Fuzzy Prediction of Pedestrian Steering Behavior with Local Environmental Effects

This research focuses on prediction of pedestrian walking paths in indoor public environments during normal and non-panic situations. The aim is to incorporate uncertain and non-precise aspects of pedestrian interaction with the environment to enhance steering behavior modeling. The proposed model introduces a fuzzy logic framework to predict the impact of environmental stimuli within a pedestrian’s field of view on movement direction. The environment is treated as a set of discrete attractions and repulsions. Attractive and repulsive effects of the surrounding environment, which drive the pedestrian to select next step position, are quantified by social force method. A high flow corridor in an office is considered for the case study. Stochastic simulation is used to generate walking trajectories and calculate a dynamic contour map of environmental stimuli in each step. To verify the simulation results and gain a better insight into the problem, a dataset defining walking trajectories of 25 participants passing through that hallway was collected using motion tracking system. Results demonstrate a strong correlation between real data and simulated results.