| Title |
Short-term Prediction of Travel Speed in Urban Areas Using an Ensemble Empirical Mode Decomposition |
| Authors |
김의진(Kim, Eui-Jin) ; 김동규(Kim, Dong-Kyu) |
| DOI |
https://doi.org/10.12652/Ksce.2018.38.4.0579 |
| Keywords |
단기 통행속도 예측;앙상블 경험적 모드 분해;인공신경망;고유모드함수 Short-term prediction of travel speed;Ensemble empirical mode decomposition (EEMD);Artificial neural network (ANN);Intrinsic mode function (IMF) |
| Abstract |
Short-term prediction of travel speed has been widely studied using data-driven non-parametric techniques. There is, however, a lack of research on the prediction aimed at urban areas due to their complex dynamics stemming from traffic signals and intersections. The purpose of this study is to develop a hybrid approach combining ensemble empirical mode decomposition (EEMD) and artificial neural network (ANN) for predicting urban travel speed. The EEMD decomposes the time-series data of travel speed into intrinsic mode functions (IMFs) and residue. The decomposed IMFs represent local characteristics of time-scale components and they are predicted using an ANN, respectively. The IMFs can be predicted more accurately than their original travel speed since they mitigate the complexity of the original data such as non-linearity, non-stationarity, and oscillation. The predicted IMFs are summed up to represent the predicted travel speed. To evaluate the proposed method, the travel speed data from the dedicated short range communication (DSRC) in Daegu City are used. Performance evaluations are conducted targeting on the links that are particularly hard to predict. The results show the developed model has the mean absolute error rate of 10.41% in the normal condition and 25.35% in the break down for the 15-min-ahead prediction, respectively, and it outperforms the simple ANN model. The developed model contributes to the provision of the reliable traffic information in urban transportation management systems. |