Traffic demand prediction based on

spatial-temporal guided multi-graph Sandwich-Transformer

基于时空引导的多图层夹层变压器的交通需求预测

Authors: Wen Yanjie; Li Zhihong; Wang Xiaoyu; Xu Wangtu

作者：文彦杰，李志宏，王晓宇，许旺土

Abstract：The ability of spatial-temporal traffic demand prediction is crucial for urban computing, traffic management and future autonomous driving. In this paper, a novel Spatial-Temporal Guided Multi-graph Sandwich-Transformer (STGMT) is suggested to address the ubiquitous spatial-temporal heterogeneity in traffic demand forecasting. Compared to the original Transformer, we employ Time to Vector (Time2Vec) and Node to Vector (Node2Vec) in the embedding layer to obtain universal representations for temporal nodes and spatial nodes, respectively, which are then combined to form Spatial-Temporal Embedding (STE) blocks. The STE guides the attention mechanism, maintaining a unique parameter space for spatial-temporal nodes and enabling the learning of node-specific patterns. In STGMT, we develop Multi-head Temporal Attention (MTA) and Multi-head Temporal Interactive Attention (MTIA) for extracting temporal features, while Multi-head Spatial Attention (MSA) is employed for extracting spatial features. Furthermore, MSA incorporates both the accessibility graph determined by road topology and the similarity graph determined by specific traffic events to characterize the pairwise relationships among spatial nodes. Various attentions and feed-forward layers are rearranged and combined to form the Sandwich-Transformer. Extensive experiments are conducted on public datasets of node-level tasks of two different types (highway and urban) and indicate that the STGMT outperforms state-of-the-art models. The proposed STGMT effectively addresses the ubiquitous spatial-temporal heterogeneity challenge in traffic demand forecasting, thereby enhancing the accuracy of traffic demand prediction and offering valuable guidance for traffic planning and operations. Our code and data are open source at https://github.com/YanJieWen/STGMT-Tensorflow-implementation.

摘要：交通需求的时空预测能力对于城市运行、交通管理和未来的自动驾驶都至关重要。本文提出了一种新颖的时空引导的多图层夹层变压器（STGMT），可以解决交通需求预测中无处不在的时空异质性。与以往的变压器不同，我们在嵌入层中采用了时间向量（Time2Vec）和节点向量（Node2Vec），使用通用标志分别表示时间节点和空间节点，并将其结合构成时空嵌入层。时空嵌入层可以引导注意力机制，时空节点拥有独立的参数空间，同时可以学习特定的的节点模式。在这款时空引导的多图层夹层变压器中，我们开发了多头注意力机制和多头交互注意力机制，可以提取时间信息，同时也采用了多端空间监控来提取空间信息。此外，道路拓扑会影响可达性图层，而具体交通时间则会影响相似性图层，多端空间监控将二者结合，可以描述空间节点之间的配对关系。夹层变压器可以将各种监控图层和前馈图层进行重新排列组合，并在两种不同类型（公路和城市）的节点级任务的公共数据集上进行了广泛的实验，其表明时空引导的夹层变压器是最先进的模型。时空引导夹层变压器有效地解决了交通需求预测中无处不在的时空异质性难题，提高了交通需求预测的准确性，为交通规划和运营提供了有益的指导。我们的代码和数据为公开数据，详情可查阅：https://github.com/YanJieWen/STGMT-Tensorflow-implementation

Keyword: intelligent transportation system; multi-head attention mechanism; sandwich-Transformer; spatial-temporal learning; meta learning

关键词：智能交通系统，多端监控机制，夹层变压器，时空学习，元学习

来源：Information Sciences

https://www.sciencedirect.com/science/article/abs/pii/S002002552300854X?via%3Dihub

发表时间：2023年6月20日

检索：高贺元

翻译：颜彬彬

一审：许晓雪

二审：彭莉

三审：罗玲娟

上传发布：姜浩