Biology subjects

Nikmanesh, M.

Publications and source records attributed to Nikmanesh, M..

2 recordsLinked to original sources

bioRxiv2024

Space matters: virtual pedestrians with mobility constraints affect individuals' avoidance behaviours

Walking in urban settings requires people to negotiate crowds. In these situations, people typically want to maintain a level of personal space around themselves. Recent work on one-versus-one interactions demonstrated that whether one of the pedestrians looked distracted or interacted with an object (e.g., stroller, bike) predicted the medial-lateral separation between them as they walked past each other. However, this work did not distinguish between the type of object interaction (or mobility constraint) and thus, it is unclear whether different constraints have different effects on avoidance behaviours. Here we tested the hypothesis that the type of object an approaching pedestrian held or pushed would affect the extent of path deviation, which would also depend on whether that pedestrian was distracted. To address this hypothesis, we created an immersive virtual environment that consisted of a 3.5-m-wide paved urban path. Participants had to walk and avoid colliding with approaching virtual pedestrians that often held a shopping bag or pushed a bike or stroller while looking straight ahead or off to the side as if distracted. Distraction did not affect avoidance behaviours. However, participants increased medial-lateral separation with the virtual pedestrian at the time of crossing when a stroller was present compared to the other mobility constraints. The type of mobility constraint also differentially affected onset of deviation and rate of progression before and after a path deviation. These results support the idea that characteristics of the obstacle to avoid (in this case, a virtual pedestrian) influence collision avoidance behaviours.

neuroscience↗

bioRxiv2024

Identifying factors that contribute to collision avoidance behaviours while walking in a natural environment

Busy walking paths, like in a park, a sidewalk in a city centre, or a shopping mall, frequently necessitate collision avoidance behaviour. Lab-based research has shown how a variety of situation-specific factors (e.g., distraction, object/pedestrian proximity) and person-specific factors (e.g., pedestrian size, age), typically studied independently, affect avoidance behaviour. What happens in the real world is unclear. Thus, we filmed unscripted pedestrian walking behaviours on a busy [~]3.5 m urban path adjacent to the water. We leveraged deep learning algorithms to identify and extract walking trajectories of pedestrians and had unbiased raters characterize interaction details. Here we analyzed over 500 situations where two pedestrians approached each other from opposite ends (i.e., one-on-one pedestrian interactions). We found that smaller medial-lateral distance between approaching pedestrians and a lower number of surrounding pedestrians (i.e., smaller crowd size) predicted an increase in the likelihood of a subsequent path deviation. Furthermore, we found that whether a pedestrian looked distracted or held, pushed, or pulled something while walking predicted the medial-lateral distance between pedestrians at the time of crossing. Although pedestrians maintained a larger personal space boundary compared to lab settings, this is likely because of the outdoor paths width. Overall, our results suggest that collision avoidance behaviours in lab and real-world environments share similarities and offer insights relevant to developing more accurate computational models for realistic pedestrian movement.

neuroscience↗