Experimental study on the evading behavior of individual pedestrians when confronting with an obstacle in a corridor
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In this paper, controlled experiments have been conducted to make deep analysis on the obstacle evading behavior of individual pedestrians affected by one obstacle. Results of Fourier Transform show that with the increase of obstacle width, the frequency and amplitude of body sway would barely be affected while the lateral deviation of walking direction would largely increase. On the one hand, the relation among the extracted gait features including body sway, stride length, frequency and speed has been illustrated. On the other hand, the walking direction can be featured by three critical evading points where apparent change of walking direction could be observed. Gaussian function has been used to fit the walking direction, thus allowing to estimate the three critical points and examine their variation with the increase of obstacle size. Furthermore, the direct-indirect evading and left-right turning preference as well as the possible psychological motivations behind have been analyzed. It is indicated that direct-evading pedestrians have a higher walking efficiency and right-turning pedestrians have a stronger tendency to behave `direct'. Results of this paper are expected to provide practical evidence for the modeling of pedestrian dynamics affected by obstacles.
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Data-driven simulation of pedestrian collision avoidance with a nonparametric neural network
A nonparametric neural network trained on high-precision lab trajectories simulates pedestrian collision avoidance with one obstacle from any direction.
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