The invention relates to a traffic flow stationary sequence data fusion prediction method based on a cloud model, and belongs to the technical field of traffic data processing. The method comprises the following steps: firstly, selecting a training set based on original traffic flow sequence variables, constructing a cloud model for the training set, carrying out prediction solution by selecting two related prediction models, carrying out cloud droplet formation by using a forepart cloud generator, and respectively generating a cloud droplet 1 and a cloud droplet 2; cloud droplet certainty degree fusion is carried out on the cloud droplet 1 and the cloud droplet 2, and cloud model reasoning is carried out to generate fusion prediction, so that early warning is carried out on conditions such as traffic jam in the future. The method does not limit the selection of a prediction model before the cloud droplet determinacy fusion, and can use a common model which can predict and analyze a stationary sequence in the traffic field. The method has the characteristics of high universality, high comprehensive prediction level and the like.
本发明涉及一种基于云模型的交通流平稳序列数据融合预测方法,属于交通数据处理的技术领域。本发明首先基于原始的交通流序列变量进行训练集的选取后,对训练集进行云模型的构建,在选用相关的两种预测模型进行预测求解后,使用前件云发生器进行云滴化,分别产生云滴1和云滴2,然后,对云滴1和云滴2进行云滴的确定度融合并进行云模型的推理即产生融合预测的目的,进而对未来的交通拥堵等状况进行预警。该方法不限定云滴确定度融合之前的预测模型的选用,可以使用交通领域常见的可以对平稳序列进行预测分析的模型即可。该方法具有普适性强、综合预测水平高等特点。
Traffic flow stationary sequence data fusion prediction method based on cloud model
一种基于云模型的交通流平稳序列数据融合预测方法
2024-07-16
Patent
Electronic Resource
Chinese
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 / G08B SIGNALLING OR CALLING SYSTEMS , Signalisier- oder Rufsysteme |
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