A Visualized Research of Human’s Behavior in Public Spaces Based on Deep Learning: A Case Study of Weijin Road Campus, Tianjin University

Public space is an important part of contemporary cities, and researches based on human spatial behavior can provide a reference for its optimal design. Currently, research methods which obtain pedestrian trajectories with video data to further evaluate public spaces are emerging. However, the current methods have the disadvantage of slow computing speed and the inability to obtain results in real time. A computer vision algorithm based on deep learning using Python can obtain the trajectory of pedestrians in the public space in real time. Citing the three public spaces in the Weijin Road campus of Tianjin University as examples, this research uses the trajectory data to draw a heat map of crowd distribution and a heat map of walking speed to respectively characterize the connection strength between the entrances and exits of the public spaces and the spatial distribution differences of the chosen paths of pedestrians with different speeds. Finally, it puts forward the possibility of this method in assisting design, its limitations and improvement strategies.