基于XGBoost算法的短期交通流预测

Short-Term Traffic Flow Prediction Based on XGBoost

摘要: 针对短期交通流预测问题，为完成实时精准预测，建立了一种基于Huber损失的极端梯度上升(Extreme Gradient Boosting, XGBoost)短时交通流预测模型。通过对交通流数据周期性、关联性的分析，提取时间特征，并进行时间特征重要性分析。利用该模型以及提取的特征进行交通流预测，实验结果表明：该模型优于基于均方误差损失的极端梯度上升模型以及基于平均绝对误差损失的极端梯度上升模型。同时，该模型较梯度提升回归模型、支持向量机回归模型具有更高的预测精度，各误差指标小，且模型训练时间短，符合短时交通流预测所要求的时效性。

Abstract: For short-term traffic flow prediction, in order to complete real-time accurate prediction, an extreme gradient boosting (XGBoost) short-term traffic flow prediction model based on Huber loss is established. By analyzing the periodicity and relevance of traffic flow data, time features are extracted and feature importance analysis is performed. Using this model and the extracted features for traffic flow prediction, the experimental results show that the model is superior to the extreme gradient boosting model based on mean square error loss and the extreme gradient boosting model based on average absolute error loss. At the same time, the model has higher prediction accuracy than gradient boosting regression model and support vector machine regression model, each error index is small, and the model training time is short, which meets the timeliness required by short-term traffic flow prediction.