Year: 2024
Type: article
Abstract: ABSTRACT The digital economy has revolutionized the tourism industry by integrating Semantic Internet of Things (IoT) technologies for enhanced data analysis. This paper proposes a comprehensive method for analyzing tourism flow using Semantic IoT. The method combines semantic data processing with advanced machine learning techniques, specifically stacked autoencoders and long short‐term memory (LSTM) models, to understand and predict changes in passenger flow. The approach addresses challenges of high dimensionality and complex temporal characteristics in tourism data, offering significant support for tourism management, improving operational efficiency, and enhancing the visitor experience. Experimental results demonstrate the effectiveness of the proposed method in analyzing and predicting tourism passenger flow data, providing accurate and insightful trends that are crucial for decision‐making in the tourism industry.
Source: Internet Technology Letters
DOI: 10.1002/itl2.597
Authors: Lu Wang, B. D. Parameshachari, Junfeng Miao
Language: English
Subfield: Sociology and Political Science
Field: Social Sciences
Domain: Social Sciences
Open Access status: bronze
OpenAlex ID: W4403742094