Using support vector regression and K-nearest neighbors for short-term traffic flow prediction based on maximal information coefficient

The prediction of short-term traffic flow is critical for improving service levels for drivers and passengers as well as enhancing the efficiency of traffic management in the urban transportation system. For transportation departments, the issue remains of how to efficiently utilize the spatial and temporal information of traffic data for better prediction performance. As a means of improving traffic prediction accuracy, this paper proposes a method for screening spatial time-delayed traffic series based on the maximal information coefficient. The selected time-delayed traffic series are transformed into traffic state vectors, from which traffic flow is predicted by adopting the combination of support vector regression method and k-nearest neighbors method. We employ the proposed framework for real-world traffic flow prediction. Root Mean Squared Error (RMSE) and Mean Absolute Percent Error (MAPE) validate the superior performance of the proposed model compared to traditional methods. This new approach reduces the RMSE by 23.448% and the MAPE by 14.726% of the predicted results.