Pedestrian spaces are increasingly becoming popular locations for shopping, recreation, festivities, and other social activities. Therefore, an improved understanding of the factors that make walking environments enjoyable and safe is essential. Most existing studies focus on modelling walking behaviours of individual pedestrians. However, most people participate in these activities as parts of social groups. Although the movement and choice behaviours of pedestrians in social groups differ from those of individuals, a model featuring group movements has not been developed. This study uses neural networks to analyse the effects of variables influencing pedestrian movements of social groups and predict the variation in walking dynamics. A top-view video was used to extract the trajectories of pedestrian groups. After identifying the social groups in a crowd, the movement characteristics, pedestrian–environment interaction, inter-pedestrian interaction, intra-group relationship, and inter-group relationship of all group members were calculated and considered in the model. After a variable selection process using neural networks, a neural network model was developed featuring variables that are strongly related to the lateral or longitudinal changes in the individual’s walking speed. The current movement condition, presence of obstacles nearby, impending collisions, current position and velocity of other group members, and following behaviour were found to impact a pedestrian’s walking dynamics. The proposed model can predict the pedestrian density and distribution according to a space function, contributing to better crowd management and efficient design and renovation of pedestrian spaces. Furthermore, the variable selection method can optimise and simplify other pedestrian behaviour prediction models.

Commercial areas, especially urban ones with numerous buildings, are becoming increasingly prone to congestion because of their popularity. Visual inspections show that interactions between pedestrians and building entrances affect the distribution of pedestrian trajectories, which influences the utility of pedestrian spaces and the design of urban shopping areas. Herein, we analyse the dynamics of pedestrian deviations around building entrances. We used a video recorded using an unmanned aerial vehicle to determine pedestrian trajectories in a Chinese commercial walking space. First, the candidate variables affecting deviation behaviours were determined via correlation testing. Second, two regression models were developed by considering the deviation behaviours of pedestrians walking past a building entrance. The models suggest that the starting position of a pedestrian's deviation, the total pedestrian flow at the building entrance, the density in an area in the vicinity of the entrance, and the number of interacting pedestrians impact the total distance traversed during path deviation.