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| Traffic Congestion Prediction Model Based on Integrated Learning |
| School of Cyber Science and Engineering
School of Geodesy and Geomatics
School of Mathematics and Statistics
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Abstract The current navigation software has obvious inaccurate speed assessment when facing some serious traffic congestion, and
cannot accurately predict the duration of the traffic congestion. Therefore, we propose a traffic congestion prediction model to accu-
rately predict the congestion time in the face of most congestion situations through the prediction of speed. Regarding the speed pre-
diction model, we select high-similarity samples based on the KNN algorithm. The prediction speed model is divided into two main
models, KNN-VA and KNN-RBF, and we use an integrated learning method to fuse these two models to obtain more accurate aver-
age speed prediction. Then, the congestion time can be predicted. In order to determine the congestion time, we use the RBF speed
prediction method and the sampling method in a fixed area to verify. The results show that the model has high reliability for conges-
tion time prediction.
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Published: 10 April 2020
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