The invention belongs to the technical field of traffic intensity prediction, and relates to a multi-module traffic intensity prediction method based on semantic information. The method comprises the following steps: preprocessing collected traffic intensity data, defining related problems, and constructing input of a multi-module traffic intensity prediction model; constructing a multi-module traffic intensity prediction model based on semantic information, wherein the multi-module traffic intensity prediction model comprises a recent module, a weekly period module and a module fusion component, the recent module and the weekly period module are used for extracting recent and weekly period space-time and semantic characteristics of traffic intensity respectively, and the module fusion assembly is used for fusing the outputs of the recent module and the weekly module to predict the traffic intensity; training and optimizing a multi-module traffic intensity prediction model; and performing traffic intensity prediction based on the multi-module traffic intensity prediction model. The method can effectively capture the dynamic spatial-temporal characteristics and semantic correlation of the traffic intensity, and has the advantages of high prediction speed, high prediction precision and the like.
本发明属于交通密集度预测技术领域,涉及一种基于语义信息的多模块交通密集度预测方法。该方法包括:对采集的交通密集度数据进行预处理,进行相关问题定义,并构建多模块交通密集度预测模型的输入;构建基于语义信息的多模块交通密集度预测模型,包括近期模块、周周期模块和模块融合组件,其中:近期和周周期模块分别用于提取交通密集度的近期和周周期的时空和语义特性;模块融合组件用于将近期和周周期两个模块的输出融合进行交通密集度预测;训练与优化多模块交通密集度预测模型;基于多模块交通密集度预测模型进行交通密集度预测。本发明能有效地捕捉交通密集度动态时空特征和语义相关性,具有预测速度快、预测精度高等优点。
Multi-module traffic intensity prediction method based on semantic information
一种基于语义信息的多模块交通密集度预测方法
2021-07-13
Patent
Electronic Resource
Chinese
Prediction of traffic intensity
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