Knowledge Commons of Institute of Automation,CAS
Meta Graph Transformer: A Novel Framework for Spatial-Temporal Traffic Prediction | |
Ye, Xue1,2![]() ![]() ![]() | |
发表期刊 | NEUROCOMPUTING
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ISSN | 0925-2312 |
2022-06-28 | |
卷号 | 491页码:544-563 |
文章类型 | 原创性研究 |
摘要 | Accurate traffic prediction is critical for enhancing the performance of intelligent transportation systems. The key challenge to this task is how to properly model the complex dynamics of traffic while respecting and exploiting both spatial and temporal heterogeneity in data. This paper proposes a novel framework called Meta Graph Transformer (MGT) to address this problem. The MGT framework is a generalization of the original transformer, which is used to model vector sequences in natural language processing. Specifically, MGT has an encoder-decoder architecture. The encoder is responsible for encoding historical traffic data into intermediate representations, while the decoder predicts future traffic states autoregressively. The main building blocks of MGT are three types of attention layers named Temporal Self-Attention (TSA), Spatial Self-Attention (SSA), and Temporal Encoder-Decoder Attention (TEDA), respectively. They all have a multi-head structure. TSAs and SSAs are employed by both the encoder and decoder to capture temporal and spatial correlations. TEDAs are employed by the decoder, allowing every position in the decoder to attend all positions in the input sequence temporally. By leveraging multiple graphs, SSA can conduct sparse spatial attention with various inductive biases. To facilitate the model’s awareness of temporal and spatial conditions, Spatial–Temporal Embeddings (STEs) are learned from external attributes, which are composed of temporal attributes (e.g. sequential order, time of day) and spatial attributes (e.g. Laplacian eigenmaps). These embeddings are then utilized by all the attention layers via meta-learning, hence endowing these layers with Spatial–Temporal Heterogeneity-Aware (STHA) properties. Experiments on three real-world traffic datasets demonstrate the superiority of our model over several state-of-the-art methods. Our code and data are available at ( http://github.com/lonicera-yx/MGT). |
关键词 | Traffic prediction Spatial-temporal modeling Meta-learning Attention mechanism Deep learning |
DOI | 10.1016/j.neucom.2021.12.033 |
关键词[WOS] | NEURAL-NETWORKS ; FLOW PREDICTION ; MODEL |
收录类别 | SCIE |
语种 | 英语 |
资助项目 | National Key Research and Development Program of China[2020AAA0104903] ; National Natural Science Foundation of China[91646207] ; National Natural Science Foundation of China[62072039] ; National Natural Science Foundation of China[62076242] |
项目资助者 | National Key Research and Development Program of China ; National Natural Science Foundation of China |
WOS研究方向 | Computer Science |
WOS类目 | Computer Science, Artificial Intelligence |
WOS记录号 | WOS:000830181200012 |
出版者 | ELSEVIER |
七大方向——子方向分类 | 人工智能基础理论 |
国重实验室规划方向分类 | 人工智能基础前沿理论 |
是否有论文关联数据集需要存交 | 否 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/49798 |
专题 | 多模态人工智能系统全国重点实验室_先进时空数据分析与学习 |
通讯作者 | Ye, Xue |
作者单位 | 1.National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China 2.School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 101408, China 3.School of Mathematical Sciences, Capital Normal University, Beijing 100048, China 4.School of Computer Science and Technology, Beijing Institute of Technology, Beijing 100081, China |
第一作者单位 | 模式识别国家重点实验室 |
通讯作者单位 | 模式识别国家重点实验室 |
推荐引用方式 GB/T 7714 | Ye, Xue,Fang, Shen,Sun, Fang,et al. Meta Graph Transformer: A Novel Framework for Spatial-Temporal Traffic Prediction[J]. NEUROCOMPUTING,2022,491:544-563. |
APA | Ye, Xue,Fang, Shen,Sun, Fang,Zhang, Chunxia,&Xiang, Shiming.(2022).Meta Graph Transformer: A Novel Framework for Spatial-Temporal Traffic Prediction.NEUROCOMPUTING,491,544-563. |
MLA | Ye, Xue,et al."Meta Graph Transformer: A Novel Framework for Spatial-Temporal Traffic Prediction".NEUROCOMPUTING 491(2022):544-563. |
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