| Title | Artificial intelligence-enabled smart city construction |
|---|---|
| ID_Doc | 45054 |
| Authors | Jiang, YX; Han, LF; Gao, YF |
| Title | Artificial intelligence-enabled smart city construction |
| Year | 2022 |
| Published | Journal Of Supercomputing, 78, 18 |
| Abstract | This work aims to promote smart city construction and smart city management. Firstly, this work analyzes the relevant theories and processing methods of short-term traffic flow prediction. Secondly, the random forest regression (RFR) theory in machine learning is discussed to realize the short-term traffic flow prediction model (STTPM). Meanwhile, STTPM data are processed by k-nearest neighbors (KNN) and optimized by Complete Ensemble Empirical Mode Decomposition (CEEMD) and RFR method. Finally, the KNN-CEEMD-RFR model is proposed, and the performance of the model is evaluated. The results show that the proposed KNN-CEEMD-RFR model has better traffic prediction effect than support vector regression, RFR model, and CEEMD-RFR model. The prediction of support vector regression model is the worst, followed by RFR model. The mean square error of CEEMD-RFR is about 2% lower than that of RFR without data preprocessing. The mean square error of KNN-CEEMD-RFR model is 4% smaller than that of CEEMD-RFR model. Finally, the prediction accuracy of the proposed KNN-CEEMD-RFR model is more than 92%, which has a very ideal prediction effect. This work provides specific ideas for the application of artificial intelligence in smart city construction and smart city management. The proposed KNN-CEEMD-RFR model for smart city has made an important contribution to the development of traffic management in smart city management. |
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