The invention discloses an RSCN traffic flow prediction method based on a time sequence convolutional network. A time sequence feature data preprocessing and normalization module, a feature data parallel processing module and an error judgment and correction module based on a residual network are included. The time sequence characteristic data preprocessing and normalization module is used for sorting the traffic flow data of the time sequence recorded by a plurality of transmitters on one road section into sequence data, and preprocessing and normalizing the traffic flow data; the characteristic data parallel processing module is used for distributing the preprocessed and normalized traffic flow data in an expansion convolution and radial basis network for parallel processing; and carrying out self-correction error analysis training on the residual network by taking the traffic flow data after parallel processing as the input of residual processing and taking the residual neuron as the output of residual processing. According to the invention, an RSCN traffic flow prediction algorithm based on a time sequence convolutional network is adopted, and based on a convolutional network layer, traffic flow data of a time sequence recorded by a plurality of transmitters on a road section are predicted and calculated by using parallel calculation and residual checking methods. A short-time traffic flow prediction model which is higher in prediction precision and capable of changing learning parameters in real time is constructed, and the problems that an original model is not high enough in precision and poor in real-time data processing capacity are solved.
本发明公开了一种基于时序卷积网络的RSCN交通流预测方法,包括时序特征数据的预处理和归一化模块、特征数据的并行处理模块和基于残差网络的误差判断校正模块;时序特征数据的预处理和归一化模块,对一条路段上的多个传输器记录的时间序列的交通流数据整理成序列数据,对交通流数据进行预处理和归一化;特征数据的并行处理模块,将预处理和归一化后的交通流数据分置在膨胀卷积和径向基网络中并行处理;将并行处理后的交通流数据作为残差处理的输入,残差神经元作为残差处理的输出,进行残差网络的自我校正误差分析训练。本发明采用一种基于时序卷积网络的RSCN交通流预测算法,基于卷积网络层,运用并行计算和残差校核的方法对一条路段上的多个传输器记录的时间序列的交通流数据进行预测计算,构造一种预测精度更高和能够实时改变学习参数的短时交通流预测模型,解决了原有的模型精度不够高,处理实时数据能力较差的问题。
RSCN traffic flow prediction method based on time sequence convolutional network
基于时序卷积网络的RSCN交通流预测方法
2025-04-11
Patent
Electronic Resource
Chinese
IPC: | G08G Anlagen zur Steuerung, Regelung oder Überwachung des Verkehrs , TRAFFIC CONTROL SYSTEMS |
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