It is important to understand and predict the behavior of pedestrians, who are vulnerable road users, for the safe implementation of autonomous driving including on ordinary roads. Pedestrians make decisions and move considering other traffic participants, and decision-making models have been proposed to understand pedestrians' behavior. However, as the traffic situations become more complex, such as the number of vehicles, the models become more complicated and the scale of the models become larger, which makes modeling more difficult. To solve the problem, this paper models the crossing decisions of pedestrians using small-scale models by dividing the crossing decisions into decisions for each traffic participant. In the proposed model, crossing decisions of pedestrians are expressed by a Bayesian network, and the divided decisions for each traffic participant are modeled by logistic regression. Data of pedestrians' behavior were obtained using interactive multiplayer simulators, and then the proposed model was trained and validated. The accuracy of the proposed model was as good as that of the conventional model, which indicates the effectiveness of the proposed model. The proposed model succeeded in expressing crossing decisions of pedestrians in complex situations from small-scale models. Therefore, the flexibility of the model is increased in response to changes in the number of vehicles.
