Research on the Impact of Human–Robot Interaction on Pedestrian Circulation Performance in Urban Public Spaces: A Simulation Analysis Based on AnyLogic

With the increasing deployment of service robots in urban public spaces, this study examines how robot proportion (RP) and composite interaction scenarios affect pedestrian circulation performance. An AnyLogic agent-based model of Quancheng Square, Jinan, was developed using a fixed total population of 10,000 agents. Three composite interaction scenarios (CIS-1–CIS-3) jointly vary interaction distance and duration. Human–robot mixed density (HRMD) and average pedestrian speed (APS) were evaluated over a common post-transient interval. To address population composition confounding and stochastic variability, an additional fixed-pedestrian-demand validation under CIS-3 used 10 independent random-seed runs per condition. Across the original runs, HRMD generally increased with RP, whereas APS showed a non-monotonic pattern. In the validation experiment, APS decreased monotonically as robot number increased when pedestrian demand was held constant, while the fixed-total design showed a high robot-level recovery. This contrast indicates that the apparent APS recovery was partly associated with reduced pedestrian demand. Overall, pedestrian circulation patterns were sensitive to both robot deployment and population composition.