Fan Jin, Weng Wenchao, Chen Qikai, Wu Huifeng, Wu Jia
Hangzhou Dianzi University, Hangzhou, China; Zhejiang Provincial Key Laboratory of Industrial Internet in Discrete Industries, Hangzhou, China.
Zhejiang University of Technology, Hangzhou, China.
Neural Netw. 2025 Mar;183:106941. doi: 10.1016/j.neunet.2024.106941. Epub 2024 Dec 2.
Traffic flow prediction is the foundation of intelligent traffic management systems. Current methods prioritize the development of intricate models to capture spatio-temporal correlations, yet they often neglect the exploitation of latent features within traffic flow. Firstly, the correlation among different road nodes exhibits dynamism rather than remaining static. Secondly, traffic data exhibits evident periodicity, yet current research lacks the exploration and utilization of periodic features. Lastly, current models typically rely solely on historical data for modeling, resulting in the limitation of accurately capturing future trend changes in traffic flow. To address these findings, this paper proposes a Periodic Dynamic Graph to Sequence Model (PDG2Seq) for traffic flow prediction. PDG2Seq consists of the Periodic Feature Selection Module (PFSM) and the Periodic Dynamic Graph Convolutional Gated Recurrent Unit (PDCGRU) to further extract the spatio-temporal features of the dynamic real-time traffic. The PFSM extracts learned periodic features using time points as indices, while the PDCGRU leverages the extracted periodic features from the PFSM and dynamic features from traffic flow to generate a Periodic Dynamic Graph for extracting spatio-temporal features. In the decoding phase, PDG2Seq utilizes periodic features corresponding to the prediction target to capture future trend changes, leading to more accurate predictions. Comprehensive experiments conducted on four large-scale datasets substantiate the superiority of PDG2Seq over existing state-of-the-art baselines. Related codes are available at https://github.com/wengwenchao123/PDG2Seq.
交通流预测是智能交通管理系统的基础。当前的方法优先发展复杂模型以捕捉时空相关性，但它们往往忽视了对交通流中潜在特征的挖掘。首先，不同道路节点之间的相关性呈现动态变化而非保持静态。其次，交通数据呈现出明显的周期性，但当前研究缺乏对周期特征的探索和利用。最后，当前模型通常仅依赖历史数据进行建模，导致在准确捕捉交通流未来趋势变化方面存在局限性。为了解决这些问题，本文提出了一种用于交通流预测的周期动态图到序列模型（PDG2Seq）。PDG2Seq由周期特征选择模块（PFSM）和周期动态图卷积门控循环单元（PDCGRU）组成，以进一步提取动态实时交通的时空特征。PFSM使用时间点作为索引来提取学习到的周期特征，而PDCGRU利用从PFSM提取的周期特征和交通流的动态特征来生成一个周期动态图，用于提取时空特征。在解码阶段，PDG2Seq利用与预测目标对应的周期特征来捕捉未来趋势变化，从而实现更准确的预测。在四个大规模数据集上进行的综合实验证实了PDG2Seq相对于现有最先进基线的优越性。相关代码可在https://github.com/wengwenchao123/PDG2Seq获取。
