Short-term Traffic Flow Forecasting Model Based on SVM and Adaptive Spatio-Temporal Data Fusion

Centering around the periodicity and randomness properties of short-term traffic flow,a better model was derived by correcting the results predicted by time series with the prediction data of space series based on SVM in this paper. In additions,the future projections were dynamically adjusted through the analysis of spatial and temporal historical predictions. The proposed model is compared with three well-known prediction models including SVR,Holt exponential smoothing,and Multiple regression. The resultant performance comparisons suggest that the adaptive spatio-temporal data fusion model performs better than other models,and the average relative error is less than 4%.