Entropy-based guidance and predictive modelling of pedestrians' visual attention in urban environment

Selective visual attention determines what pedestrians notice and ignore in urban environment. If consistency exists between different individuals' visual attention, designers can modify design by underlining mechanisms to better meet user needs. However, the mechanism of pedestrians' visual attention remains poorly understood, and it is challenging to forecast which position will attract pedestrians more in urban environment. To address this gap, we employed 360 degrees video and immersive virtual reality to simulate walking scenarios and record eye movement in 138 participants. Our findings reveal a remarkable consistency in fixation distribution across individuals, exceeding both chance and orientation bias. One driver of this consistency emerges as a strategy of information maximization, with participants tending to fixate areas of higher local entropy. Additionally, we built the first eye movement dataset for panorama videos of diverse urban walking scenes, and developed a predictive model to forecast pedestrians' visual attention by supervised deep learning. The predictive model aids designers in better understanding how pedestrians will visually interact with the urban environment during the design phase. The dataset and code of predictive model are available at https://github.com/LiamXie/UrbanVisualAttention