The invention relates to a transfer learning model-based urban road network unknown flow estimation method and system. The method comprises the following steps of: searching similar road sections and nearly similar road sections in road sections which are not identified by license plate photos for each road section which is not identified by the license plate photos; carrying out feature extraction on the taxi GPS data of the road sections without license plate photo recognition, the similar road sections and the nearly similar road sections, wherein the traffic flow of license plate photo recognition serves as feature labels, and a similar road section sample set and a nearly similar road section sample set are formed; and training a transfer learning-based support vector regression model of each road section without license plate photo recognition; and acquiring taxi GPS data of a road section without license plate photo recognition, performing data preprocessing to obtain the average speed of each taxi, extracting features, inputting the features into the support vector regression model corresponding to the road section, and outputting the estimated traffic flow. By means of the method, accurate estimation of the traffic flow of a road section without license plate photo recognition is realized.
本发明涉及一种基于迁移学习模型的城市路网未知流量估计方法及系统,为每个未安装车牌照片识别的路段查找安装车牌照片识别的路段中的相似路段及近乎相似路段;对于每个未安装车牌照片识别的路段,相似路段和近乎相似路段的出租车GPS数据,分别进行特征提取,车牌照片识别的车流量作为特征的标签,形成相似路段和近乎相似路段样本集;训练每个未安装车牌照片识别的路段的基于迁移学习的支持向量回归模型。采集未安装车牌照片识别的路段的出租车GPS数据,进行数据预处理获取每辆出租车的平均速度,提取特征后,输入该路段对应的支持向量回归模型,输出估计的车流量。实现了未安装车牌照片识别的路段车流量的准确估计。
Urban road network unknown flow estimation method and system based on transfer learning model
基于迁移学习模型的城市路网未知流量估计方法及系统
2021-12-31
Patent
Elektronische Ressource
Chinesisch
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