The invention discloses an urban road network detector-free road section flow real-time estimation method based on transfer learning, and belongs to the field of intelligent traffic systems. The method comprises an urban local road network similarity evaluation step and a model transfer learning step, in the urban local road network similarity evaluation step, a similarity evaluation system is established by performing quantitative analysis and weight assignment on multi-dimensional road network features influencing road section flow, and the similarity evaluation system is established; in the model transfer learning step, the detector-free road section road network needing to be estimated is used as a transfer learning target field, and the detector road section road network found by the similarity evaluation system is used as a source field; carrying out transfer learning of the traffic estimation model by using a fining-tuning method, thereby completing real-time traffic estimation of a non-detection road section; according to the method, the influence rule of the urban road network on the flow is fully excavated, and the high-precision flow estimation of the data-free road section is realized by a brand new data driving thought.
本发明公开了一种基于迁移学习的城市路网无检测器路段流量实时估计方法,属于智能交通系统领域。本发明的方法包括城市局部路网相似性评价步骤和模型迁移学习步骤,城市局部路网相似性评价步骤通过对影响路段流量的多维度路网特征进行量化分析和权重赋值,建立相似性评价系统,用以寻找与无检测器路段路网流量分布相似的有检测器路段路网,模型迁移学习步骤以需要估计的无检测器路段路网为迁移学习目标领域,以相似性评价系统寻找出的有检测器路段路网为源领域,使用fine‑tuning方法进行流量估计模型的迁移学习,从而完成对无检测路段的流量实时估计;本发明充分挖掘了城市路网对流量的影响规律,以一个全新的数据驱动思路实现了无数据路段高精度流量估计。
Urban road network detector-free road section flow real-time estimation method based on transfer learning
基于迁移学习的城市路网无检测器路段流量实时估计方法
2023-08-15
Patent
Electronic Resource
Chinese
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