The invention discloses a traffic flow prediction method based on clustering and evolutionary deep learning, and the method comprises the steps: carrying out the preprocessing of traffic flow data collected in advance, and carrying out the clustering of the traffic flow data through an improved clustering algorithm; extracting amplitude, phase and frequency feature information related to a time sequence from the traffic flow data by adopting an Euler feature extraction method; secondly, improving an EDO algorithm by adopting Tent chaotic mapping, a nonlinear inertia weight factor and a leader strategy to obtain an IEDO algorithm; and sending the extracted effective traffic flow data into a CLform model for training, optimizing the parameters of the CLform model by using an IEDO algorithm, and predicting the traffic flow by using the trained and optimized CLform model. According to the method, the future traffic flow condition is accurately pre-judged through the historical traffic flow time sequence data, and the method is of great significance to the development of the traffic industry.
本发明公开了一种基于聚类与进化深度学习的交通流预测方法,对预先采集的交通流数据进行预处理,用改进的聚类算法对交通流数据进行聚类;采用欧拉特征提取方法从交通流数据中提取出有关时间序列的振幅、相位和频率特征信息;之后采用Tent混沌映射、非线性惯性权重因子和领导者策略对EDO算法进行改进,得到IEDO算法;把提取有效的交通流数据送入CLformer模型中进行训练,同时利用IEDO算法优化CLformer模型参数,利用训练优化后的CLformer模型对交通流进行预测。本发明通过历史交通流时间序列数据准确预判未来的交通流量情况,对交通行业的发展有着重要意义。
Traffic flow prediction method based on clustering and evolution deep learning
一种基于聚类与进化深度学习的交通流预测方法
2024-04-12
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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