Highway accident prediction method and system based on gradient boosting tree model
A gradient boosting tree and accident prediction technology, applied in the field of traffic safety, can solve the problem of inability to quantify the risk state, and achieve the effect of improving the detection performance, reducing the false alarm rate, and overcoming the imbalance.
Pending Publication Date: 2021-08-20
招商新智科技有限公司
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One or more embodiments of the invention provide a highway accident prediction method and system based on a gradient boosting tree model, and the method comprises the steps of carrying out the data cleaning of a collected traffic accident data set, carrying out the data preprocessing, carrying out the feature extraction of the processed data, obtaining a key variable for predicting the occurrence of an accident, constructing and training a dynamic accident prediction model in combination with a gradient boosting tree model, and inputting the online data into the trained dynamic accident prediction model to obtain an output predicted accident value. According to the embodiment, the accident number of the road section can be predicted through the real accident data details, the problem that an existing model cannot carry out refined prediction on traffic accidents is solved, imbalance of training data can be overcome, and the effects of improving the detection performance and particularly reducing the false alarm rate are achieved; and compared with a traditional method, the model can carry out more accurate prediction on the traffic accidents.
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