pp. 2867-2878
S&M3706 Research Paper of Special Issue https://doi.org/10.18494/SAM4887 Published: July 24, 2024 Sensor Network Based on Machine Learning in Tourist Area [PDF] Chengxiang Wang, Qilong Chen, Pingrong He, Wei-Ling Hsu, and Hsin-Lung Liu (Received January 3, 2024; Accepted June 21, 2024) Keywords: traffic flow, tourist area, machine learning, tourist awareness
The sensor network is an important part of the real-time monitoring system of traffic flow in tourist areas. In this study, we built a traffic flow prediction model based on machine learning to assist the construction and optimization of a sensor network. Using the sensor network, we developed a real-time monitoring system of traffic flow in tourist areas. Factors affecting traffic flow in the study area (Hongze Lake, China) were determined through interviews with 50 tourists. Using the scores of the factors, machine learning models such as random forest, decision tree, and support vector machine were constructed to predict traffic flow, and the result was used to design an appropriate sensor network. The predictions of three machine learning methods were compared to build a traffic flow prediction model. After comparing the predicted results with the tested ones, the performance of the prediction model was validated. Referring to such results, we selected the area near a square and bridges and the intersections of roads as key areas to install sensors for monitoring traffic flow. The system is being implemented in the study area to renovate the area to promote further growth of the tourism industry.
Corresponding author: Wei-Ling Hsu and Hsin-Lung Liu![]() ![]() This work is licensed under a Creative Commons Attribution 4.0 International License. Cite this article Chengxiang Wang, Qilong Chen, Pingrong He, Wei-Ling Hsu, and Hsin-Lung Liu, Sensor Network Based on Machine Learning in Tourist Area, Sens. Mater., Vol. 36, No. 7, 2024, p. 2867-2878. |