An Intelligent Pedestrian Model with perception distance and speed to better reproduce traffic dynamics in the single-file movement

In the pedestrian traffic flow, individuals should continuously adjust the movement speed and direction based on the distance to and speed of the surrounding ones. However, during the interaction process, the pedestrian can hardly obtain the real distance and speed instantly, and he/she has to instead rely on perceptual estimates. Current pedestrian flow models typically overlook this crucial relationship between the real and perceived variables. In this paper, we propose an Intelligent Pedestrian model (IPM) incorporating perception headway and speed in the single-file movement scenarios. Experiments to estimate distance and speed are conducted respectively to establish quantitative functions for perception. Individuals tend to overestimate these two variables on average, with perception deviations exhibiting significant variability. Furthermore, it can effectively capture the headway fluctuation characteristics in the stop-and-go flow. Sensitivity analysis reveals that systematic overestimation or underestimation influences the average speed in the flow, while perception heterogeneity predominantly affects the headway fluctuation patterns.