Intelligence in Tourism Management: A Hybrid FOA-BP Method on Daily Tourism Demand Forecasting with Web Search Data

Keqing Li,Wenxing Lu,Changyong Liang,Binyou Wang

doi:10.3390/math7060531

Abstract

The Chinese tourism industry has been developing rapidly for the past several years, and the number of people traveling has been increasing year by year. However, many problems still beset current tourism management. Lack of effective management has caused numerous problems, such as tourists stranded during tourist season and the declining service quality of scenic spots, which have become the focus of tourists’ attention. Network search data can intuitively reflect the attention of most users through the combination of the network search index and the back propagation (BP) neural network model. This study predicts the daily tourism demand in the Huangshan scenic spot in China. The filtered keyword in the Baidu index is added to the hybrid neural network, and a BP neural network model optimized by a fruit fly optimization algorithm (FOA) based on the web search data is established in this study. Different forecasting methods are compared in this paper; the results prove that compared with other prediction models, higher accuracy can be obtained when it comes to the peak season using the FOA-BP method that includes web search data, which is a sustainable means of practically solving the tourism management problem by a more accurate prediction of tourism demand of scenic spots.

Highlights

The Chinese tourism industry has grown along with development of the Chinese economy
This study aims to propose an effective short-term tourism demand forecasting method that can effectively forecast daily tourist flow on the basis of web search data and back propagation (BP) neural network optimized by a fruit fly optimization algorithm (FOA)
The Genetic algorithms–back propagation (GA-BP) model, and particle swarm optimization–back propagation (PSO-BP) model were selected as the benchmark model in this study

Summary

Introduction

The Chinese tourism industry has grown along with development of the Chinese economy. The number of inbound and domestic tourists in China are increasing year by year and the tourism industry is developing rapidly [1] Such rapid growth has resulted in tourism management problems requiring urgent solutions, including forecasting tourism demand especially when large numbers of tourists travel to scenic areas for short-term visits. A number of research on the prediction of tourist flow and various prediction methods have been proposed, including an econometric model, a time series model, artificial neural network and support vector machine, and hybrid methods [5]. With the development of computer technology, artificial intelligence methods such as artificial neural network and support vector machine are being applied in the research on tourism demand forecasting [10,11]. Neural networks (NN) are used to improve the accuracy of the Grey–Markov (GM)

Objectives

Methods

Results

Conclusion