The invention relates to the technical field of intelligent traffic engineering, in particular to a traffic situation prediction system which comprises a data acquisition unit and a central processing unit, the output end of the data acquisition unit is connected with the input end of a wireless communication module, and the output end of the wireless communication module is connected with the input end of the central processing unit. The central processor is connected with the input end of the data information scheduling unit. According to the traffic situation prediction system, through cooperative use of the linear learning unit and the traffic situation prediction unit, checking calculation training of a prediction model on a large amount of data and a guide avoidance module of a situation prediction checking calculation center are learned, the labor amount of part of manual operation is saved, and the defects that a traditional traffic situation prediction model is insufficient in precision and high in accuracy are overcome. According to the method, the problem that multi-step prediction is difficult in the prior art is solved, a deep learning prediction model with a double-layer attention mechanism is constructed, the time-space relevance of road network traffic objects is learned, the problem of repeated calculation of unnecessary data information is reduced, and the multi-step prediction result of the traffic situation is more accurate.
本发明涉及智能交通工程技术领域,具体为一种交通态势预测系统,包括数据采集单元和中央处理器,数据采集单元的输出端和无线通信模块的输入端连接,无线通信模块的输出端和中央处理器的输入端连接,中央处理器与数据信息调度单元的输入端连接。该交通态势预测系统,通过线性学习单元和交通态势预测单元的配合使用,学习预测模型对大量数据的验算训练及态势预测验算中心的向导规避模块,节省了部分人力操作的劳动量,克服了传统交通态势预测模型精度不足,难以多步预测的问题,构建具有双层注意力机制的深度学习预测模型,对路网交通对象的时空关联性进行学习,降低不必要数据信息重复计算的问题,使得交通态势多步预测结果更加准确。
Traffic situation prediction system
一种交通态势预测系统
2023-03-07
Patent
Electronic Resource
Chinese
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