Traffic flow prediction based on improved deep extreme learning machine

A new hybrid prediction model is proposed for short-term traffic flow, which is based on Deep Extreme Learning Machine improved by Sparrow Search Algorithm (SSA-DELM). Firstly, Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise algorithm (ICEEMDAN) is employed to improve prediction accuracy. Then multiple Intrinsic Mode Function components (IMF) can be obtained. Secondly, Permutation Entropy algorithm (PE) is used to analyze the randomness of IMFs. Finally, different prediction models can be built according to the randomness characteristics. SSA-DELM prediction models are established for IMFs with large permutation entropy values. The IMFs with small permutation entropy values are put into ARIMA prediction models. To obtain the predicted traffic flow, different IMFs predicted values are added together. Two actual signalized intersections are selected to verify the performance of the new proposed model in this paper. Several prediction models based on different algorithms are built. The results obtained by MATLAB software show that the prediction errors of the new proposed model are the smallest and the fitting effect with the measured data is the best, which can effectively improve prediction accuracy.