The invention discloses an attention-based dynamic space-time diagram convolution traffic flow prediction system and method, and relates to the field of intelligent traffic, and the system comprises an input layer, n space-time layers which are connected in sequence, and an output layer. Wherein the input layer receives input data and is connected with the first space-time layer, and the input layer performs data preprocessing and inputs the data into the first space-time layer for training; the output layer is connected with the nth space-time layer and outputs prediction data; each time-space layer comprises a multi-granularity trend self-attention module, a semantic adaptive graph convolution module and a gating time convolution module. The method comprises the following steps of: 1, preprocessing input data by an input layer; 2, the space-time layers receive input data, and a training result is formed after the input data are processed by a plurality of space-time layers; each time-space layer comprises a multi-granularity trend self-attention module, a semantic adaptive graph convolution module and a gating time convolution module; and step 3, the output layer converts the training result into prediction data and outputs the prediction data.
本发明公开了一种基于注意力的动态时空图卷积交通流预测系统与方法,涉及智能交通领域,所述系统包括:1个输入层、依次连接的n个时空层和1个输出层;其中,输入层接收输入数据并与第1个时空层连接,输入层进行数据预处理,并输入到第1个时空层中进行训练;输出层与第n个时空层连接并输出预测数据;每个时空层包括:多粒度趋势自注意力模块、语义自适应图卷积模块和门控时间卷积模块。所述方法包括:步骤1、输入层预处理输入数据;步骤2、时空层接收输入数据,经过若干个时空层处理后形成训练结果;每个时空层包括:多粒度趋势自注意力模块、语义自适应图卷积模块和门控时间卷积模块;步骤3、输出层将训练结果转换为预测数据并输出。
Attention-based dynamic space-time diagram convolution traffic flow prediction system and method
一种基于注意力的动态时空图卷积交通流预测系统与方法
2023-11-14
Patent
Elektronische Ressource
Chinesisch
IPC: | G06F ELECTRIC DIGITAL DATA PROCESSING , Elektrische digitale Datenverarbeitung / G06N COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS , Rechnersysteme, basierend auf spezifischen Rechenmodellen / G08G Anlagen zur Steuerung, Regelung oder Überwachung des Verkehrs , TRAFFIC CONTROL SYSTEMS |
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