The invention discloses a prediction quality-oriented traffic flow interval quantification method, and relates to the field of intelligent traffic system prediction, and the method comprises the following steps: S1, building an interval loss function method for quantifying a traffic flow by taking the interval prediction quality as a target; s2, constructing a traffic flow model through a deep learning method; s3, feature selection is carried out on the information features of the traffic flow through an extreme gradient increase model; and S4, determining a final traffic flow model by combining the interval fractional function obtained in the S1, the deep learning model obtained in the S2 and the important features obtained in the S3. According to the prediction quality-oriented traffic flow interval quantization method, the deep learning theory is introduced into traffic flow prediction deviation quantization, the application of the deep learning method and the interval prediction function theory in the long-term multi-step traffic flow prediction problem is researched, the traffic flow performance of an intelligent traffic system is enhanced, and the traffic flow prediction accuracy is improved. The method is of great significance to the development of an intelligent traffic system.
本发明公开了一种预测质量为导向的交通流区间量化方法,本发明涉及智能交通系统预测领域,包括以下步骤:S1:以区间预测的质量为目标建立量化交通流的区间损失函数方法;S2:通过深度学习方法构建交通流量化模型;S3:通过极端梯度增加模型对交通流的信息特征做特征选择;S4:结合S1所得的区间分数函数、S2所得的深度学习模型和S3所得的重要特征确定最终的交通流量化模型。本发明采用上述的一种预测质量为导向的交通流区间量化方法,通过将深度学习理论引进交通流预测偏差量化中,研究深度学习方法以及区间预测函数理论在长期多步交通流预测问题中的应用,增强智能交通系统对交通流量化的性能,对智能交通系统的发展具有重要意义。
Prediction quality-oriented traffic flow interval quantification method
一种预测质量为导向的交通流区间量化方法
2023-12-08
Patent
Electronic Resource
Chinese
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