Forecasting of Short-Term Daily Tourist Flow Based on Seasonal Clustering Method and PSO-LSSVM
Bibliographic Data
| ID | 22032164 |
|---|---|
| Authors | Keqing Li (0000-0001-5398-4486, Hefei University of Technology, corresponding author), Changyong Liang (0000-0003-2076-5345, Hefei University of Technology), Wenxing Lu (0000-0002-8598-8155, Hefei University of Technology), Chu Li (0009-0003-7612-6224, Hefei University of Technology), Shuping Zhao (0000-0001-6081-934X, Hefei University of Technology), Binyou Wang (0000-0002-8821-4120, Hefei University of Technology) |
| Year | 2020 |
| Volume | 9 |
| Issue | 11 |
| Pages | 676 |
| Publication date | 2020-11-13 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | ISPRS International Journal of Geo-Information (JOURNAL) |
| Journal identifiers | ISSN: 2220-9964 • E-ISSN: 2220-9964 |
| Publisher | MDPI AG (PUBLISHER • IT) |
| DOI | 10.3390/ijgi9110676 |
| OpenAlex | W3106365912 |
| Language | EN |
| Citations received | 3 |
| References cited | 31 |
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
Cluster analysis · Data mining · Geography · k-means clustering · Machine learning · Operations research · Particle swarm optimization · Support vector machine · Tourism · Computer Science · Diverse Aspects of Tourism Research · Mathematics · Sport and Mega-Event Impacts · Wine Industry and Tourism
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| Unique citing works | 3 |
|---|---|
| Citations per year | 1 |
| Citation span | 2023 - 2024 (2) |
| Citation velocity | recent |
| Highly cited | No |
| Citation types | Neutral: 3 |