The invention discloses an urban traffic flow prediction method based on multi-source data fusion. The urban traffic flow prediction method comprises the following steps: performing a fusion algorithmoriented to structured and unstructured traffic data; to be specific, designing a wavelet neural network (WNN) containing a three-layer network topology structure to process traffic volume parametersof a multi-source detector; supposing that the traffic detector performs statistics by taking 5min as a time interval, setting an acquired traffic flow sequence to be xi (i = 1, 2, 3,..., k), and using the xi as an input traffic parameter of the wavelet neural network. According to the invention, multi-azimuth detection is carried out in combination with various sensors, comprehensive and complete traffic information is obtained, structured and unstructured data are comprehensively analyzed and fused, and the accuracy and reliability of traffic flow data are improved; and in combination witha traffic flow prediction algorithm based on a deep belief network, prediction of the traffic flow is realized, so that the traffic flow prediction precision is improved and the traffic jam is relieved.
本发明公开了一种基于多源数据融合的城市交通流预测方法,包括下述步骤:(1)面向结构化与非结构化交通数据的融合算法;首先,设计含有三层网络拓扑结构的小波神经网络WNN对多源检测器的交通量参数进行处理;假定交通检测器以5min为一个周期进行统计,所采集交通流量序列为x(i=1,2,3,…,k),并作为小波神经网络的输入交通参数;本发明结合多种传感器进行多方位检测,获取全面和完整的交通信息,将结构化与非结构化数据进行综合分析并进行数据融合,提高交通流数据的准确性和可靠性;再结合基于深度信念网络的交通流预测算法,实现对交通流量的预测,从而提高交通流预测精度,缓解交通拥堵。
Urban traffic flow prediction method based on multi-source data fusion
一种基于多源数据融合的城市交通流预测方法
2020-05-08
Patent
Elektronische Ressource
Chinesisch
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