Abstract
This study used space syntax to discuss user movement dynamics and crowded hot spots in a commercial area. Moreover, it developed personas according to its onsite observations, visualized user movement data, and performed a deep-learning simulation using the generative adversarial network (GAN) to simulate user movement in an urban commercial area as well as the influences such move might engender. From a pedestrian perspective, this study examined the crowd behavior in a commercial area, conducted an onsite observation of people’s spatial behaviors, and simulated user movement through data-science-driven approaches. Through the analysis process, we determined the spatial differences among various roads and districts in the commercial area, and according to the user movement simulation, we identified key factors that influence pedestrian spatial behaviors and pedestrian accessibility. Moreover, we used the deformed wheel theory to investigate the spatial structure of the commercial area and the synergetic relationship between the space and pedestrians; deformed wheel theory presents the user flow differences in various places and the complexity of road distribution, thereby enabling relevant parties to develop design plans that integrate space and service provision in commercial areas. This research contributes to the interdisciplinary study of spatial behavior analysis and simulation with machine learning applications.
Highlights
The spatial movement of people has been presented in various forms in different fields
This study determined the spatial differences of user movements in local and global environments in a commercial area, identified the key factors affecting pedestrian spatial behaviors, and discerned pedestrian accessibility within the space
This study used the global integration of space syntax to analyze hot spots where pedestrians most frequently stopped; the Keras-generative adversarial network (GAN) to simulate three movement track graphic results; and the deformed wheel theory to investigate the relationship between the commercial area and pedestrians
Summary
The spatial movement of people has been presented in various forms in different fields. Approaches to demonstrating and discussing movement results are determined by the relationships perceived by researchers between people and spaces. In addition to the quantitative analysis of spatial data, this study discussed from a pedestrian perspective the relationship between crowds and spaces in commercial areas as well as observed the spatial behaviors of pedestrians onsite. Underpinned by a data-science-driven approach, this study simulated pedestrian movement in different scenarios. This study determined the spatial differences of user movements in local and global environments in a commercial area, identified the key factors affecting pedestrian spatial behaviors, and discerned pedestrian accessibility within the space. This study is to discuss the predictions of user spatial behavior and pedestrian movement simulations within the established business area. For the movement of hanging around and the consumption movement except walking are not discussed in this study
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More From: International Journal of Advanced Computer Science and Applications
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