AI Design Approach for Urban Plaza: Human Behavior-Based Algorithm

The model of human behavior concerning urban plaza design emerges that designers mostly approach it with their intuitions. However, the built environment and how humans interact with it have various hidden consequences on users’ behavior. Besides immediate movement patterns and physical interaction, social interactions can be dictated by the built environment’s design. In recent years, with the expansion of design research, human behavior, and technology advancement, machine learning (ML) and multi-agent modeling/system (MAS) have become the driving factors in the evidence design field. Conventional urban designs were driven just by the practicality of the design and in order to satisfy the needs of the program of the space. Moreover, the data collection methods have been done through traditional methods of manual observation, user survey, and cellphone tracking, which are either obsolete, have user errors, or are not suitable for this design purpose. This paper is the first phase of a study investigating the potential of implementation of artificial intelligence as a means of intelligent data-driven design approach for open urban spaces. The aim of the research is to promote the user experience’s positive affordance and enhancing urban open spaces’ livelihood through ML and MAS. This paper aims to investigate the use of visual object recognition algorithms of histogram of oriented gradients (HOG) and edge histogram descriptors (EHD) for human detection in urban plazas. We investigate the impact of each stage of the computation on performance results and conclude that EHD is inaccurate due to the absence of adequate descriptors. HOG results were proven accuracy of 89% on a preprocessed image of MPII Human Pose Dataset containing over 500 annotated human images with a wide range of pose variations and backgrounds.