Deviation of Pedestrian Path due to the Presence of Building Entrances

Abstract

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.

Data science for pedestrian and high street retailing as a framework for advancing urban informatics to individual scales

“…Several recent video-based experiments have looked into the motion of individuals and crowds within outdoor events (Helbing & Mukerji, 2012 ; Johansson et al, 2008 ), and these would seem to resonate with adjacent characteristics of retail high streets during mass events such as celebrations and festivals (Batty et al, 2003a , b ). Recent work by Sun et al ( 2021 ) has shown how commercially-available drone platforms can be used to analyze high street movement, in their case to examine the impact of building entrance geometry on the movement patterns of people on the street outside. Methods for machine learning from video could be useful in supporting knowledge-building of urban journeys in two key ways: (1) automated extraction of journey data from video footage of real streetscape scenes, and (2) the performance of streetscape audits to classify and label urban environment scenes in video.…”

Functional Objects in Urban Walking Environments and Pedestrian Trajectory Modelling

“…To apply the knowledge-driven approach in the pedestrian–object relation framework, a specialized hand-crafted model is required for every relation type. Yue et al, Li et al, and Sun et al [ 25 , 26 , 27 ] have illustrated the effort required to develop such a model.…”