A Machine Learning and Computer Vision Study of the Environmental Characteristics of Streetscapes That Affect Pedestrian Satisfaction

Abstract

Pedestrian-friendly cities are a recent global trend due to the various urbanization problems. Since humans are greatly influenced by sight while walking, this study identified the physical and visual characteristics of the street environment that affect pedestrian satisfaction. In this study, vast amounts of visual data were collected and analyzed using computer vision techniques. Furthermore, these data were analyzed through a machine learning prediction model and SHAP algorithm. As a result, every visual feature of the streetscape, for example, the visible area and urban design quality, had a greater effect on pedestrian satisfaction than any physical features. Therefore, to build a street with high pedestrian satisfaction, the perspective of pedestrians must be considered, and wide sidewalks, fewer lanes, and the proper arrangement of street furniture are required. In conclusion, visually, low enclosure, adequate complexity, and large green areas combine to create a highly satisfying pedestrian walkway. Through this study, we could suggest an approach from a visual perspective for the pedestrian environment of the street and see the possibility of using computer vision techniques.