Sensor Network Based on Machine Learning in Tourist Area.

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. [ABSTRACT FROM AUTHOR]