Abstract
AbstractThe pedestrians always want to avoid collision with others. A data-driven simulation model of collision avoidance behavior is proposed in this paper. In order to predict the walking speed and direction of pedestrians, the machine learning method is used to learn the movement rule of pedestrians from trajectory data. First, the features are selected based on the microscopic interaction analysis and we extract the features from trajectory data. Secondly, the decision tree model is used to predict the walking direction and speed of pedestrians and the simulation model is proposed to smooth the speed evolution. Finally, an experimental study is conducted to simulate the collision avoidance behavior of pedestrians. The experimental results show that the proposed simulation model can provide walking direction and speed decision strategy which contributes to the generation of the natural collision free path.KeywordsPedestriansCollision avoidanceSimulationMachine learningDecision tree
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