An Agent-Based Simulation Model To Measure Tourism Spillover Effects: A Demand Perspective

Spillover effects mean externalities resulting from economic activity or processes. In terms of tourism, spillover effects are common regarded as a useful indicator for regional tourism cooperation. Tourism spillover effects may be generated on both supply side and demand side and from the perspective of demand, tourists’ multi-destination travel leads to strong spillover effects, which attracts more academic interests. In previous studies, four mainstream models are used to measure tourism spillover effects, which are the seemingly unrelated equation model, the simultaneous equation model, the spatial econometric model and the gap model, each of which cannot take account of both micromechanism and large sample size. Thus, an Agent-Based Simulation Model is used for tourist behavior modeling in this research to show tourism spillover effects among cities in China. The database includes 361 prefectural-level divisions of China and 1989 AAAA-grade scenic spots. Based on reality, three typical travel patterns are abstracted out, which are sightseeing pattern, wandering pattern and vacation pattern. Each tourist agent in the model chooses one pattern according to the given probability and begins to visit scenic spots. When a tourist agent travels from one city A to another city B, three indicators of tourism spillover effects change:interacting numbers of both cities increase by 1, accepting number of city A increases by 1 and generating number of citiy B increases by 1.Results show that:in models with single travel pattern, there are big differences of tourism spillover effects among sightseeing pattern, wandering pattern and vacation pattern, and the model with sightseeing pattern generates the highest level of spillover effects. Also when increasing the probability of sightseeing pattern in scenarios, tourism spillover effects are siginificantly enhanced. These two results collectively suggest that sightseeing pattern is the main driving pattern for tourism spillover effects. In the random model with all three patterns, inter-regional tourism spillover effects are ubiquitous in China, which examines the rationality of the fever for tourism destination circle from a demand perspective. Further more, scenario simulation is carried out in this research by adjusting traffic speed, visiting time of scenic spots and the probabilities of three patterns. Results show that tourism spillover effects are improved with increased speed, and significantly suppressed with more visiting time of scenic spots. When increasing the probability of sightseeing pattern, the spillover effects become more significant. For the foreseeable future with more holiday tourists and upgraded scenic spots, tourism spillover effects would be reduced, as would the enthusiasm for tourism destination circle construction. In addition, an optimized gap model is used to validate agent-based simulation model with the Yangtze Delta TDC, Changzhutan TDC and Xi’an TDC as case studies. The results of two models turn out to be partly matched and the gap model has more explanatory power at the micro level than that of ABS. In future research, quantitative verification for tourism spillover effects is suggested.