Short-term traffic flow prediction based on adaptive rank dynamic tensor analysis - Lingchao HE; Dong LIN; Xinxin FENG (物联网学报)
Short-term traffic flow prediction in intelligent transportation system can provide data support in areas such as route planning,traffic management,public safety and so on.In order to improve the prediction accuracy with missing and abnormal data,a short-term traffic flow prediction method based on the adaptive rank dynamic tensor analysis was proposed.Firstly,a four dimensional tensor consisted of week,day,time and space was constructed,which could excavate the multimodal correlation of traffic flow data.Secondly,tensor flow data with dynamic structure was formed by using sliding window model.The principal component analysis (PCA) algorithm was extended to an offline tensor analysis algorithm that could accept tensor input.Then the adaptive rank and the forgetting factor were introduced to generate an adaptive rank dynamic tensor analysis algorithm.Finally,the tensor stream data was inputted into the adaptive rank dynamic tensor analysis algorithm to realize the short-term traffic flow prediction.The experimental results show that a good prediction can be achieved even with data missing.