The invention relates to the technical field of intelligent traffic control, in particular to an unobservable road section-oriented network-level traffic operation state inference method, which comprises the following steps of: by taking checkpoint detection data as a data basis, completing data preprocessing based on checkpoint data characteristics, and extracting traffic flow characteristic parameters; constructing a model algorithm based on a Bayesian network and a traffic flow fundamental diagram, and realizing traffic flow characteristic parameter completion for unobservable road sections; spatial characteristics of a road network are introduced on the basis of constructing a double attention mechanism-directed graph long short-term memory network model. Therefore, the traffic management department can extract the traffic flow characteristic parameters by means of the checkpoint and complement the traffic flow characteristic parameters of the unobservable road section by means of the model algorithm so as to construct a complete road network traffic state inference model. Therefore, the problem that the real-time traffic state cannot be mastered due to the lack of corresponding detection equipment in part of road sections in a real urban road network is solved, and the traffic operation state of the whole road network can be deduced and mastered.
本发明涉及智能交通管控技术领域,具体为一种面向不可观测路段的网络级交通运行状态推断方法,包括以下步骤:以卡口检测数据为数据基础,基于卡口数据特性完成数据预处理,提取交通流特征参数;构建基于贝叶斯网络和交通流基本图的模型算法,实现面向不可观测路段的交通流特征参数补全;在构造双注意力机制‑有向图长短期记忆网络模型的基础上引入路网的空间特性。如此,交通管理部门可借助于卡口以提取交通流特征参数,且借助于模型算法对不可观测路段交通流特征参数进行补全,以构造出完整路网交通状态推断模型,从而解决了现实城市路网中部分路段因缺少相应的检测设备而无法掌握实时交通状态的问题,进而利于推断、掌握整个路网交通运行状态。
Network-level traffic operation state inference method for unobservable road section
一种面向不可观测路段的网络级交通运行状态推断方法
2025-02-25
Patent
Electronic Resource
Chinese
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