文献类型：期刊文献

英文题名：Enhancing tourism routes optimisation accuracy as well as dynamic adjustments using data analytics approach

作者：Liu, Qinyun[1]; Hao, Zhicheng[1]

第一作者：Liu, Qinyun

机构：[1] Tourism College, Beijing Union University, Beijing, 100101, China

第一机构：北京联合大学旅游学院

通讯机构：[1]Tourism College, Beijing Union University, Beijing, 100101, China|[1141732]北京联合大学旅游学院;[11417]北京联合大学;

年份：2026

卷号：27

期号：51

起止页码：17-35

外文期刊名：International Journal of Information and Communication Technology

收录：EI(收录号：20262120774185);Scopus(收录号：2-s2.0-105039766725)

语种：英文

外文关键词：Artificial intelligence - Behavioral research - Computation theory - Data accuracy - Graph theory - Tourism - Transportation routes

摘要：Tourism route planning has traditionally emphasised shortest-path optimisation, often over-looking the importance of enhancing tourists' overall experiences. Many travellers rely on user-generated content to guide their journeys, yet manually searching and adjusting routes in real-time can be inefficient and inaccurate. This study focuses on building a high-quality database to support model training for tourism route optimisation and dynamic adjustments. By leveraging graph theory and the Floyd-Warshall algorithm, the proposed approach integrates various tourism-related data factors to enhance route planning accuracy based on personalised preferences. The high-quality dataset, sourced from travel agencies and user-generated data, ensures the algorithm's adaptability in real-world scenarios. The model is tested on an online tourism platform, with its effectiveness evaluated through a framework grounded in tourism theories and user behaviour research. The results demonstrate significant improvements in both route planning accuracy and the efficiency of real-time adjustments when travellers modify their plans mid-journey. Copyright ? The Author(s) 2026. Published by Inderscience Publishers Ltd. This is an Open Access Article distributed under the CC BY-NC-ND license. (http://creativecommons.org/licenses/by-nc-nd/4.0/).