Effect of Visual Quality of Street Space on Tourists’ Stay Willingness in Traditional Villages—Empirical Evidence from Huangcun Village Based on Street View Images and Machine Learning

As the texture skeleton of the traditional village, the street space is the main area for tourists to visit in traditional villages; it is regarded as the spatial conversion place of human flow and the space frequently visited by tourists. Accumulating evidence shows that the visual quality of street spaces has an effect on pedestrians’ walking behaviors in urban areas, but this effect in traditional villages needs to be further explored. This paper takes Huangcun Village, Yixian County, Huangshan City, as the research area to explore the influence of the objective visual factors of street spaces on tourists’ subjective stay willingness. First, an evaluation system of the visual quality of street spaces was developed. With the assistance of computer vision and deep learning technologies, semantic segmentation of Huangcun Village street view images was performed to obtain a visual quality index and then calculate the descriptive index of Huangcun Village’s street space. Then, combining the data of tourists’ stay willingness with the visual quality of the street space, the overall evaluation results and space distribution of tourists’ stay willingness in Huangcun Village were predicted using the Trueskill algorithm and machine learning prediction model. Finally, the influence of the objective visual quality of the street space on tourist subjective stay willingness was analyzed by correlation analysis. This research could provide some useful information for street space design and tourism planning in traditional villages.