Song Chunqiao
College of Tourism, Xinyang Normal University, Xinyang, Henan, China.
PLoS One. 2024 Dec 10;19(12):e0308135. doi: 10.1371/journal.pone.0308135. eCollection 2024.
In the face of increasingly diverse demands from tourists, traditional methods for scenic route planning often struggle to meet these varied needs. To address this challenge and enhance the overall service quality of tourist destinations, as well as to better understand individualized preferences of visitors, this study proposes a novel approach to scenic route planning and itinerary customization based on multi-layered mixed hypernetwork optimization. Firstly, an adaptive multi-route feature extraction method is introduced to capture personalized demands of tourists. Subsequently, a personalized tourist inference method based on a multi-layered mixed network is presented, utilizing the extracted personalized features to infer the true intentions of the tourists. Lastly, we propose a hypernetwork optimized route planning method, incorporating the inference results and personalized features to tailor the optimal touring paths for visitors. The results of our experiments underscore the efficacy of our methodology, attaining an accuracy score of 0.877 and an mAP score of 0.881 and outperforming strong competitors and facilitating the design of optimal paths for tourists.
面对游客日益多样化的需求，传统的景区路线规划方法往往难以满足这些多样的需求。为应对这一挑战，提高旅游目的地的整体服务质量，并更好地了解游客的个性化偏好，本研究提出了一种基于多层混合超网络优化的景区路线规划和行程定制新方法。首先，引入一种自适应多路线特征提取方法来捕捉游客的个性化需求。随后，提出一种基于多层混合网络的个性化游客推理方法，利用提取的个性化特征来推断游客的真实意图。最后，我们提出一种超网络优化路线规划方法，将推理结果和个性化特征结合起来，为游客量身定制最优游览路径。我们的实验结果强调了我们方法的有效性，准确率达到0.877，平均精度均值（mAP）得分达到0.881，优于强大的竞争对手，并有助于为游客设计最优路径。
