The invention provides a road tunnel traffic incident real-time prediction method based on multi-model fusion, and the method comprises the steps: S1, collecting traffic data from a plurality of data sources, carrying out the data preprocessing of the traffic data, dividing the processed data into a training set, a verification set and a test set according to a specific proportion, carrying out normalization processing on data of the training set and the test set; s2, designing and training the processed data to obtain M models; and S3, constructing a stack model from the M models obtained in the S2 by adopting a stack ensemble learning strategy, and inputting the prediction probability of each model as feature input into the stack model for prediction to obtain a final prediction result. The problems that the prediction accuracy and reliability are insufficient due to the fact that complex traffic data features are difficult to fully capture in the prior art, the characteristics of various models cannot be effectively fused, the advantages of an integrated model cannot be fully played, and the prediction effect is uneven in hierarchy are solved.
本发明提供一种基于多模型融合的公路隧道交通事件实时预测方法,所述方法包括:S1:从多种数据源中收集交通数据并对其进行数据预处理,然后将处理后的数据按照特定的比例划分为训练集、验证集和测试集,并对训练集和测试集数据进行归一化处理;S2:利用处理好后的数据设计并训练得到M个模型;S3:将S2中得到的M个模型采用堆叠集成学习策略构建出堆叠模型,将各个模型的预测概率作为特征输入,输入到堆叠模型中进行预测得到最终的预测结果。解决了现有技术难以充分捕捉复杂的交通数据特征导致预测准确性和可靠性不足,以及未能有效融合各类模型的特长,无法充分发挥集成模型的优势、预测效果层次不齐等问题。
Highway tunnel traffic incident real-time prediction method based on multi-model fusion
一种基于多模型融合的公路隧道交通事件实时预测方法
2025-03-07
Patent
Electronic Resource
Chinese
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