The invention relates to a traffic flow prediction method and device based on time-space sequence deep learning. The method comprises the following steps: obtaining a road shape and traffic flow historical data of a road; the method comprises the following steps: preprocessing traffic flow historical data according to a related time correlation sequence, further arranging the traffic flow historical data into a tensor form according to a batch form, and constructing a multivariable time-space sequence data set of the traffic flow historical data; dividing the multivariable space-time sequence data set into a training data set, a verification data set and a test data set; training the traffic flow prediction model by using the training data set; and collecting traffic flow data at the current moment, inputting the collected traffic flow data at the current moment into the trained traffic flow prediction model, and predicting a time sequence value at a future moment. According to the method, the effective change condition of the traffic flow can be predicted in the actual traffic flow prediction application, and a traffic flow manager can be assisted to provide reference and safety risk assessment for the current traffic flow condition.
本发明涉及一种基于时空序列深度学习的交通流量预测方法及装置,包括:获取道路形状以及道路的交通流量历史数据;对交通流量历史数据按照相关的时间关联顺序进行预处理,并按照批的形式进一步整理为张量的形式,构建交通流量历史数据的多变量时空序列数据集;将多变量时空序列数据集划分为训练数据集、验证数据集和测试数据集;利用训练数据集对交通流量预测模型进行训练;采集当前时刻的交通流量数据,将采集的当前时刻的交通流量数据输入训练好的交通流量预测模型,预测未来时刻的时序值。本发明能够在实际的交通流量预测应用中,预测出交通流量的有效变化情况,可以辅助交通流量的管理人员对当前交通流量情况提供参考和安全风险评估。
Traffic flow prediction method and device based on space-time sequence deep learning
一种基于时空序列深度学习的交通流量预测方法及装置
24.11.2023
Patent
Elektronische Ressource
Chinesisch
IPC: | G08G Anlagen zur Steuerung, Regelung oder Überwachung des Verkehrs , TRAFFIC CONTROL SYSTEMS / G06F ELECTRIC DIGITAL DATA PROCESSING , Elektrische digitale Datenverarbeitung / G06N COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS , Rechnersysteme, basierend auf spezifischen Rechenmodellen |
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