Effective models for analysing and predicting pedestrian flow are important to ensure the safety of both pedestrians and other road users. These tools also play a key role in optimising infrastructure design and geometry and supporting the economic utility of interconnected communities. The implementation of city-wide automatic pedestrian counting systems provides researchers with invaluable data, enabling the development and training of deep learning applications that offer better insights into traffic and crowd flows. Benefiting from real-world data provided by the City of Melbourne pedestrian counting system, this study presents a pedestrian flow prediction model, as an extension of Diffusion Convolutional Grated Recurrent Unit (DCGRU) with dynamic time warping, named DCGRU-DTW. This model captures the spatial dependencies of pedestrian flow through the diffusion process and the temporal dependency captured by Gated Recurrent Unit (GRU). Through extensive numerical experiments, we demonstrate that the proposed model outperforms the classic vector autoregressive model and the original DCGRU across multiple model accuracy metrics.

翻译：有效的行人流量分析与预测模型对于保障行人及其他道路使用者的安全至关重要。此类工具在优化基础设施设计与几何布局、支持互联社区的经济效用方面亦发挥着关键作用。全市范围自动行人计数系统的实施为研究者提供了宝贵数据，使得能够开发和训练深度学习应用，从而更深入地理解交通与人群流动规律。本研究利用墨尔本市行人计数系统提供的真实数据，提出一种行人流量预测模型，该模型作为扩散卷积门控循环单元（DCGRU）的扩展，结合动态时间规整技术，命名为DCGRU-DTW。该模型通过扩散过程捕捉行人流量的空间依赖性，并利用门控循环单元（GRU）捕获时间依赖性。通过大量数值实验，我们证明所提模型在多项精度指标上均优于经典向量自回归模型及原始DCGRU模型。