A Study on the Potential of Understanding Pedestrian Space Characteristics Through Analysis Results of Human Flow by AI

Abstract

In recent years, efforts have been made to create interaction and staying spaces in urban areas, and there is an increasing demand for behavioral observation in urban spaces. This paper reports on the development of technology that digitizes pedestrians and cyclists (divided into three categories by vehicle type) in pedestrian spaces from video footage using image recognition technology with AI, building on previous research that automated pedestrian traffic surveys. Based on the digitized results, we converted them into multiple indicators such as speed and stay probability, and considered the possibility of feedback to real-world issues from the characteristics of pedestrian traffic in pedestrian spaces.