Huang Shenshi, Wei Ruichao, Lian Liping, Lo Siuming, Lu Shouxiang
School of Architectural Engineering, Shenzhen Polytechnic, Shenzhen, Guangdong, China.
School of Automobile and Transportation, Shenzhen Polytechnic, Shenzhen, Guangdong, China.
Heliyon. 2024 May 5;10(10):e30659. doi: 10.1016/j.heliyon.2024.e30659. eCollection 2024 May 30.
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.
近年来，人工智能方法已广泛应用于行人动力学和人群疏散研究。人们提出了不同的神经网络模型，并使用公开可用的行人数据集进行了测试。这些研究表明，不同的神经网络模型在不同的人群场景下表现出很大的性能差异。为了帮助未来的研究选择更合适的模型，本文对神经网络方法在行人动力学研究中的应用进行了综述。这些研究分为两类：行人轨迹预测和行人行为预测。从概念角度以及方法、测量和结果的角度对这两类进行了详细讨论。综述发现，目前行人轨迹预测的主流方法是基于长短期记忆网络（LSTM）的方法，该方法在短期预测中具有足够的准确性。此外，深度神经网络是行人行为预测中最流行的方法。这种方法可以模拟复杂环境中的决策过程，并且有可能彻底改变行人动力学的研究。总体而言，发现仍需要新的方法和数据集来使行人动力学研究系统化，并最终确保其在工业中的广泛应用。
