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Crossroads LOS Prediction Method Based on Big Data and AI, and Storage Medium Having the Same

机译:基于大数据和AI的交叉路LOS预测方法,以及具有相同的存储介质

摘要

Big data and artificial intelligence-based intersection service level prediction method according to the present invention is big data and artificial intelligence-based intersection service level prediction performed through a management server installed with big data and artificial intelligence-based intersection service level prediction program stored in a storage medium In the method, (a) collecting a plurality of traffic relation data having a correlation with the intersection service level, (b) performing pre-processing on the traffic relation data collected by the step (a), the pre-processing (c) calculating the control delay value per vehicle for each inlet of the intersection from the traffic relation data, based on a preset time unit standard, and using the artificial neural network (d) of inputting the input data into the , learning and evaluating in a preset time unit, and (e) of predicting the intersection service level through the model learned by the artificial neural network by the step (d). .
机译:根据本发明的大数据和基于人工智能的交叉服务级预测方法是通过安装在存储的大数据和基于人工智能的交叉服务级预测程序的管理服务器进行的大数据和基于人工智能的交叉路口服务级预测在方法中的存储介质,(a)收集与交叉服务级别相关联的多个流量关系数据,(b)对由步骤(a)收集的流量关系数据执行预处理,该流量关系数据(b)执行预处理(c)基于预设时间单位标准,使用将输入数据输入,学习和评估输入输入数据的人工神经网络(D)计算每个车辆的每个入口计算每个车辆的控制延迟值。在预设时间单元中,通过人工神经网络学习的模型预测交叉服务水平的(e)通过步骤(d)工作。 。

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