The invention discloses a burst congestion discrimination method and system based on multi-source traffic big data fusion. The method comprises the following steps: firstly, obtaining multi-source traffic data comprising traffic parameter type characteristic data, time period type characteristic data and environment type characteristic data; establishing a set of data samples, dividing the set ofdata samples into a training set and a test set; training and optimizing the neural network model by using the training set and the test set; and obtaining a burst congestion discrimination model, inputting the acquired multi-source traffic data of the current time period and the road section into the burst congestion discrimination model for congestion discrimination. According to the invention,data detected by a multi-source traffic detector in an urban road network is used as a basis, and the multi-source traffic data of time period type characteristics and environment type characteristicsrelated to burst congestion and a neural network model with extremely high fault tolerance are considered, so that the road traffic state is accurately sensed by utilizing traffic detectors widely distributed in a road network, and the burst congestion event of the road network is timely and accurately judged.
本发明公开了一种基于多源交通大数据融合的突发拥堵判别方法及系统,所述方法首先获得包括交通参数类特征数据、时段类特征数据和环境类特征数据的多源交通数据,建立数据样本集,并划分为训练集和测试集,利用训练集和测试集对神经网络模型进行训练和优化,得到突发拥堵判别模型,将采集得到的当前时段和路段的多源交通数据输入突发拥堵判别模型,进行拥堵判别,本发明利用以城市路网中的多源交通检测器检测的数据为基础,并考虑了突发拥堵相关的时段类特征和环境类特征的多源交通数据和具有极强容错性的神经网络模型,实现了利用道路网络中广泛布设的交通检测器精准感知道路交通状态,实现路网突发拥堵事件的及时准确判别。
Burst congestion discrimination method and system based on multi-source traffic big data fusion
一种基于多源交通大数据融合的突发拥堵判别方法及系统
18.09.2020
Patent
Elektronische Ressource
Chinesisch
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