The invention relates to an intelligent service area traffic flow prediction model and an application method, which relate to the technical field of big data, collect various data of a service area, including historical traffic flow data, weather data and mainline flow data, pre-process the collected multi-dimensional data, store the pre-processed data in a database, read the database, and store the pre-processed data in the database. The method comprises the steps of obtaining historical flow data, inputting multi-dimensional data into a CNN model, performing feature extraction on the data through a convolutional layer and a pooling layer, inputting the extracted features into a BiLSTM model, performing data prediction, after prediction is completed, introducing an Attention mechanism, calculating a weight for each feature and a result, and reversely influencing the BiLSTM model through the weight so as to optimize the model. And training the sorted data by using a CNN-BiLSTM-Attention model to obtain a prediction result of the model on the traffic flow, and transmitting the prediction result to a service area management and control platform to manage and control the service area.
本发明涉及一种智慧服务区车流量预测模型及应用方法,涉及大数据技术领域,收集服务区各项数据,包括历史车流量数据、天气数据、主线流量数据,并对收集到的多维度数据进行预处理,将其存储在数据库中,读取数据库,得到历史的流量数据,将多维度数据输入CNN模型中,通过卷积层和池化层对数据进行特征提取,并将提取的特征输入BiLSTM模型中,进行数据预测,在完成预测后,引入Attention机制,针对每个特征与结果计算权重,通过权重反向影响BiLSTM模型,以优化模型,使用CNN‑BiLSTM‑Attention模型对整理后的数据进行训练,获取模型对车流量的预测结果,并将其传入服务区管控平台,对服务区进行管控。
Intelligent service area traffic flow prediction model and application method
一种智慧服务区车流量预测模型及应用方法
2024-10-01
Patent
Electronic Resource
Chinese
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