A deep-learning-based approach for simulating pedestrian turning flow

The applications of artificial intelligence technology in pedestrian and evacuation dynamics research have achieved gratifying progress recent years. Benefiting from the non-linear fitting ability of deep learning algorithm, such learning-based method might have better performance in modeling the individual micro behaviors comparing to traditional pedestrian and evacuation dynamics models. Hence, this paper proposes a deep-learning-based pedestrian dynamics model which can simulate the pedestrian flow in right-angled corridors. The training process is conducted with a deep learning framework composed of two functional layers namely Scene Perception layer (SP layer) and Motion Dynamic layer (MD layer). The input features of the SP layer and MD layer are obtained from a defined ‘sense field’ which captures information about walking facility structures and neighbors. Dataset generated from twelve groups of pedestrian turning flow experiments is used for data training. The initial experiments are further adopted to evaluate the model at both qualitative and quantitative level from pedestrian motion data simulated by trained model. Qualitatively, the simulation results align with the corresponding experiments in terms of fundamental diagrams in different measurement areas and the headway distance-velocity relationship, demonstrating realistic motion characteristics, proper reactions to changeable walking facility structures and collision avoidance tendencies of agents driven by our model. For quantitative evaluation of the model precision, two indicators respectively calculate the duration and trajectory disparities are introduced and both of them yield relatively small values. Moreover, eight groups of external experiments completely independent from training data are introduced to validate the generalization ability of our model, the simulation results are found to match reality well without prior knowledge. The proposed framework presented is a success trial for simulating pedestrian turning flow and have the potential to be adapted to different scenarios. Outcomes presented will be of beneficial guidance for different engineering application such as performance-based fire design and crowd management.