用扩散门控循环单元模型预测行人流量，提升城市交通安全性与规划效率。
Pedestrian Volume Prediction Using a Diffusion Convolutional Gated Recurrent Unit Model
- 融合扩散过程与门控循环单元，捕捉行人流动的空间与时间依赖性。
- 在墨尔本行人计数数据上，优于传统向量自回归和原版DCGRU模型。
- 适合城市交通规划、智慧城市建设及行人流量监控研究者使用。
高效分析与预测行人流量的模型对保障行人及其他道路使用者的安全至关重要，同时在优化基础设施设计与布局、支持互联社区的经济效能方面发挥关键作用。城市级自动行人计数系统的实施为研究人员提供了宝贵数据，推动了深度学习应用的发展，从而更深入理解交通与人群流动。基于墨尔本行人计数系统提供的真实世界数据，本研究提出一种改进的扩散卷积门控循环单元模型（DCGRU-DTW），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）建模时间依赖性。通过大量数值实验验证，所提模型在多个评估指标上均优于经典向量自回归模型与原始DCGRU模型。
原文摘要 · Abstract (English)
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.
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