The invention discloses a traffic flow prediction method based on a PSO (Particle Swarm Optimization)-Attention-LSTM (Long Short Term Memory) model. The method comprises the following steps: improving a traffic flow data prediction effect by adopting a long short-term memory (LSTM) network; an attention mechanism is introduced to learn the space relation between different traffic nodes, and the space dependency relation is captured; and optimizing parameters of the network model structure by using a particle swarm optimization (PSO) algorithm. Compared with the prior art, the method can maintain the accuracy of traffic flow prediction, and improves the prediction speed at the same time.
本发明公开了一种基于PSO‑Attention‑LSTM模型的交通流预测方法;该方法包括:采用长短期记忆网络LSTM提高交通流数据预测效果;引入注意力机制学习不同交通节点之间的空间联系,捕捉空间依赖关系;利用粒子群优化算法PSO优化网络模型结构的参数。与现有技术相比,本发明可以保持交通流预测的准确性,同时提高预测速度。
Traffic flow prediction method based on PSO-Attention-LSTM model
一种基于PSO-Attention-LSTM模型的交通流预测方法
2023-09-15
Patent
Electronic Resource
Chinese
IPC: | G06F ELECTRIC DIGITAL DATA PROCESSING , Elektrische digitale Datenverarbeitung / G06N COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS , Rechnersysteme, basierend auf spezifischen Rechenmodellen / G06Q Datenverarbeitungssysteme oder -verfahren, besonders angepasst an verwaltungstechnische, geschäftliche, finanzielle oder betriebswirtschaftliche Zwecke, sowie an geschäftsbezogene Überwachungs- oder Voraussagezwecke , DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES / G08G Anlagen zur Steuerung, Regelung oder Überwachung des Verkehrs , TRAFFIC CONTROL SYSTEMS |
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