Research on a forecasting model of tourism traffic volume in theme parks in China

Mei, Zhenyu; Qiu, Hengwei; Feng, Chun; Cheng, Yang

doi:10.1093/tse/tdz011

Cited by 4 publications

(4 citation statements)

References 12 publications

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“…Finally, in the context of events, [36] investigate the traffic management plans used for sporting events with objective and quantitative data over a five-year period, while [54] analyses the need to manage vehicular traffic during sporting events and, applying simulation approaches, evaluates various alternative solutions. In the context of theme parks, [55], apply a multiple regression model to forecast tourism traffic volume.…”

Section: Vehicular Traffic In the Context Of Tourismmentioning

confidence: 99%

A Review and Analysis of Traffic Data Sources

Balugani

Marinello

Gamberini

et al. 2023

Advances in Transdisciplinary Engineering

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Transportation is essential for economic and social development, and vehicle flow data can be used for safety monitoring, pollution analysis, and traffic flow management. Unfortunately, traffic management and control centres do not always comply with codified standards, making it difficult to obtain up-to-date data. This paper analyses open traffic datasets and Italian public traffic data sources available online, providing a knowledge base for transportation managers and researchers. Open traffic datasets are dimensionality-reduced and clustered. An event with 209,135 visitors is used to benchmark the public data sources, the time series of traffic flows are decomposed and a regression tree is used to identify different periods. The results suggest that the available Italian sensor grid is not fine enough to identify all incoming and outgoing traffic, more infrastructure investments are required or the available measurements should be coupled with other evaluation approaches capable of extending the punctual data through mathematical means.

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Section: Vehicular Traffic In the Context Of Tourismmentioning

confidence: 99%

A Review and Analysis of Traffic Data Sources

Balugani

Marinello

Gamberini

et al. 2023

Advances in Transdisciplinary Engineering

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“…SVR originated from a machine learning model hence a support-vector machine (SVM) can work for regression tasks and is suggested in order to forecast tourism demand. Unlike most conventional neural network model, SVR applies the theory of structural risk minimization which based on the idea of empirical risk minimization, to minimize the upper limit of the generalization error, instead of minimizing the error in training [15][16][17].…”

Section: B Prediction In Tourist Domainmentioning

confidence: 99%

“…Empirical research has shown that the choice of the parameters in an SVR model significantly influences the accuracy of forecasting. SVR solves the problems of estimation, classification, and nonlinearity via its loss function [17]. Tourism data often exhibit nonlinear characteristics, with SVR widely used in the tourism demand forecast.…”

Section: B Support Vector Regressionmentioning

confidence: 99%

Prediction of Tourist Visit in Taman Negara Pahang, Malaysia using Regression Models

Mutalib¹,

Razali²,

Kamarudin³

et al. 2021

IJACSA

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Tourism is among the significant source of income to Malaysia and Taman Negara Pahang is one of the Malaysia's tourism spots and the heritage of Malaysia in achieving the Sustainable Development Goals (SDG). It has attracted many international and local tourists for its richness in flora and fauna. Currently, the information of tourists' visits is not properly analyzed. This study integrates the internal and public information to analyze the visits. The regression models used are multiple linear regression, support vector regression, and decision tree regression to predict the tourism demand for Taman Negara, Malaysia and the best model was deployed. Predictive analytics can support the decision-making process for tourism destinations management. When the management gets a head-up of the demand in the future, they can choose a strategic planning and be more aware about the factors influencing tourism demand, such as the tourists' web search engine behaviors for accommodation, facilities, and attractions. The factors affecting the tourism demand are determined as the first objective. The role of independent variable was set to the total number of visitors, subsequently being set as the target variable in the modeling process. A total of 30 models were generated by tuning the cross-validation parameters. This study concluded that the best model is the multiple linear regression due to lower root mean square error (RSME) value.

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“…Through the above literature analysis of the theme park, it can be seen that the entertainment facilities, overall atmosphere, specific theme, food types and service quality of the theme park itself are important factors in determining the satisfaction of tourists. In addition, through various data analyses of the theme park [20][21][22][23][24] and the investigation and analysis of the botanical landscape in the park [25][26][27], it can be seen that the relevant theories of the research object have become more scientific and rational.…”

Section: Introduction and Literature Reviewmentioning

confidence: 99%

Innovation and Development: An Analysis of Landscape Construction Factors in Quanzhou Maritime Silkroad Art Park

2023

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From the perspective of tourists, this paper takes Quanzhou Maritime Silkroad Art Park as the research object to study the botanical landscape factors concerned with tourists in the theme park. Through a questionnaire survey, and combined with interviews, the collected results were scientifically analysed using the data. According to the statistical results, the factors of plant landscape construction in the theme park concerned with tourists were summarised, extracted, and named, which were “plant landscape healing”, “plant landscape culture”, “plant landscape continuity”, “plant landscape spatial sense”, and “plant landscape aesthetic sense”. Through an in-depth analysis of the five common factors of the construction of modern theme park plant landscapes, this study creatively centred on the construction of theme park landscapes and established a scientific evaluation system, combined with the development and construction of the park, and put forward innovative and constructive suggestions based on the summary and analysis results. It provides a scientific reference for plant landscape construction in other theme parks.

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Research on a forecasting model of tourism traffic volume in theme parks in China

Cited by 4 publications

References 12 publications

A Review and Analysis of Traffic Data Sources

A Review and Analysis of Traffic Data Sources

Prediction of Tourist Visit in Taman Negara Pahang, Malaysia using Regression Models

Innovation and Development: An Analysis of Landscape Construction Factors in Quanzhou Maritime Silkroad Art Park

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