Direction-Decision Learning Based Pedestrian Flow Behavior Investigation - Zhe Zhang; Limin Jia (IEEE Access, 2020)
To investigate the pedestrian flow behavior in corridors, a microscopic simulation model of pedestrian flow is proposed in this paper based on the desired-direction-decision learning and social force model. The proposed model is composed of two parts: direction-decision and walking behavior decision. First, the decision tree model is proposed to predict the walking direction of pedestrians by comparing the prediction and simulation performance of three different models. Then, to avoid collisions between pedestrians and obstacles, the acceleration model and the collision avoidance model are proposed to compute the walking speed. Finally, an computational experiment is conducted to simulate crowd movement in corridors. The experimental results show that the proposed model can suggest the shortest overtaking route for individual pedestrians among four models, and the speed-density relationship fits the experimental data well. The sensitive analysis shows that the lanes in bidirectional pedestrian flow can be formed much more easily if the pedestrians have higher direction changing frequency, and there is an optimal visibility field (2.8) to realize the highest traffic efficiency.