Forecasting of Short-Term Daily Tourist Flow Based on Seasonal Clustering Method and PSO-LSSVM

Keqing Li,Binyou Wang,Shuping Zhao,Chu Li,Changyong Liang,Wenxing Lu

doi:10.3390/ijgi9110676

Keqing Li, Binyou Wang + Show 4 more

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https://doi.org/10.3390/ijgi9110676

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Abstract

The accurate prediction of tourist flow is essential to appropriately prepare tourist attractions and inform the decisions of tourism companies. However, tourist flow in scenic spots is a dynamic trend with daily changes, and specialized methods are necessary to measure it accurately. For this purpose, a tourist flow forecasting method is proposed in this research based on seasonal clustering. The experiment employs the K-means algorithm considering seasonal variations and the particle swarm optimization-least squares support vector machine (PSO-LSSVM) algorithm to forecast the tourist flow in scenic spots. The LSSVM is also used to compare the performance of the proposed model with that of the existing ones. Experiments based on a dataset comprising the daily tourist data for Mountain Huangshan during the period between 2014 and 2017 are conducted. Our results show that seasonal clustering is an effective method to improve tourist flow prediction, besides, the accuracy of daily tourist flow prediction is significantly improved by nearly 3 percent based on the hybrid optimized model combining seasonal clustering. Compared with other algorithms which provide predictions at monthly intervals, the method proposed in this research can provide more timely analysis and guide professionals in the tourism industry towards better daily management.

Highlights

In recent years, owing to steady improvements in the standards of living, tourism has become an important part of leisure and lifestyle for people worldwide
Particle swarm optimization is used to optimize the least squares support vector machine; on the other hand, we focus on rearranging the seasons by clustering algorithm
The experimental results corroborate that: (1) season is a factor that profoundly affects the accuracy of prediction of the daily tourist flow, which can be supported by evidence from Table 4; (2) seasonal adjustments improve the prediction accuracy effectively by nearly 3%

Summary

Introduction

In recent years, owing to steady improvements in the standards of living, tourism has become an important part of leisure and lifestyle for people worldwide. According to data released by the World Travel Tourism Council, tourism was the third largest industry in the world in terms of the growth rate of Gross Domestic Product (GDP) in 2019. The growth rate of tourism was reportedly 3.5%, which was significantly greater than the global economic GDP growth rate of 2.5% [1]. The tourism industry created nearly 80 million jobs in China, accounting for 10.3% of the country’s total labor force. Its output value was estimated to be 10.9 trillion Yuan, accounting for 11.3% of China’s economy [1]. China’s tourism industry has entered the stage of ‘mass tourism’, with people’s willingness to travel constantly rising [2]. It is expected that the domestic tourism market will continue to thrive even in the post-epidemic era [3]

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