Review of the application of neural network approaches in pedestrian dynamics studies.

In recent years, artificial intelligence methods have been widely used in the study of pedestrian dynamics and crowd evacuation. Different neural network models have been proposed and tested using publicly available pedestrian datasets. These studies have shown that different neural network models present large performance differences for different crowd scenarios. To help future research select more appropriate models, this article presents a review of the application of neural network methods in pedestrian dynamics studies. The studies are classified into two categories: pedestrian trajectory prediction and pedestrian behavior prediction. Both categories are discussed in detail from a conceptual perspective, as well as from the viewpoints of methodology, measurement, and results. The review found that the mainstream method of pedestrian trajectory prediction is currently the LSTM-based method, which has adequate accuracy for short-term predictions. Furthermore, the deep neural network is the most popular method for pedestrian behavior prediction. This method can emulate the decision-making process in a complex environment, and it has the potential to revolutionize the study of pedestrian dynamics. Overall, it is found that new methods and datasets are still required to systemize the study of pedestrian dynamics and eventually ensure its wide-scale application in industry.