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<p>This study investigates the use of wearable positioning systems supported by sensor networks for rural tourism management. A novel Advanced Kalman Filter Tourism based on Sensor Network (AKFT-SN) is proposed to integrate infrastructure monitoring, tourist interaction platforms, and environmental sensing. The model predicts visitor flows, restricts overcrowding through traffic detection, and supports real-time health and safety monitoring. Experimental evaluation using a rural tourism dataset (2018–2023) demonstrates that AKFT-SN achieves superior accuracy (96.5%) compared with existing methods such as Support Vector Machine (SVM), Internet of Things (IoT), Convolutional Neural Network (CNN), and Apriori algorithms. The results highlight its potential for optimizing resource allocation, enhancing visitor experiences, and promoting sustainable rural tourism development.</p>

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Application of wearable positioning system based on sensor network in rural tourism management

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年1月17日

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  • Zixuan Ding ,
  • Yufeng Li,
  • Yuanyuan Ma

摘要

This study investigates the use of wearable positioning systems supported by sensor networks for rural tourism management. A novel Advanced Kalman Filter Tourism based on Sensor Network (AKFT-SN) is proposed to integrate infrastructure monitoring, tourist interaction platforms, and environmental sensing. The model predicts visitor flows, restricts overcrowding through traffic detection, and supports real-time health and safety monitoring. Experimental evaluation using a rural tourism dataset (2018–2023) demonstrates that AKFT-SN achieves superior accuracy (96.5%) compared with existing methods such as Support Vector Machine (SVM), Internet of Things (IoT), Convolutional Neural Network (CNN), and Apriori algorithms. The results highlight its potential for optimizing resource allocation, enhancing visitor experiences, and promoting sustainable rural tourism development.