基于小波分析和集成学习的短时交通流预测

针对短时交通流具有随机性和不确定性等特征,提出一种基于小波分析和集成学习的组合预测模型.首先,对原始交通流数据的平均行程时间序列应用Mallat算法进行多尺度小波分解,且对各尺度上分量进行单支重构;其次,对于各重构的单支序列分别使用极端梯度提升模型(extreme gradient boosting,XGBoost)进...

Short-term Traffic Flow Forecasting Based on Wavelet Analysis and Ensemble Learning

In view of the randomness and uncertainty of short-term traffic flow, a combined forecasting model based on wavelet analysis and ensemble learning was proposed. Firstly, the average travel time series of the original traffic flow data were decomposed by Mallat algorithm, and the components on each scale were reconstructed by a single branch. Then, for each reconstructed single branch series, the extreme gradient boosting(XGBoost) model was used to predict and obtain multiple sub models, and the Bayesian optimization algorithm was used to select the best parameters of the sub models. Finally, the predicted values of all the sub models were algebraically summed to obtain the predicted results of the overall traffic flow. The actual traffic flow data of a road section in Brooklyn, New York, USA was used to predict, and the prediction results were compared with other models. The results show that the prediction effect of the combined model of wavelet analysis and XGBoost is better than that of the traditional linear model and single XGBoost model, so as to provide better guidance for traffic management.