WSEAS Transactions on Environment and Development
Print ISSN: 1790-5079, E-ISSN: 2224-3496
Volume 21, 2025
Enhancing Tourist Forecasting in Thailand’s
Authors: , ,
Abstract: This article focuses on predicting the number of tourists visiting Thailand’s national parks using count
data models. Given the discrete and overdispersed nature of the tourist count data, traditional Poisson regression
models were extended to include Negative Binomial (NB) and Zero-Inflated models. Using data from 2016 to
2022, we evaluated four model types: Poisson, Negative Binomial, Zero-Inflated Poisson, and Zero-Inflated
Negative Binomial (ZINB). Model performance was assessed using the Akaike Information Criterion (AIC),
log-likelihood values, and the Vuong test. Findings reveal that the ZINB model best fits the data, addressing
both overdispersion and excess zeros, resulting in more accurate predictions. This model is thus recommended
for similar count data applications in tourism and environmental studies. Future work may focus on optimizing
the model by reducing complexity and improving outlier handling.
Search Articles
Keywords: Count data, Regression model, Poisson, Negative Binomial, Zero-inflated model, Overdispersion,
Tourists
Pages: 284-292
DOI: 10.37394/232015.2025.21.25