Paratactic Spatial-Temporal Tow Dimension Data Fusion for Traffic Volume Prediction

It is significant that to get accurate prediction of dynamic traffic flow for intelligent traffic system management and control. A traffic flow prediction model of spatial-temporal 2D (2-dimension) data fusing based on SVM (support vector machines) is put forward in this paper. The section flow results predicted by temporal SVM, spatial SVM and spatial-temporal 2D data fusing are all satisfied the precision requirement. However, the prediction precise is significantly improved by spatial-temporal 2Dimension data fusing. Moreover, from the comparison of the results with different samples, it is shown that the more the sample used in the prediction the lower the error will be. Especially, when there are unexpected situations (e.g. traffic jam, traffic accidents), the structural system error of one-dimensional data fusion can be avoided to a large extent with the spatial-temporal 2Dimension data fusing model proposed by this study.