are the first steps for self-organized tourism. In reality, different tourists select their destination areas on the basis of their own time, finances, and other constraints. Tourists with little time and a limited budget typically select one destination area for one travel event. In contrast, tourists with abundant time and an unlimited budget can select two or more destination areas for one travel event. These types of diverse choices complicate the observation and modeling processes of tourist flow. To simplify the model, this study assumes that all tourists start their tourism from the same distribution center and that each tourist selects one main tourism destination as his or her tourism direction. This assumption is reasonable, particularly for modeling long-distance travel because most such travelers need to stop in a central location in which an airport or central train station for inter-regional rail lines is located. Another assumption made is that long-distance and local tourists engage in different behavior: long-distance travelers start with high-grade tourist spot destination directions and local travelers start with random tourist spot destination directions (see Figure 2). The literature indicated that, different from short-distance tourists, long-distance tourists normally prefer high-level destinations in China (Yang & Ma, 2004), which are tourist spots classified from “A” to “5A” on the basis of certain criteria.
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Distribution Center
Long distance?
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Yes
No
Tourist
Tourist
High grade
Random
First Destination
Figure 2. Selection of the first destination.
After confirming the first tourist spot, the tourist checks the next nearest tourist spot depending on his/her own unique situation (see Figure 3). If one has enough time to visit a site and considers the tourist spot worthwhile, the tourist adds that tourist spot to the tour itinerary. Empirical research found that transportation time and tour time are
First Spot
Nearest next
No
Enough time?
Yes
No
Utility >1? Yes Next Spot
End
Figure 3. Organization of tourism itinerary.
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the main factors that influence tourists when they make decisions regarding subsequent tourist spots (Li, Wang, & Wang, 2005; McKercher & Lew, 2004). Accordingly, this study defines the ratio between tour time TMv and transportation time TMt as tourism utility (U).
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U ¼ TMv =TMt
(1)
Normally, a given tourist with rational decision-making behavior considers going to the next tourist spot only when the value of U exceeds 1 (Wang & Wang, 2000). That is, tourists believe that the next tour is not worthwhile when the transportation time exceeds the time spent touring around a tourist spot. After the selection of one tourist spot, the tourist agent deducts the tour time and the transportation time to that tourist spot from the total time budgeted, and then continues the journey until the budgeted time runs out (see Figure 3). Through this process, a tourist agent creates the tourism itinerary around the planned destination area. Tourist Agents’ Interaction According to the itineraries developed, tourist agents carry out their tourism journey. During the journey, each tourist agent evaluates and judges the tourist spots visited. Situations always exists in which tourists meet one another and compare experiences about the tourist spots they have already visited. These interactions can lead tourists to adapt and change their itineraries on the basis of the new information (Song, Wong, & Chon, 2003). For instance, if one tourist does not like congestion and meets other tourists who tell him that the next tourist spot on his itinerary is very crowded, he is likely to remove this tourist spot from his schedule. To date, although two methods—designative and evaluative image—have been devised to assess tourists’ image of destinations (Mao, Zhang, & Bao, 2005), accessibility, infrastructure, and service quality are three of the most used attributes adopted by these two approaches (Chi & Qu, 2008; Gallarza, Saura, & Garcıa, 2002; Sirgy & Su, 2000). Meanwhile, crowdedness is becoming one of the most important attributes of destination image evaluation, particularly in China (Choi, Chan, & Wu, 1999; Li & Wang, 2011; Wan & Li, 2013; Yan, Barkmann, & Marggraf, 2007). Hitherto, this model defines four factors that determine tourist agent j’s sense of tourist spot i (Si): transportation T, accommodation A, degree of crowdedness C, and facility service F. h i j j j j j Si ¼ Ti Ai Ci Fi (2) Transportation (T) represents the accessibility of the tourist spot. Excessively longdistance travel might encourage tourists to give up their intention to visit a particular tourist spot. Research has found that human psychological endurance has an exponential relationship with distance during traveling (Miaou & Song, 2005). This study borrows that concept and defines the transportation impression of the tourist spot as: T ¼ eμt
(3)
where t is the transportation time and μ is the coefficient value. The longer the transportation time to the tourist spot, the lower the impression value.
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The senses that tourists have of the accommodation (A) and facility and service (F) quality of a tourist spot are correlated with the grading of the tourist spot and the instant tourist population. Since the classification of tourist sites is partially based on the criteria of the tourist spot’s service quality, we use grades to indicate the service ability of a tourist spot. High grade tourist spots usually receive more support from the local government through investments and subsidies. In turn, such support helps tourism management develop better accommodation, and basic facilities and infrastructure. However, the capacity of the accommodation and facilities of a given tourist spot is not always greater than the number of tourists over time (Gelman & Salop, 1983), but may be equal to or less than the tourist demand. Experimentally, excessive instant tourist demand at a given time will adversely impact a tourist’s impression of the tourist spot. This study assumes the impression of accommodation A and facility infrastructure F as: A ¼ α f ð g Þ=f ð pÞ
(4)
F ¼ β f ð g Þ=f ð pÞ
(5)
where g is the grade of the tourist spot, p is the overall instant tourist population of the tourist spot at a specific time, and α and β are coefficient factors. Considering g and p have different units, we use transition functions to convert them into unit-free variables. The crowding degree (C) of a tourist spot is directly correlated to its instant tourist population (Tarrant, Cordell, & Kibler, 1997). A tourist’s impression of a tourist spot’s crowdedness worsens as the number of tourists that he/she sees during the journey increases. However, the relationship between perceived crowding and number of other tourists should not be linear. Experience indicates that the crowding perception brought by one more person in an already overcrowded place is significantly higher than a loose perception. Therefore, this study assumes that the degree of crowding is exponentially related to tourist population. C ¼ eσp
(6)
where p is the instant tourist population at a tourist spot at a specific time and σ is the coefficient value. Individual tourists’ sense of a tourist spot and subsequent possibility of visiting the tourist spot are not just objectively determined by these four factors. Their impression of a tourist spot may also be shaped subjectively from the stories heard from other tourists during the touring process. Although various tourists could have the same impression about a particular tourist spot, they may share with other tourists different feelings about what they learn and experience from their own perspectives given their independent background (Fakeye & Crompton, 1991). For instance, long-distance tourists consider transportation very important because they lack detailed knowledge about the destination. At the same time, they are not likely to be influenced by the accommodation and the degree of crowding of the tourist spot because most longdistance tourists have already booked their hotels and may be less negatively impacted by the crowds. In contrast, local tourists do not consider transportation to be as important as long-distance travelers do because their familiarity with the destination area allows them to easily adjust their schedules. Local tourists likely find the degree of
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crowding and accommodation level more important. Therefore, this study defines a corresponding vector to represent the background dependency of tourists (D): Dj ¼ tj aj cj f j (7) where Dj is the background dependency of tourist j, and t, a, c, and f are the four coefficient values that relate to the background of tourist j and that affect a tourist’s sense of transportation T, accommodation A, crowding degree C, and facility and service level F. According to the aforementioned analysis, the model assumes t þ f > a þ c for long-haul travelers and a þ c > t þ f for local travelers. Meanwhile, the model requires that t þ f þ a þ c = 1. When traveling, tourists communicate with other tourists, obtain information about future tourist spots, and make their own judgements, as represented by multiplication of two vectors: X j X 0 j Ii ¼ Ii;k ¼ Dj Si;k (8) k¼1
n¼1
j Ii
where is the pre-impression of tourist j about tourist spot i and k is the tourist who has been to tourist spot i and happens to meet tourist j when touring. Tourist j obtains feedback from tourist k and forms a pre-impression about tourist spot i from the feedback. If the value of I is lower than the threshold value (n) that a tourist can accept, the tourist removes tourist spot i from his/her planned itinerary. If the value of I is equal to or higher than the predetermined threshold, tourist m continues his/her travel as planned.
Tourist Spot Management Agents In reality, tourist spot managers are constantly attempting to conduct corresponding mitigation measures to improve a tourist’s impression of a tourist spot through better services. Their mitigation abilities are closely related to the level and quality of the tourist spot’s management (Hsu, 2010). In this study, the mitigation vector of the tourist spot manager agent is defined as: Wi ¼ ½wi T wi A wi C wi F
(9)
where W is the mitigation vector of tourist spot i, and wi is the mitigation ratio that reflects the mitigation ability of tourist spot i. This vector is added to the tourist’s sense vector to obtain the adjusted impression of tourist j on tourist spot i: j
Wi Si ¼ ½ð1 þ wi ÞTð1 þ wi ÞAð1 þ wi ÞCð1 þ wi ÞF
(10)
Certain situations create extreme positive or negative impressions about a tourist spot, such as unexpected amazing views, kind help, poor service attitude, and/or conflicts with other tourists. These positive or negative impressions can lead to subjective judgements about the tourist spots visited (Liu, 2010). For example, a tourist will form a negative impression about the scenic spot at which he was injured even though he had a good time before that event. To simulate this phenomenon, the model applies a multiple to the vector to account for a tourist’s impression:
JOURNAL OF CHINA TOURISM RESEARCH j
j
ESi ¼ WSi x
11
(11)
where ESm i is the extreme sense vector that tourist j experiences about tourist spot i and x is the extreme impact ratio. Generally, the shadowing/halo-effects of an independent incident will involve the entire travel experience (Aschauer, 2010). Accordingly, this model supposes that the extreme positive or negative impressions that a tourist develops from one aspect (transportation/accommodation/crowding/facility/service) discount or enhance the tourist’s sense with respect to all aspects.
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Overall Tourist Flow ABM Model Using the definitions previously outlined, the overall tourist agents’ interaction model is defined as follows: 8 agent :j > > < itinaryf1; 2; 3 . . .; ng Tourist agent : (12) background depency : tj ; aj; cj ; fj > > : spot sense :½T; A; C; F spot :i Tourist spot manager agent : (13) mitigation rate :w X 0 j j Dj ðSi;k þ Wi xÞ ; (14) Interactions : Ii ¼ k¼1
j
Decisions : if Ii :
>n continue tour to i n cancel the tour to i;
(15)
where n is the threshold that a tourist agent uses to determine whether or not to go to the tourist spot. The value represents the worst situations that a tourist can withstand at the tourist spot that he plans to visit, which varies among different tourists. With respect to computer simulation, a higher value indicates more cancellations of planned visits to tourist spots. To simplify the model, this study takes this value as uniform and develops it using the simulation test.
Study Area The study area for this investigation is the Sichuan province in China, which uses a rating system to determine the quality of an attraction relative to its peers in terms of safety, cleanliness, sanitation, and transportation. The rating system categorizes all tourist attractions in China into five categories: A (or 1A, the lowest level), AA (2A), AAA (3A), AAAA (4A), and AAAAA (5A, the highest level). Considering the numerous scenic spots and historic sites in China, only 3A and above attractions have been credited by authorities by far. In 2010, 41 National Scenic Spots and Historic Sites were classified as Grade 3A or higher tourist spots in the Sichuan province according to information from the China National Tourism Administration. This study categorized these scenic spots and historic sites into three groups—natural, cultural, and recreational tourist sites according to the types of attractions at these sites (see Figure 4 and Table 1). To represent
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Figure 4. Tourism destinations with Class 3A and above in the Sichuan province.
Table 1. Tourism Spots Graded 3A and above in the Sichuan Province. ID 1
Type 2
Grade 4A
Time 4
ID 22
Spot Bailong Lake
1 1
5A 5A
6 5
23 24
4 5
Spot Leshan Grand Buddha Scenic Area EmeiMountain Qingcheng MountainDujiangyan Siguniang Mountain Jiuzhai Valley
1 1
4A 5A
5 6
25 26
6 7 8 9 10
Huanglong Temple Bifeng Valley Liu’s Manor Museum Southern Sichuan Bamboo Sea Hometown of Deng Xiaoping
1 1 2 1 2
4A 4A 4A 4A 4A
5 5 4 5 4
27 28 29 30 31
11 12 13
Huaying Mountain Hailuogou Valley XichangSattellite Launch Center Qionghai Lake-Luoji Mountain GonggaMountain Wawu Mountain Tiantai Mountain Mengshan
1 1 3
4A 4A 3A
5 5 3
32 33 34
DoutuanMountain Sanxingdui Archaeological Site Yuchan Ountain Hejiang Buddha Scenic Area Xingwen Stone Sea Panzhihua Rafting Base Fangshan China’s Dead Sea Porcelain Ware Museum of Song Dynasty Jianmen Pass Cuiyue Lake Furong Old Town
1 1 1 1 1
4A 4A 3A 3A 3A
5 5 4 4 4
35 36 37 38 39
1 2
4A 3A
5 3
1
4A
5
2 3
14 15 16 17 18 19 20 21
Xilingxue Snow Mountain AnyueStone Carving Scenice Area Guangwu Mountain-Nuoshui River
Type 1
Grade 4A
Time 5
1 2
4A 4A
5 4
1 1
3A 3A
4 4
2 3 1 1 2
4A 3A 3A 4A 3A
4 3 4 5 3
2 1 2
4A 3A 3A
4 4 3
2 2 3 2 2
3A 3A 3A 4A 3A
3 3 3 4 3
40 41
Oriental Buddha Park Gold Eagle Villa Zhuyeqing Garden Zigong Dinosaur Museum Zigong Salt Industry Museum Zigong Lantern Museum Mengding Mountain
2 1
3A 4A
3 5
42
Chengdu City
3
3A
3
Note. Type 1 represents natural spot; Type 2 represents culture site; and Type 3 represents recreational facilities. To represent urban tourism activities in the distribution center, this study classified Chengdu as a 3A-level tourism site.
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the urban tourism activities, this study classified Chengdu as a 3A recreational site in addition to the other 41 scenic spots and historic sites. For each group, different touring times were applied. Natural tourist spots and recreational facilities usually require more time to visit than cultural sites. High-grade scenic spots and historic sites usually have more or better attractions that require additional time to tour around relative to lowergrade spots. One of the main reasons for selecting the Sichuan province is its geographical layout. A map of this province shows that its main part sits inside the Sichuan Basin (see Figure 4). The steep mountains in the surrounding areas make it a closed region. The city of Chengdu, with the largest railway station and airport in the province, is the provincial capital and transportation hub. It serves the primary function of transporting travelers in and out of the province. As a result, most tourists take Chengdu as the only distribution center in this area (Yang & Wong, 2013). A relatively closed region means that the model does not need to be concerned about cross-region tourism activities, and being the only distribution center means that all tourist agents start and end their tourism at this point. These two unique characteristics significantly simplify the simulation process. Moreover, Sichuan was among one of the earliest and leading provinces to develop tourism resources. The advanced administration system makes the data needed for this study more available than in other provinces. For example, the Sichuan province is the only province with a daily tourist reception report system. One of the difficult tasks of this study is to define the origins of all tourists in the Sichuan province. Some tourists come from other provinces, whereas other tourists are from within the province. Although the province published statistics on the volume and ratio of tourists from other provinces, no information exists on local tourists. To compare the behaviors of local tourists and external tourists, this study uses the tourism market share model as defined by Li, Wang, and Zhong (2012) to determine the origins of tourists: Pj1α Gαj exp βrjk Tjk k pj ¼ P ¼ P 1α α (16) Gj exp βrjk j Tjk j Pj where pkj is the market share of location j, k is the central location, Tjk is the spatial interaction strength between location j and central location k, Pj is the population at location j, Gj is the GDP of location j, 1 α is the population elasticity coefficient, α is the income elasticity coefficient, β is the spatial damping coefficient, and rjk is the distance between location j and k. This study took 21 cities in the Sichuan province and 33 other provinces as tourist generating regions and calculated the tourism market share using the statistical data in 2007. The analysis showed that the origins of tourist agents are defined by multiplying market share and total agents. The data on population and GDP were obtained from the China Statistical Yearbook (National Bureau of Statistics of China, www.stats.gov. cn) and the Sichuan Province Statistical Yearbook (Sichuan Provincial People’s Government, www.sc.gov.cn). Moreover, the research took the geometric centers of the 54 regions as tourists’ origin centers and calculated the Euclidian distances between these tourist origins and the distribution center, Chengdu. Subsequently, this study classified all tourists into two groups: long-distance travelers and short-distance
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travelers. Because the distance from Chengdu to the nearest border of Sichuan province is approximately 100 km and the distance to the nearest neighbor province’s capital city of Chongqing is 300 km, this study used 200 km to delineate long-distance travelers and short-distance travelers. The ABM developed in the previous section assumes that different tourists can have different impressions about the same influencing factors because of their background dependency. Due to the lack of relating study and experimental research, this study estimated these parameters through sensitivity simulation. They will be explained in the following section.
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Model Sensitivity Test and Estimation of Model Parameters When developing the simulation model, we define a vector D to represent the context dependency of tourists. Given the difficulty of observing a tourist’s psychological activity, short of taking a sample survey, this study conducts a sensitivity test to define these values. The presumptions of the definition are that the sum of the four factors (t, a, c, f) equals 1 and none of these factors equals 0 for both long-distance and short-distance travelers (equation (7)). Long-distance tourists take transportation t and facility and service f as more important than accommodation a and crowding degree c; short-distance tourists take accommodation a and crowding degree c as more important than transportation t and facility and service f. Among the factors, the model assumes that transportation is more important than facility and service for long-distance tourists; and crowding degree is more important than accommodation for short-distance tourists. Hence, the vector (t, a, c, f) for long-distance tourists has only three choices: (0.4, 0.2, 0.1, 0.3), (0.4, 0.1, 0.2, 0.3), and (0.5, 0.1, 0.1, 0.3). The vector (t, a, c, f) for short-distance tourists has only three choices (0.1, 0.3, 0.5, 0.1), (0.2, 0.3, 0.4, 0.1), and (0.1, 0.3, 0.4, 0.2). One decimal place was used for each of the values during simulation. To determine the parameter value definitions that have better performance for the simulation, this study tested all combinations of these values. The results show that the {(0.5, 0.1, 0.1, 0.3) vs. (0.2, 0.3, 0.4, 0.1)} has modest tourist spot cancellations among all combinations. Considering that both the low and the high overall cancellation times can mask important distinctions of the complex tourism system, this study selected the modest performance values. Inside the context dependency vector D, this study defined the transportation impression of a destination as being inversely related to the transportation time needed to arrive at a scenic spot (function 3). During simulation, t was defined as the time that a tourist needs to get to the scenic spot, and μ was defined as 1 divided by total tourism time. When t ¼ 0, T = 1 has the largest value; when t equals total tourism time, T ¼ 1=e has the lowest value. For crowding degree C (function 6), we define σ as 1 divided by total simulated tourists. When p ¼ 0, C ¼ 1is the highest value and when p equals the total number of simulated tourists, C ¼ 1=e is the lowest value. The lowest value of both T and C is 1/e, ensuring that the impression of a scenic spot is higher than 0; otherwise, the tourist does not put this scenic spot into his/her itinerary. For the quality of accommodation impression A (function 4) and facility and service impression F, we define p as 1 minus the proportion of tourists at the
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current scenic spot out of the total number of simulated tourists, and g as the grade of the current scenic spot divided by 5. Both α and β equal 1. When no other tourist comes to the current scenic spot, p = 1 and both A and F equal 1 as a 5A scenic spot. When all simulated tourists come to the current scenic spot, both A and F equal 0 regardless of the grade of the scenic spot. This study also defined an extreme impression function to account for extremely good or bad experiences encountered by a tourist agent. The extreme experience factor x is multiplied by a tourist agent’s impression of a tourist spot. The magnitude of x has a linear relationship with the number of tourist agents who cancel one tourist spot from their schedules. According to simulation tests, the variation of the value x does not change the comparison among tourist spots, which is one of the main characters for the analysis of a tourist spot system. Accordingly, this study of the Sichuan province defines the value of x as 1.2 for an extremely good experience and 0.8 for an extremely bad experience.
Tourist Flow Simulations Based on the previously described model specification, a simulation software was developed using Microsoft Visio Studio (C#) and the ArcGIS engine. This study used the software to simulate the process of the tourist flow dynamic in Sichuan, China, and to record the general overall tourist flow, tourist flow snapshot, and unfinished travel plan. Considering that most hotels serve breakfast from 06:00 to 08:00 and dinner from 18:00 to 20:00, tourists attempt to catch up at the two mealtimes and make use of a full day. The simulation supposed that each tourist agent spends 12 hours a day (including transportation time) for tourism and all tourists have a one-week (seven days, which correspond to National Day holiday) vacation. The National Day holiday (seven days) attracts 19 million tourist trips in Sichuan province (Sichuan Provincial Tourism Bureau, scta.gov.cn). Because of the inadequacy of detailed data to indicate the origins of all tourists, this study used the aforementioned tourism shared market model (equation (16)) to determine the origins of these tourists. Therefore, tourists from other provinces or cities are proportional to the overall number of tourists that come to Sichuan province. To reduce the computational complexity, a total tourist population of 10,000 was used to simulate the tourist flow of the Sichuan province.
Overall Tourist Flow The ABM simulation recorded the accumulated tourist traffic volume between each pair of subsequent tourist spots. The result showed that the overall tourist flow in the Sichuan province started from the central tourism distribution center, Chengdu, and fans out in a radial distribution with the primary travel directions being southwest, south, and southeast. Among these directions, the southeast direction attracted more than 15% of the tourism activities, the south direction attracted more than 16% of the tourism activities, and the southwest direction attracted more than 19% of the tourism
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Figure 5. Overall tourist flow of Sichuan during 7 days.
activities (see Figure 5). Therefore, the tourist flow distribution volume in the Sichuan province is uneven under the current geo-spatial situation.
Tourist Flow Snapshot One very important point regarding the simulation of the tourism system is capturing the dynamic process. The model simulation recorded the tourists’ movements at any specific time (see Figure 6). Figure 6(1) indicates a high rate of tourist volume on the route from Leshan to Panzhihua; Figure 6(2) indicates peak tourist flow inside Zigong; Figure 6(3) indicates a large number of tourists on the routes from Chengdu to Ya’an. This phenomenon came with simultaneous low tourist flow routes among other tourist spots. Figure 7 shows the resultant tourist load during the seven days of simulation for six tourist spots. The statistical graphs demonstrate that visitors to some of the tourist spots, such as 3, 5, and 9, were primarily concentrated in short time intervals instead of spread throughout the entire simulation period. Therefore, some tourist spots, such as 3, 5, and 9 experienced high touring pressure at peak times but an inadequate number of tourists at other times. This pattern corresponds to the map simulation presented in Figure 8. Additionally, the results show that tourist spots 1 and 2 have complementary
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Figure 6. Tourist flow on the 15th hour of the 7 days.
Figure 7. Individual tourism site population of the 7 days.
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Figure 8. Un-finished destinations according to the simulation.
tourist peaks, signaling that most tourists move simultaneously from one tourist spot to another, potentially leading to the result that one tourist spot is overcrowded with tourists while the other spots have few tourists.
Unfinished Tourist Spots The model simulation also recorded the tourist spots that tourists readily cancel because of interactions with other tourists in the middle of the tourism processes. The results as presented in Figure 8 show that faraway tourist spots in the north and west, such as those in Aba, Guangyuan, Mianyang, Deyang, and Ya’an may experience higher cancelation rates than others. These tourist spots are most likely bypassed by tourists based on the information they obtain from others who have already visited the sites.
Concluding Remarks This study presents an ABM that simulates the tourist flow diffusion process in a defined spatial and temporal space, namely the Sichuan province in China, which possesses 41 class “A” tourist spots. The results of the tourist flow simulation show that an uneven tourist flow distribution exists in the Sichuan province; local congestion between pairs of tourist spots occurs at certain times; several pairs of tourist spots have
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complementary tourist peaks; and the tourist spots in the north and southwest directions have higher cancelation rates than spots in other directions. Tourist statistics of the 17 main tourist spots in the Sichuan province in October 2007 (Sichuan Provincial Tourism Bureau, www.scta.gov.cn) showed that Leshan, Emei Mountain, Qingcheng Mountain-Dujiangyan, Huanglong, Jiuzhai Valley, Southern Sichuan Bamboo Sea, and Sanxingdui were the leading and most populated tourist spots. Among them, Leshan, Emei Mountain, Qingcheng Mountain-Dujiangyan, and Southern Sichuan Bamboo Sea constituted the main tourist flow in the nearby southwest, south, and southeast directions, which was consistent with the simulation results. Both statistical data and computer simulation indicated that the tourist spots in the east, northeast, north, and remote southwest directions have far fewer tourists compared with the other directions. The model developed above showed that short-distance tourists are very sensitive to transportation conditions, indicating that the current transportation infrastructure and tourism resource distribution may create an uneven distribution of tourist flow considering that the bulk of the tourists are from within the province. The simulation assumed that all tourists start their tourism activities simultaneously from the only distribution center, Chengdu, to correspond with the seven days’ National Day holiday. The tourist spot monitoring report of the province showed that a majority of tourist spots welcome their tourist volume peaks during the first three days of the National Day holiday. Subsequently, the number of tourists declines continuously in most tourist spots (Government of China, www.gov.cn/). This tourismrelated fact is consistent with the assumption that all tourists select one destination area to start their tourism activities. Moreover, the simulation found that several pairs of tourist spots have complementary tourist peaks and this consequently leads to local congestion. This phenomenon occurs when a high volume of tourists simultaneously moves from one tourist spot to the next, making one tourist spot experience a much higher volume of tourists than the other. Hitherto, both tourist spots must prepare for the highest accommodation and transportation, which can be a waste of resources since both tourist spots must collaborate with one another to simultaneously attract approximately the same number of tourists. Take Leshan-Emei Mountain and Huanglong-Jiuzhai Valley as examples. Huanglong is a 4A tourist spot and Jiuzhai Valley is a 5A tourist spot, and they are very close to each other. However, the relationship between these two tourist spots is more sequential than equal. On October 3, the National Day holiday in 2013, more than 41,000 tourists went to Jiuzhai Valley and 30,000 went to Huanglong Temple (Government of China, www.gov.cn/), creating a large number of stranded tourists in Jiuzhaigou. Comparatively, the tourist volumes in Leshan and Emei Mountain are more equally distributed because of the cooperative planning and management of these two tourist spots. This study shows that ABM simulation is particularly appropriate in representing tourists’ individual decision-making processes and their impacts on a complex tourism system. The ABM simulation provides a new method for studying regional tourist flow and is a good complement for the traditional statistical, social-physiological analysis methods (Campbell, 1967; Higham et al., 1996; Mings & McHugh, 1992; Pearce, 1995). The ABM-based tourist flow simulation can help tourists and local managers gain the
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necessary knowledge of the destination areas and create better tourism management strategies. Moreover, modeling the complexity of tourists’ behavior enables planners and researchers to experiment with different management strategies and to identify the best points at which planning actions can be applied—a strategy that cannot be carried out using traditional static analysis. Although the published statistics data verify the success of the model and the simulation, further research is still needed in the field of tourist behavior analysis and tourist spot management policies and mechanisms. Information on these aspects can assist in improving the accuracy of the model. To simplify the model and the simulation, this study selected only one distribution center and assumed that all tourists start their journey from this central location. Using multi-entrances and/or multi-distribution centers is believed to improve the simulation results. Furthermore, support from field surveys on tourist activity and tourist spot data can also assist in refining the model parameters, and hence make the simulation closer to reality.
Disclosure statement No potential conflict of interest was reported by the authors.
Funding This research was supported by the National Natural Science Foundation of China (Funding number: 41071092).
Notes on contributors Rongxu Qiu is in the Department of Geography, University of Lethbridge, AB, Canada (E-mail:
[email protected]) Wei Xu is in the Department of Geography, University of Lethbridge, AB, Canada Shan Li is in the Department of Geography, East China Normal University, Shanghai, China
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